Oxford Man Institute Annual Report by Annabel Goss

A n n ua l R ep o rt O x f o rd - M an Institute of Quantitative Finance

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The Oxford-Man Institute would like to acknowledge the extraordinary support of Man Group plc who have generously provided our core funding for the period 2007 - 2015 and more generally for their wider support of the University of Oxford including an endowment for the post of Man Professor of Quantitative Finance.

welcome I am pleased to introduce this, the third Annual Report of the Oxford-Man Institute, in a year that has marked more milestones in our development. We moved into our long-run home, Eagle House, in August 2009 and so have enjoyed our first full year here. During this year our faculty and student body has strengthened. Further, we have welcomed many of the best researchers from around the world to visit us, give seminars and attend our conferences. The core of this year’s report is the articles exploring the work of faculty members Mike Giles, Jan Obłój and Lan Zhang. As well as these in-depth interviews, details of the research interests of all of our members can be found in the subsequent pages. The report gives special mention to one of our founding members, Georg Gottlob, who we are delighted to report has recently been made a Fellow of the Royal Society for his work in computer science. Details of key Institute events can also be found in this report. A highlight of the year being the first of an annual OMI conference series on New Directions and Contemporary Issues in Quantitative Finance organised by Thaleia Zariphopoulou and Xunyu Zhou. The idea of the series is to focus each year on three topics, bringing together the maths and economics perspectives of the research problem. This year’s topics were Information Percolation in Financial Markets, Financial Bubbles, and Principal-Agent Problem and Contract Theory.

An insert is also included providing an insight into life at the Institute from the perspectives of faculty, associate and student members, visitors to the Institute and our colleagues from the Man Research Laboratory. I am also pleased to be able to take this opportunity to report that our main research grant from Man Group, which provides the core funding for the Institute, has been extended so that it now runs until 2015. We are extremely grateful to them for their continued support. Finally I would like to extend my thanks to Thaleia Zariphopoulou who has edited this year’s report.

Professor Neil Shephard, FBA Director of the Oxford-Man Institute August 2010

o pe n i n g On the 24th September 2009, the Oxford-Man Institute celebrated the official opening of its new home, Eagle House, by Lord Patten of Barnes, Chancellor of the University and Peter Clarke, Chief Executive of Man Group plc

ge o rg g o tt l o b Oxford-Man Institute member becomes Fellow of the Royal Society Georg Gottlob, Professor of Computing Science at the University of Oxford and senior member of the Oxford-Man Institute, was earlier this year elected as a Fellow of the Royal Society. The decision recognises his contributions to fields including artificial intelligence, database theory and the logical representation of knowledge.

“It’s a big honour for me personally. I knew I’d been nominated, but I didn’t really expect to be chosen. Going up during the ceremony and signing my name in the book of members that also includes scientists like Newton and Darwin was a great moment,” he says, though he adds that the quill pen new fellows have to use didn’t make the task easy. Much of Gottlob’s recent research focuses on automatically extracting information from the internet. Most websites are designed to be read by humans; while we can easily recognise which onscreen symbols represent an item’s price, computers find the task impossible. He has developed software that lets users show a computer where to find specific information on a particular website. The computer can then regularly visit that site and report what it finds. Gottlob cofounded Lixto, a Vienna-based company offering the technology to corporate clients for tasks like monitoring rivals’ prices. He now leads the Diadem project, which aims to take the concept much further by creating software to extract information without human guidance from websites within a particular domain, pulling out highly-structured data that can then be manipulated and analysed in other applications. The five-year project received a €2.4m European Research Council Advanced Investigators Award in 2009, with additional pledged funding from the James Martin 21st Century School. Gottlob’s newlyassembled team has already made progress towards creating a new programming language suited to data extraction and manipulation, and work on the project is now moving into high gear.

It could transform how we use the internet, tapping into the ‘deep web’ – the information that’s held in databases behind individual websites, beyond the reach of today’s search engines. For companies, the technology could be still more important, shedding unprecedented light on market conditions and competitors’ activities. Search giants like Google, Yahoo and Microsoft have already shown interest. There could be important financial applications too. Indeed markets move on economic data like inflation figures or housing market announcements. These numbers are calculated from large numbers of prices that are already available, albeit scattered around the internet. By monitoring prices on retail websites, for example, a hedge fund could potentially anticipate official inflation data and position itself accordingly. Another current interest with possible business applications is combinatorial auctions. In these, participants bid on whole packages of goods at a time rather than on single items. They could be useful in many situations, but deciding who has won the auction is challenging. A computer must search through a vast number of possible solutions before deciding which is most profitable for the vendor. In some cases, it may never find an answer. Gottlob has worked extensively on this kind of problem, known as ‘NPcomplete’. His talk at the Royal Society New Fellows Seminar discussed the possibility of identifying and isolating the limited number of difficult cases in which NP-complete problems are insoluble, in order to concentrate on the majority of much easier cases. If such an approach could make it easier to determine a combinatorial auction’s winner, the method could become useful in many business-to-business contexts – one obvious application would be auctioning airport landing slots to airlines.

A convivial type, Gottlob is also enthusiastic about the social side of membership. “I’ve become a member of the Royal Society Club, and I’m really looking forward to joining them for interesting conversation and good company over meals. The club includes no more than 100 current members, so I’m grateful to Terry Lyons [Wallis Professor of Mathematics, Royal Society Fellow and OMI member] for nominating me to join it!”

from fluid dynamics to finance Mike Giles’ mathematical interests have taken him from helping design better jet engines into everything from highperformance computing to valuing complex derivatives portfolios and working with others to process vast amounts of astronomical data. Now Professor of Scientific Computing in the Mathematical Institute and a member of OMI, Giles spent the first part of his working life as an aeronautical engineer specialising in computational fluid dynamics (CFD) – the mathematical study of how fluid moves around obstacles. As a young mathematician at Cambridge, he spent his summers working at Rolls-Royce in Derby. The relationship was to last decades, continuing through graduate study and seven years as a professor at MIT and into a long spell researching CFD at Oxford. Giles only moved into finance in the last few years, but it hasn’t taken him long to make an impression. CFD helps design everything from cars to wind turbines, and is crucial to the aeronautics industry’s efforts to make jet engines more efficient. Once, engineers would painstakingly build and test multiple new designs; now they use computers and the mathematical techniques developed by researchers like Giles to simulate their behaviour. This gives them far more insight into their designs’ aerodynamics, and is responsible for much of the last few decades’ huge advance in aircraft efficiency. In the middle of a successful career, Giles decided to decamp and see what the mathematical and computational tool-kit he’d built up could do in other areas. Embarking on a radical new direction at this stage might look daring, but he says a new challenge was overdue.

“The ratio of perspiration to inspiration was getting unacceptable,” he grins. “CFD modelling programs now stretch over tens of thousands of lines of code; working in the field was getting more and more like managing a large software project. I needed a change, and one of the real joys of academic life is that you can do that.”

Giles settled on quantitative finance as his new field, partly because he knew certain techniques were similar to the CFD equations he was used to manipulating. But his biggest achievement so far has come in an area he’d started with no plans to work in – using Monte Carlo methods to help banks and other institutions quantify risk more efficiently. It’s a great example of the benefits of cross-fertilisation between different fields. Intending to brush up on less familiar areas of financial maths so he could teach them, Giles took a class in London on the theory of Monte Carlo simulation. This is a way of understanding how a complex situation may develop by simulating it thousands of times with different random inputs to account for the uncertainty of future events.

Investment banks do this to model risks involving multiple sources of uncertainty, like how moves in multiple markets would affect a derivatives portfolio. It works well, but takes huge computing power – banks need whole rooms of expensive, electricity-hungry computers just for these simulations. The course was on Monte Carlo methods for pricing option portfolios, taught by Professor Paul Glasserman of Columbia Business School. During a break, Giles asked how much the subject was being simplified for the students’ benefit – he’d noticed an area that could be handled much more efficiently with a branch of mathematics known as ‘adjoint equations’, used in CFD to calculate how sensitive a complex processes results are to its inputs. He assumed they were involved behind the scenes, but were thought too difficult for students at this level. They weren’t, though. Nobody had ever applied adjoints to Monte Carlo simulations, so the finance industry was wasting money by buying much more computer power than it needed. “To someone from a CFD background, it seemed obvious,” Giles explains. “Luckily for me, people developing Monte Carlo methods more often come from statistics or theoretical physics, and nobody had thought of doing this before.” Giles and Glasserman wrote an article on using adjoints to compute option portfolios’ sensitivity to interest rate changes. It won them recognition as 2007’s Quants of the Year from industry journal Risk. Giles knows of major investment banks that are already putting the theory into practice, and others are probably following. When trying to calculate a portfolio’s sensitivity to many factors at once, he thinks his method could reduce the computational workload by a factor of twenty. Subsequent work has adapted the method to computing portfolios’ sensitivity to the degree of correlation between different assets, and Giles thinks more applications remain to be found. Not bad for a first foray into a new discipline.

m i k e gi l es

Beyond financial mathematics, another long-standing interest is highperformance computing, in particular parallel computing – solving big problems with lots of small, cheap processors rather than relying on a single very powerful computer. Beyond a certain point the latter approach becomes impractical, as the laws of physics mean it gets harder and harder to squeeze more processing power into a given area of silicon while dispersing the heat it produces. Parallel computing offers a cheaper and more energy-efficient way forward.

“I’m an evangelist for GPU processing,” Giles says. “You get a lot more done for your money – at the moment, the ratio is about 10 to 1.” He’s collaborated with specialists in fields ranging from engineering and statistics to handling the output of medical imaging hardware in real time.

The field was a big part of Giles’ work with Rolls-Royce. Since moving to OMI he’s kept up the interest, doing extensive work on tapping the unexploited power of

He’s even starting to work on the Square Kilometre Array project, which aims to create the biggest radiotelescope ever built by linking receiving stations which stretch up to 3000km from a central core of dish antennas. One proposed site stretches across Australia and New Zealand; the other extends from a base in South Africa to outlying receivers in Ghana, Kenya, Madagascar and Mauritius. Giles is advising on the high-speed parallel computers that will be needed to piece together the streams of data coming from each receiver to form a single detailed image.

graphics cards. Graphics processing units (GPUs) were originally developed to let gamers enjoy ever-smoother and faster-moving 3D visuals. But in recent years it’s become clear they’re better for certain heavy-duty computational tasks than the central processing units that sit at the heart of most modern computers.

Uniting these varied interests is Giles’ search for new ways to apply his mathematical tools in practical, beneficial ways. The Institute provides a great venue for this. “I’ve been involved with OMI from the start,” he says. “It seemed like a natural place for me. Their aim was to bring together various groups that had been slightly isolated from each other before, and I’ve always been interested in interdisciplinary work and in the practical applications of my research.”

Tasks that lend themselves to parallel processing are particularly suitable for this approach – GPUs are designed to do many small things at once, and can be many times faster at it. Until recently, programming for parallel computing with graphics hardware was discouragingly hard, but that’s now changing, and researchers are working out how to link lots of GPUs together to tackle challenges that would once have seemed impossibly big.

“It’s a great environment – you get into very stimulating discussions with the other people working here.” The achievements of the past couple of years bear witness to just how beneficial those stimulating conversations can be.

finding meaning in the microstructure When it comes to financial markets, people tend to focus on how share prices or exchange rates move over a day or longer. But for Lan Zhang, the really interesting bit is what you see only when you look up close. That means studying not what prices do over weeks, months or years, but how they behave second by second. Zhang is a financial statistician and econometrician, and since arriving from Chicago last year to join OMI as a Senior Research Fellow, as well as becoming part of the Saïd Business School’s finance group, she’s continued to work on what’s known as the ‘microstructure’ of financial markets.

Seen at a very small scale, financial market movements can look meaningless. But the concept of microstructure suggests that by looking at how prices evolve tick by tick, order by order, you can gain surprising insights with much wider application. Zhang became interested in market microstructure after finishing her PhD, and has seen the field take off spectacularly over the last few years. “My interest is in taking financial theory and comparing it to what the data is telling us,” she explains. “We can’t be satisfied with a beautiful model; we have to look at the data and ask if it agrees with what the model is predicting.” Much of her career has been devoted to this – using the statistical study of high-frequency data to improve financial modelling. This is even more important now that the financial crisis has shown the limitations of many widely-used models. Zhang’s recent work focuses on understanding volatility in financial markets better. In principle, volatility is the main determinant of market stability. “The theory says that when looking at data with very high frequency, the volatility should be observable from transactions or quotes,” she explains.

So if you looked at the market second-by-second, you should be able to measure the volatility with perfect precision. But in reality, the opposite is true: the volatility appears to explode out of all proportion to market movements. What’s wrong with the theory?

Zhang and her co-authors found the answer in the theory of what mathematicians call martingales – these are fair games, situations in which no player should be at an advantage and there is no free profit to be made without risk. The central insight was that prices in financial markets combine information with an element of noise. The information is the martingale, the sense in which the market is a fair game, but there is the noise on top. “In the short run, prices are in theory indistinguishable from a fair game, but every price has a noisy component,” Zhang elaborates. “When you look at high-frequency data, the noise starts to swamp the signal – during a very short period of time, the true price doesn’t move very much, but the noise does.” For instance, at the micro level, prices have a tendency to bounce back and forth between the current bid and ask price as traders buy and sell. Or when a trader places a block order to sell many shares and there are not enough buyers to meet the demand, the price repeatedly jerks downwards as the interest at each price level is exhausted. Neither of these patterns conveys much information about the real value of what’s being traded. By adapting martingale theory to cope with the problem of microstructure noise, Zhang found a way to modify classical techniques to account for actual market prices. Estimates of market volatility radically improved, no matter how small an interval prices were examined over.

“Before our work on this, people knew about the problem of how noisy second-by-second data was, but the response was just to use lower-frequency data,” Zhang explains. “They got less noise, but our work showed that the volatility estimate they were getting was still biased, while also imprecise: they’d arbitrarily thrown out a large amount of data!” Her solution managed to eliminate the noise altogether while preserving the usefulness of the complete data. “This is just one example of how data can guide you to update theoretical models,” Zhang notes. The solution is already being used in the markets by major banks and financial institutions, which have put Zhang et al’s ‘two-scale realised volatility estimator’ (TSRV) to work measuring market variability. One bank uses the TSRV every five minutes to get a rolling measure of how volatile the markets are. Not long ago, this would have been inconceivable. Zhang extended this work still further to help risk managers by combining TSRV with the classical GARCH model, to forecast volatility

La n z ha n g

in stock indices. The improvements are dramatic – the model’s predictive power leaps manyfold. She is now working on comparing the method with other measures of market variability, such as the VIX volatility index. So far it’s measuring up well. Another project takes examining high-frequency data to the next stage by looking at order book information. The book doesn’t just include the exchange’s best bid and ask prices; it provides far more information about how much demand there was to buy and sell at a whole range of prices at each instant. Looking at it literally adds an extra dimension to each price: in addition to time, there is now also price-space. Order books can give even more insight into how prices develop, and Zhang is currently using her statistical skills on this problem. Early results suggest that individual quotes possess their own martingale + noise structure. Some of this work is in collaboration with researchers at the Chicago Mercantile Exchange; she also works with Per Mykland at OMI.

“We’re hoping to peel back the noise and look at the functions of different bid and ask prices,” Zhang says. Studying the order book can also help quantify other hard-to-measure qualities of the market, like liquidity – how easy it is to buy or sell a given security without affecting its price. In fact, order book behaviour provided early warning signs of the loss of liquidity which was part of the recent financial crisis. Ultimately, examining this data can provide advance warning of major market shifts; this could help policy-makers, regulators and exchanges as well as traders to avoid being caught off guard.

dealing robustly with risk Not many mathematicians have training in sociology, but the value of exposure to new fields was brought home to Jan Obłój early on in his career. He’s gone from this unusual start to employing probability theory in search of possible ways to reduce financial markets’ dependence on models that recent events have shown to be dangerously flawed. After completing a Master’s in Mathematics in his native Poland, Obłój did a joint PhD in Probability Theory between Paris VI and the University of Warsaw. At the same time, he finished his second Master’s Degree in Sociology at the University of Warsaw.

In France he got into the habit of attending lectures and seminars in his friends’ areas of expertise. “A lot of the time I didn’t understand much,” he admits. “But it was a great way of finding out about new areas – at the end of the lecture I’d have picked up some of the buzz-words and had an idea of the kinds of problem people in that field worked on.” His interest in learning about new areas remained throughout a Marie Curie Postdoctoral Fellowship working with Mark Davis at Imperial College London. And since joining the Oxford-Man Institute of Quantitative Finance, Obłój has continued to work at the boundary between mathematics, finance and probability theory. “I’ve always been interested in real-life applications for my mathematical work,” he explains. “My doctorate was in pure probability, but I wanted to study things with concrete applications – so mathematical finance was a natural area to go into.” When it was time to move on from Imperial, Oxford was one of only a few places Obłój considered. “The Institute was central to my decision,” he says. As he wanted to keep living in London while commuting to Oxford, he couldn’t take on the teaching and administrative workload of a college fellow. Instead, he agreed with the Mathematical Institute that he’d join OMI and be spared both the responsibilities and the perks of college life. “So OMI is like a college for me. I work here, but I also come here to eat, meet people and talk about what we’re all doing,” he adds.

Obłój’s major project in recent years has been the robust pricing and hedging of derivatives using a branch of maths that studies what is known as the ‘Skorokhod embedding problem’. This involves finding ways to value derivatives that don’t depend on assuming any particular model. Current methods, by contrast, generally involve developing a theoretical model of what a derivative’s price should be relative to the asset that underlies it; calibrating this model using data from more widely-traded derivatives, then pricing the derivative in question by assuming that its value shouldn’t leave any possibility of risk-free profit – a fundamental assumption of financial mathematics. The Black-Scholes model that the industry uses to value options works this way. This may work in a normal environment, but the events of the last couple of years have shown how fragile these model-based ways of valuing derivatives are. If the model turns out to be wrong, those who have valued and hedged derivatives using it may face catastrophic losses, like the ones that contributed to the downfall of high-profile banks. Finance professionals call this ‘model risk’ – the danger that your model is exposed by uncooperative reality. By definition we don’t know how wrong a model could be until the damage has already been done. “It’s hard to quantify model risk because we don’t really understand it until it appears,” Obłój explains. “My goal is to develop tools that let us value and hedge derivatives’ positions without relying on models.” He’s gone a long way towards achieving this, alongside collaborators like Alexander Cox at the University of Bath and David Hobson at the University of Warwick. The new method involves taking information from prices of actively-traded assets, and then using this to value a less liquid derivative product using techniques derived from Skorokhod embeddings, which provide what Obłój describes as a ‘different pair of glasses to look at the market through’.

The result is a price range within which the derivative in question’s value must lie. If anyone wants to buy or sell it outside that range, an arbitrage – free money on offer for no extra risk – must be available. The new method doesn’t just make this clear; it can also pinpoint how to profit from this opportunity. So far Obłój has successfully developed robust techniques for illiquid derivatives like double barrier options and weighted variance swaps. In practice, the appeal of the method lies not in the valuation itself, but in its use for hedging. If one derivative’s position is threatening to drag

j a n Ob ł ó j

down an otherwise sound trading book, robust hedging techniques could neutralise that position while leaving the rest untouched. They provide relative security, with a risk profile which is bounded from below.

“If you’ve no idea how to hedge a particular derivative, my method could be a better choice than a model-based approach as it at least puts a floor under your losses,” Obłój explains. “The model hedge will be perfect as long as the model it’s based on is accurate, but there’s the risk of a big loss if it fails. A robust hedge is more expensive, but doesn’t carry this risk.” Another advantage is that robust hedging involves buying and selling securities once or twice in order to neutralise the risk in a position, whereas current model-based techniques entail regularly rebalancing the hedge to keep it working as conditions change, which may entail large transaction costs. Obłój now hopes to take the techniques still further. He thinks working with econometricians and statisticians could improve them more by drawing on historical price records rather than just current market prices, letting him specify the value of the derivatives being priced within a much tighter range, with, say, 95% confidence.

This kind of interdisciplinary cross-fertilisation is where OMI excels. This doesn’t just mean academics; also around the building are quantitative researchers from AHL, part of Man Group, the hedge fund that endowed the Institute in 2007.

“It’s a very stimulating place to be,” Obłój explains. “There are people from all kinds of backgrounds to talk to in the common room, and even if their work doesn’t have any immediate connection to mine it’s interesting to hear about topics outside my experience.” For the moment it’s hard to say how widely the finance industry could adopt his methods for robust hedging. “I don’t know if it will be taken up,” Obłój muses. “Before the crisis, I’d have said not. But the events of the last few years have made people much more aware of model risk. I believe in some situations these techniques could be very useful.”

john armour

tom cass is the Lovells Professor of Law and Finance, having previously held posts at the University of Cambridge and the University of Nottingham. He studied law (MA, BCL) at the University of Oxford before completing his LLM at Yale Law School and taking up his first post at the University of Nottingham.

joined the institute in September 2009 and is a Postdoctoral Research Assistant working in the area of stochastic analysis. His research interests include rough path analysis, interacting particle systems, Malliavin calculus, stochastic differential geometry and Monte Carlo simulation in mathematical finance.

He has held visiting posts at various institutions including Pennsylvania Law School, the University of Bologna, and Columbia Law School. His

He has a PhD, Undergraduate and Master’s Degrees from the University of Cambridge.

main research interest lies in the integration of legal and economic analysis, with particular emphasis on the impact of changes in the law governing insolvency and company law on the real economy. His law and finance research is principally of an empirical orientation. He has been involved in policy related projects commissioned by the Department of Trade and Industry, the Financial Services Authority, and the Insolvency Service.

karen croxson joined the Institute in April 2008. She is currently the RankManning Junior Research Fellow in Economics at New College. Karen completed degrees in economics and German at Birmingham University. She came to the University of Oxford to pursue a Master’s Degree in Economics, followed by a DPhil. Karen’s research interests relate largely to applied microeconomics, in particular the application of game theory within industrial, organisational, and public economics. She has pursued research into several topics connected to finance, recently working on the market microstructure of financial and betting markets, the comparative performance of information aggregation mechanisms such as prediction markets, and theoretical aspects of financial regulation. Other strands of her work have dealt, from a theoretical perspective, with the topics of digital piracy, teamwork and leadership, distributed co-creation, and political extremism.

mike giles is Professor of Scientific Computing at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group. He read mathematics at Cambridge before completing a PhD in Aeronautical Engineering at the Massachusetts Institute of Technology (MIT). He was an Associate Professor at MIT before moving to Oxford in 1992 to join the Computing Laboratory. After working closely with Rolls-Royce for many years developing computational fluid dynamics techniques, he moved into the development of Monte Carlo and finite difference methods in computational finance, which led to his transfer to the Mathematical Institute in 2008. In 2007 he was named ‘Quant of the Year’ by Risk magazine, together with Paul Glasserman of Columbia Business School, for their joint work on the use of adjoints for the efficient calculation of Monte Carlo sensitivities.

m e m bers georg gottlob

ben hambly is a Professor of Computing Science. His research interests are database theory, web information processing and theoretical computer science. At the Oxford-Man Institute, he researches data exchange, semantic database and web querying, and automatic web data extraction for betting and quantitative finance.

He was a Professor at the University of Technology, Vienna from 19882005, where he still holds an adjunct appointment. Before that, he was affiliated with the Italian National Research Council in Genoa, Italy, and with the Politecnico di Milano, Italy. He has received the Wittgenstein Award from the Austrian National Science Fund, is a Fellow of the Royal Society and the Association of Computing Machinery, a member of the Austrian Academy of Sciences, the German National Academy of Sciences, and the European Academy of Sciences Academia Europaea in London.

vicky henderson is a Senior Research Fellow at OMI and is affiliated with the Mathematical Institute. Previously a Reader in the Finance Group at Warwick Business School, Vicky held positions at Princeton University, ETH Zurich, and spent six months at the Isaac Newton Institute, University of Cambridge. Vicky’s research area is mathematical finance with an emphasis on derivative pricing in incomplete markets, particularly via the utility indifference approach. She has worked on optimal stopping problems relating to American option exercise with partial hedging which have been applied to problems in real and executive stock options. Recently, Vicky has studied optimal stopping problems under prospect theory, the results of which help explain disposition effects in financial markets. Vicky has been involved in major conference organisation for the Isaac Newton program and the 2010 Quantitative Finance program at the Fields Institute, Toronto.

is a University Lecturer in Mathematics at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group and the Stochastic Analysis Group. He has a PhD from the University of Cambridge and previously had lectureships in Edinburgh and Bristol. He is Co-editor in Chief of Applied Mathematical Finance. His research interests in mathematical finance are in the modelling and pricing of financial derivatives. In particular he has worked on electricity spot price models and the pricing of complex derivative contracts in energy markets. He is also interested in credit markets and the pricing of large portfolio credit baskets contracts. His other research interests include random walks and diffusion in random and fractal environments, rough paths, branching processes, random matrices and particle systems.

chris holmes moved to Oxford in 2004 as a Lecturer within the Department of Statistics. He holds a ‘Programme Leaders’ award in Statistical Genomics from the Medical Research Council. He was awarded the title Professor in 2007 and the Royal Statistical Society’s Guy Medal in Bronze in 2009. Chris’ research is focussed on Bayesian methods and computation for high-dimensional inference problems, in particular, analysis techniques for sequential data structures arising in bioinformatics, statistical genetics and genetic epidemiology. Within the Oxford-Man Institute he has ongoing projects with Mike Giles on graphical processing unit (GPU) implementation of Monte Carlo methods for dynamic inference problems, and Stephen Roberts on Bayesian nonlinear models. Chris studied for his PhD in Bayesian Nonlinear Methods within the Statistics Group in the Department of Mathematics, Imperial College London. Following this he undertook a postdoc and then lectureship within the department. In 2002 he was awarded the Royal Statistical Society’s biennial ‘Research Prize’ for his work in Bayesian statistics.

sam howison

hanqing jin is an applied mathematician working in the Mathematical Institute. He uses applications of differential equations and appropriate approximation procedures. His interests include many aspects of mathematical finance, such as derivatives pricing and models of unusual markets.

completed his PhD in Financial Engineering in 2004 at the Chinese University of Hong Kong. He is a University Lecturer at the Mathematical Institute. His research interests include portfolio selection, behavioural finance, applied stochastic analysis and optimisation.

dmitry kramkov

shin kanaya is a Postdoctoral Research Fellow at the Department of Economics. He earned a Bachelorâ&#x20AC;&#x2122;s and Masterâ&#x20AC;&#x2122;s degree from the University of Tokyo, majoring in economics, and he earned a PhD in Economics from the University of Wisconsin-Madison in 2008. His primary field is financial and time-series econometrics, with an emphasis on nonparametric testing and estimation problems of continuous-time economic and financial models. He is currently interested in the following econometric topics: nonparametric testing of the stationarity for Markov processes, nonparametric estimation and forecasting using high frequency data.

christian litterer is a Postdoctoral Researcher in the Stochastic Analysis Group. His main research interests are in numerical analysis on Wiener space. In particular, he is interested in cubature methods and their application to problems in computational finance and non-linear filtering. He is also working on rough path theory and its probabilistic applications.

is a Professor at CarnegieMellon University, Pittsburgh, and part time Professor at the University of Oxford. He is a member of the Scientific Board of the Bachelier Finance Society. He currently serves as an associate editor for the academic journals of Stochastic Processes and their Applications and Finance and Stochastics. He was an undergraduate at Moscow Institute of Physics and Technology and did his graduate studies at Steklov Mathematical Institute in Moscow. His research primarily focuses on the mathematical questions of continuous-time finance. He is currently working with Peter Bank on an equilibrium-based model for price impact effects with applications to optimal investment, pricing of contingent claims and optimal execution policy. He is writing an undergraduate textbook on mathematical finance with William Hrusa (Carnegie-Mellon University) and is also working on an open source version of C++ library for pricing of financial derivatives. This library is currently used for the teaching of Masterâ&#x20AC;&#x2122;s Courses in Computational Finance at Carnegie-Mellon University and the University of Oxford.

m e m bers josĂŠ martinez

terry lyons is a Lecturer in Finance at the SaĂŻd Business School. He obtained his PhD from Columbia Business School. Before joining the University of Oxford he was a Visiting Researcher at the Institute for Financial Research in Stockholm, Sweden. JosĂŠ specialises in capital markets, investments and investor behaviour.

His research explores the role of information sellers in financial markets and the use investors make of their financial advice. He is also interested in the differences exhibited by pension and mutual fund investors and is currently working on understanding how capable individuals are of managing their retirement accounts.

per mykland is Man Professor of Finance and Statistics. His research currently focuses on high frequency financial data. With Lan Zhang, they have studied inference based on martingale (fair game) theory, often with noise caused by market microstructure. A recent article shows how to tie this kind of econometrics up with classical statistics using the statistical device known as contiguity. Mykland also works on the interface between finance and econometrics, including how to set intervals for prices when models are uncertain. He believes high frequency data is the empirical manifestation of continuous-time financial models, and hopes for the eventual creation of a unified theory of finance embracing asset pricing, quantitative finance, and econometrics to help the management of risk in financial markets. Mykland has earlier worked on fair games and their interface with likelihood theory, and on survival analysis in medical studies. He says that both of these backgrounds continue to give him inspiration for the analysis of financial data.

is the Wallis Professor of Mathematics and a Fellow of the Royal Society. Terry is an expert in stochastic analysis; he focuses on developing methodology needed to model interactions between systems that have a high degree of oscillatory behaviour. For example, developing systematic ways to construct representative scenarios that identify and quantify outlying behaviour and identify small but significant risks that systems might be exposed to. He is working on projects with a number of Oxford academics: With Danyu Yang he is trying to identify whether a system subject to external forcing would have large movements (risk of earthquake damage). With Arend Janssen he is trying to model order books. With Wei Pan he aims to model and hedge with the volatility surface and is using cubature methods to simulate solutions to Levy type non-local integral equations. He is collaborating with Gechun Liang and Zhongmin Qian to extend the notion of Backward SDE, and with Tom Cass to develop a mathematical framework for continuum models.

sergey nadtochiy is a Senior Postdoctoral Research Fellow at OMI. His research interests lie in the field of financial mathematics, specifically the applications of stochastic and functional analysis for the pricing and hedging of financial derivatives. His current research is concerned with the construction of so-called ‘market models’ – the financial models that are designed to be permanently consistent with the prices of the liquidly traded derivatives. In addition, he has done work on static hedging; obtaining exact semistatic replication strategies for barrier options with European-type securities in a large class of models. Sergey’s new subject of interest is portfolio choice, he is working on explicit description of optimal investment strategies in the presence of untradeable risks, and/or ambiguity about the investor’s preferences.

zhongmin qian is a University Lecturer at the Mathematical Institute. His research in recent years concentrates on rough path analysis, and non-linear partial differential equations arising from applied areas including those from mathematical finance, stochastic analysis, and backward stochastic differential equations. Zhongmin Qian is also interested in the Ricci curvature and the related partial differential equations.

jan obłój is a University Lecturer at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group. Before coming to Oxford he was a Marie Curie Postdoctoral Fellow at Imperial College London. He holds a PhD in Mathematics from the University Paris IV and Warsaw University. His general interest is in mathematical finance and its interplay with probability theory, and he looks at a number of different problems where tools from martingale theory and stochastic analysis can be applied. Recent areas of focus include: robust pricing and hedging of exotic derivatives via the Skorokhod embedding problem, market completion using options, volatility derivatives and extrapolation of implied volatility surface, portfolio optimisation under pathwise constraints and hedge-funds managers’ incentive schemes.

tarun ramadorai is a Reader in Finance at the Saïd Business School. Tarun has a BA in Mathematics and Economics from Williams College, an MPhil in Economics from Emmanuel College, Cambridge, and a PhD in Business Economics from Harvard University. He is also a Research Affiliate of the Centre for Economic Policy Research, London. He has published papers in journals such as the Journal of Finance, The Journal of Financial Economics and The Review of Financial Studies. His main areas of interest are capital markets, international finance and hedge funds. His current research deals with two main topics: the impact of international investment flows on equities and foreign currencies in a range of countries; and the performance, riskiness and capital formation processes of hedge funds. He has taught courses on international finance, behavioural finance, hedge funds and investment management for the Master of Financial Economics, MBA, Executive MBA, and PhD programs at the University of Oxford.

m e m bers steve roberts

neil shephard is Professor of Information Engineering at the University of Oxford. He studied physics, completed a PhD in Signal Processing and was appointed to the faculty at Imperial College London, before taking up his post in Oxford in 1999. He heads the Pattern Analysis and Machine Learning Research Group.

His main area of research lies in machine learning approaches to data analysis. He has particular interests in the development of machine learning theory for problems in time series analysis and decision theory. Current research applies Bayesian statistics, graphical models and information theory to diverse problem domains including mathematical biology, finance and sensor fusion. He has been awarded two medals by the IEE for papers on Bayesian signal analysis. His current research focuses on statistical models for sequential change-point analysis, forecasting and decision making and decentralised multi-agent co-ordination.

kevin sheppard

is Research Director of the Oxford-Man Institute and Professor of Economics at the University of Oxford. He is a Council Member of the Society of Financial Econometrics and an Associate Editor of Econometrica. Neil is a member of the advisory boards of research centres at Aarhus University and Singapore Management University. His research interests are mainly focused on econometrics â&#x20AC;&#x201C; particularly working with high frequency data to try and understand financial volatility and time varying dependence, market microstructure and the role of jumps in financial markets. He is also interested in the use of simulation to carry out econometric inference. He was an undergraduate at York studying economics and statistics. He has carried out graduate work and taught at LSE. He was elected a Fellow of the Econometric Society in 2004 and a Fellow of the British Academy in 2006.

ruediger stucke is a University Lecturer in the Department of Economics. His research interests focus on financial econometrics. He has carried out work on estimating large dimensional time-varying covariance matrices and has recently focused on the use of high frequency data to more precisely estimate dependence amongst asset returns.

Kevin was an undergraduate at the University of Texas at Austin and did his PhD at the University of California, San Diego.

is a Research Fellow in Finance and Economics at the SaĂŻd Business School. Ruediger came to the SaĂŻd Business School in 2007 to finish his PhD, which he had previously started at Paderborn University, Germany. Prior to this, he studied business administration and computer science at Paderborn University. His research interests cover the whole field of private equity, with focus on the buyout industry. Affiliated areas of interest include leveraged and structured finance, corporate valuation and mergers and acquisitions.

m e m bers pierre tarrĂ¨s

mungo wilson is a University Lecturer at the Mathematical Institute and works on self interacting random processes, especially reinforced random walks, and stochastic algorithms and their relationship with dynamical systems.

His approach is purely mathematical, relating to economics with the study of reinforcement learning in game theory and to statistical learning.

thaleia zariphopoulou is the first holder of the Man Professorship of Quantitative Finance and is a member of the Mathematical Institute. Her area of expertise is in financial mathematics, quantitative finance and stochastic optimisation. Her research interests are in portfolio management, investment performance measurement and valuation in incomplete markets.

xunyu zhou is the Nomura Chair of Mathematical Finance and Director of the Nomura Centre for Mathematical Finance at the University of Oxford. He obtained his PhD at Fudan University in 1989. He currently focuses on the mathematics of behavioural finance. Prior to joining the University of Oxford he was Chair of Systems Engineering and Engineering Management at the Chinese University of Hong Kong. His general research interests are in quantitative finance, stochastic control and applied probability, while he has recently engaged in mathematical behavioural finance research. He is a Fellow of IEEE and a winner of the SIAM Outstanding Paper Prize. He is on the editorial boards of Mathematical Finance, Operations Research, SIAM Journal on Financial Mathematics and SIAM Journal on Control and Optimization.

is a Lecturer in Financial Economics at the SaĂŻd Business School. His research interests include determinants of expected returns, credit risk, mutual funds and portfolio allocation.

lan zhang is a Senior Postdoctoral Research Fellow and Reader in Financial Econometrics at OMI. Her research focuses on market microstructure, statistical arbitrage, and high frequency financial econometrics. Before joining, she was an Associate Professor at the University of Illinois at Chicago and an Assistant Professor at Carnegie Mellon University (2001-2005). She was an undergraduate at Peking University and obtained her Masterâ&#x20AC;&#x2122;s Degree and PhD from the University of Chicago. She spent 2000-2001 at Princeton University as an Exchange Scholar.

A da y i n t he life of the.. .

O x f o r d-Man Institut e

Many different kinds of people come together at Oxford-Man Institute – professors and students, academics and hedge fund research analysts, mathematicians, statisticians and economists. The next few pages give a variety of perspectives on the Institute as a venue for research, social interaction and academic collaboration.

Tim Jenkinson is Professor of Finance at the Saïd Business School, specialising in the empirical study of corporate finance, and an associate member of OMI. He is Director of both the Oxford Private Equity Institute and Oxford Finance, the umbrella group for researchers working in the field at Oxford. “OMI is the interdisciplinary research centre for finance in Oxford. It’s one of the University’s unique features – it means people from different areas see each other a lot more often than they otherwise would. This is what Oxford Finance was also set up to encourage, but bringing everyone together in the same building makes it far easier to turn the vision of integrating financial research across the whole university into a reality. Before OMI, we were all beavering away in our separate departments and when we did get together it was in a much more haphazard way. The formation of OMI has created an environment that allows Oxford Finance to flourish – it provides both the institutional support and the physical environment for us to meet in. It doesn’t just enable the hard research – it also encourages the softer kind of social interaction that’s critical for progress in any discipline.”

Gechun Liang joined OMI in 2007 and is currently finishing his DPhil in Mathematics. He has now been offered a position as a Postdoctoral Researcher at the Institute, which he will take up later this year. His main research interest lies in backward stochastic differential equations and their applications in finance, but he also has a sideline in developing models to understand how bank runs and liquidity crises unfold. “I’ve been a member of OMI since the beginning – I think I’ve been here as long as anyone! I’d applied to do my DPhil at Oxford and I didn’t really know anything about the Institute. But my supervisors, Terry Lyons [Wallis Professor of Mathematics] and Zhongmin Qian [University Lecturer in Mathematics] told me about OMI and helped me apply. One of my favourite things about working here is the interdisciplinary co-operation. For example, I did a joint project with Vicky Henderson [OMI Senior Research Fellow] on modelling credit risk – I knew about her work before she joined OMI, but when we met over lunch we found we had several common interests. Working with Vicky was really valuable – I got a lot of new insights from her. There are academics here from many different areas, but we’re interested in similar problems. Working together helps us broaden our knowledge and get fresh ideas – I don’t know of anywhere else that achieves this so well. Having AHL in the building helps a lot too. We work mainly with theory but they work in practice, so they think in a very different way. For example, yesterday I was talking to Anthony Ledford [Chief Scientist at the Man Research Laboratory] after a seminar and he made the point that presentations should be in clear, everyday language with as few equations as possible. “People come to this kind of event to get fresh ideas”, he said; “if they want more details they can always read your paper, but first you have to explain the basics clearly.” It may sound simple but in an academic environment it can be easy to lose sight of this – it’s helpful to have people from a commercial background around to point this sort of thing out.”

Michael Monoyios is a University Lecturer in Financial Mathematics and an associate member of OMI. “I was invited to join OMI the year after it was created, and it’s been brilliant for me. It’s a superb set of facilities. I don’t do my day-to-day work there but I do come along to the seminars – there are usually around two a week during term and I’ve co-organised a conference at the Institute. I’d been through this before and I didn’t much want to do it again, but I agreed because I knew I’d get very professional support from the team at the Institute. As it turned out they made the whole process easy. I’ve had some very fruitful discussions at the Institute and I’ve now got some fledgling collaborations that are starting to get off the ground. For example I’ve been working with Christoph Reisinger, Sam Howison (both OMI) and Jeremy Large (AHL) on liquidity models. Nothing very concrete has come out of this yet, but we’re hoping it will lead to a very good publication. Another colleague in the maths faculty has been working with Per Mykland [Man Professor of Finance and Statistics] after they met at an OMI research seminar and discovered a common interest. Research is a mysterious thing. Nobody really understands why when you put people with different backgrounds together, things happen. It’s a process of osmosis – in the long term, bringing economists, mathematicians, physicists and others together will definitely improve the work they do. There’s a serious critical mass of people there now – a good part of our mathematical finance group now has their offices at Eagle House – and that’s a function of the resources Man have put into the project.”

Anthony Ledford is Chief Scientist at AHL and is based in AHL’s Oxford office, the Man Research Laboratory, which is co-located with OMI. Prior to joining AHL he lectured in statistics at the University of Surrey. Dr Ledford studied mathematics at Cambridge University and holds a PhD from Lancaster University in the development and application of multivariate extreme value methods. In 2001 he joined AHL’s research group and has since then worked on the research and development of its automated trading and execution models and systems. “Early morning is a productive time for me, I’m normally

One of the most important aspects of our collaboration

at my desk by about 7.30am and the first thing I do is to

with OMI is the cross fertilisation of ideas between their

go to the common room and make myself a cup of coffee.

curiosity driven academic research and Man’s commercially

Gerd Heber, who manages the research computer systems

driven research. I liaise with OMI academics in a number of

for OMI, starts at the same time and tends to be the first

different ways, including attending regular meetings of their

person I see – that’s what’s great about our shared space, the

Research Committee, Management Committee and Executive

opportunity for our AHL staff and OMI to mix informally in

Committee. Every three months OMI provides a report on

this purpose-designed environment.

the quantitative finance research that its researchers are

I spend a lot of time liaising with AHL’s various research groups as research and innovation are key to AHL’s success and are at the heart of what we do. Quantitative models

engaged in. That’s important for us and for the Institute because it allows the University to get early identification of any research that could be commercially useful.

lose their efficacy over time unless they are constantly

But that’s not the only way that I interact with OMI’s academics,

refreshed. That’s one of the reasons why our research team

nor is it necessarily the most important. Prof Neil Shephard and

has ballooned to 75 people from just 22 five years ago.

I meet often over lunch or a coffee in the common room. A lot

The coordination of people and projects is a constant challenge. Our research team is spread across London, Oxford and Hong Kong – and we will soon open an office in Pfaeffikon, Switzerland. This means we use video conferencing over our own global network for many of our

of thought went into the design of the building, but especially into the areas we share. It was important to both OMI and Man that the common room, seminar rooms and lecture theatre were bright, modern and comfortable, encouraging people to mix and spend time together there.

daily meetings; the technology is so advanced these days

Our collaboration with OMI also allows AHL research staff

that you can have a conference call with someone in Hong

to attend the regular lectures and seminars held in the

Kong and feel they are almost in the same room!

lecture theatre here at Eagle House, where both internal

Our Research Committee comes up from London on a regular basis and we spend the entire afternoon in our meeting room looking at our whole portfolio of initiatives. Other times I’ll take a more focused look at a single research

and prominent external speakers present their work. Once again this brings both parties together. Additionally our own researchers have the opportunity to present their work at the regular Wednesday lunchtime workshops.

theme, trying to pick up on anything that’s behind schedule

I think one of the most rewarding and challenging things

or anything that’s going really well. My meeting this

about working for AHL is the variety of people and disciplines

morning looked at proprietary trading proposals. When

I get to work with and it is even more enjoyable because I get

we introduce new systems we first trade using Man’s own

to do that in the relaxed and collegiate atmosphere alongside

money before we introduce those systems into the main

OMI. It’s been a very successful relationship so far and looking

client funds. We have a rich research pipeline at the moment

at what has been achieved already I’m really excited about

having invested heavily in research over the last few years.

the prospects for the future.”

Bahman Angoshtari joined the Institute to start a DPhil in Mathematical Finance with Thaleia Zariphopoulou, Man Professor of Quantitative Finance, in October 2009. “The Institute was a big factor in my decision to come to Oxford. As an overseas student from outside the EU it’s hard to get funding to study in the UK, but the Institute has been extremely supportive. And for doctoral students, this working space is completely unique in Oxford. It’s a fantastic environment for research – it’s great to work alongside so many people from different academic backgrounds. Many PhD students only meet their supervisor every couple of weeks, and spend a lot of time in between working on their own. But I meet people at lunch and throughout the day, and I’ve already had some incredibly useful conversations – some people from the AHL laboratory even helped me come up with the idea for my thesis topic. It’s particularly helpful to talk to people from a more commercial background – it definitely adds to the value of being here. I think this is unique – I know about other places that emphasise interdisciplinary work among the academic community, but the commercial element is usually missing, and this is what makes OMI so special. I still don’t know if I want to be an academic or join the financial industry, but being here has certainly exposed me to both possibilities. Academic researchers in mathematical finance speak a different language to people from the financial industry. But I think both can benefit from closer contact. Academics can get new ideas and correct any mistakes and unrealistic assumptions; finance professionals can make their ideas more precise and gain a wider perspective on their experience.”

Thomas Flury is a Quantitative Research Analyst in the Directional Model Development Team at AHL. He joined early in 2010 after completing his DPhil in Economics at the Institute. “As a doctoral student in economics at OMI it was great to learn new ways of thinking from researchers in different fields. I still find it really useful talking to people over lunch, hearing about what they are doing and picking up new ideas – sometimes even small things can get you thinking in a new direction. For students, the working environment at OMI is amazing – for example, the IT facilities are brilliant compared to what’s available elsewhere. I had to run complex computer simulations for my DPhil; if I’d had to rely on the facilities at the Economics department, this would have taken much longer! I’ve always been interested in the financial markets and during my doctorate I spent some time getting more practical experience of them. Initially I had the idea that working in the finance industry would be rather remote from doing real research. Whilst a student at OMI I met the people from AHL, and I realised you can do high-level research at hedge funds. And because we were working in the same building, I understood their working culture and knew it was the kind of place I’d like to be. This made the move from being a student to working in the industry much easier. I’m still in Oxford, still working in research and I still have the opportunity to go to all OMI seminars as part of AHL’s collaboration with OMI – it’s been a very gentle transition. If I hadn’t been exposed to people from AHL while at OMI, I don’t know if I’d ever have made the leap into the finance industry.”

Gordan Zitkovic is Assistant Professor in the Department of Mathematics at

the University of Texas at Austin. He spent a month at OMI as a long-term visitor in summer 2010, during which he worked with Professor Thaleia Zariphopoulou, a long-standing collaborator, and taught a short course on the foundations of mathematical finance. â&#x20AC;&#x153;I am very impressed by the way the Institute is structured, both organisationally and physically. It allows for easy interaction and provides plenty of opportunity for collaboration. I especially liked the fact that the Man Research Laboratory shares the common space with academics. This way a theoretician like me can learn more about the practical aspects of the financial industry. I hope the arrangement also helps the quants keep abreast of the latest academic research. I believe both sides can benefit greatly from such interactions. The commercial outlook usually comes with a dose of realism we sometimes neglect in the academic world. Apart from many fruitful exchanges with Professor Zariphopoulou, I interacted quite a bit with other faculty members too. For example, Professor Xunyu Zhou and I started a discussion on a possible

research project that would tie a number of emerging ideas on behavioural science with some classical economic insights. I also enjoyed immensely the very rich informal seminar schedule â&#x20AC;&#x201C; a wonderful forum for the exchange of ideas. Not only did I get the chance to learn about the latest research findings of the members of the Institute; I also had a chance to present my own research. OMI is quite unique, especially from the point of view of someone coming from the US. It is very rare there that a for-profit company has such a clear and healthy long-term vision - embodied in the investment in cutting-edge academic research - as the Man Group. I believe that similar collaborations mark the future of any successful financial enterprise, and I applaud OMI for its vision.â&#x20AC;?

stude n ts bahman angoshtari is a first year DPhil student in the Mathematical Institute, University of Oxford. He holds an MSc in Applied Mathematics from the University of Twente and a BSc in Industrial Engineering from Sharif University of Technology, Iran. His research interests lie in the application of stochastic analysis and control theories in finance, especially in portfolio choice. He is currently focused on identifying the optimal investment strategy in a market with co-integrated assets. The results are directly applicable to pairs-trading, and possible extensions to statistical arbitrage are under investigation.

youness boutaib is a DPhil student in the Stochastic Analysis Group. Working with Professor Terry Lyons has drawn his attention to the power of the theory of rough paths. The theory, along with giving the appropriate frame of solving equations driven by very irregular signals (like the fractional Brownian motion), encompasses the previous theories of integration (Stieltjes, Young and Stratonovitch). He aims to develop a control theory based on it that would help solve optimisation problems of systems that are ruled by differential equations driven by rough paths. Applications naturally include finance and quantum physics and other older classic problems.

christopher fogelberg is a DPhil Student within the Computing Laboratory, University of Oxford. His research interests are graphical models, particularly their efficient structural inference in bioinformatics and related fields. He is especially looking into structural expectation maximisation and dimensional reduction for dynamic Bayesian networks, and hopes to extend this research to other graphical models like Markov random fields.

xiaowei gong is a visiting student from the University of Illinois, Chicago. Her research interests include limit order book modelling, statistics applied to finance, time series analysis and computational statistics.

arend janssen is a DPhil student in Mathematics at the University of Oxford. He holds a degree (Diplom in Mathematics) from the University of Freiburg, Germany. Arendâ&#x20AC;&#x2122;s research interests lie broadly in Mathematical Finance and Stochastic Analysis, where he is particularly interested in order book models. He is also interested in the theory of rough paths and their applications to finance. Recently, Arend has been working on numerical solutions of stochastic differential equations driven by rough paths.

lei jin is studying for a DPhil in Mathematics at the University of Oxford under Ben Hambly. Her research interests include stochastic partial differential equations, particle systems, optimal stopping problems and credit derivatives modelling and pricing. Lei has already submitted her DPhil thesis “Particle Systems and SPDEs with Application to Credit Modelling”. She will start work with Goldman Sachs in September 2010.

nathaniel korda is studying for a DPhil in Mathematics at the University of Oxford under Pierre Tarrès. In 2007 he completed his Undergraduate Master’s Degree in Mathematics at the University of Oxford. Nathan’s research is focused on the n-Armed Bandit. An n-Armed Bandit is a simple probabilistic model of a game in which one repeatedly chooses to play one of n arms, each of which will yield some reward with a certain fixed, but unknown, probability. His current interests lie in the asymptotic properties of various strategies proposed in the literature for this game.

ada lau is studying for a DPhil in Mathematics at the University of Oxford. Her research interests include wind power forecasting, volatility forecasting, risk analysis and weather derivatives. Ada is also a research member of the System Analysis, Modelling and Prediction Group. She obtained a BSc in Mathematics and Physics at the University of Hong Kong and an MPhil in Physics at the Chinese University of Hong Kong.

anthony lee is a DPhil student in the Department of Statistics. He completed Bachelor’s and Master’s Degrees at the University of British Columbia, specialising in Computer Science. Anthony’s research interests lie broadly in computational statistics and Bayesian inference, with emphasis on the design and application of simulation-based numerical integration techniques in complex, data-rich domains including those found in quantitative finance. More specifically, he is interested in enhancing and expanding the use of advanced Monte Carlo methods, such as Markov chain Monte Carlo and sequential Monte Carlo, in statistical inference.

gechun liang is a third year DPhil student in the Stochastic Analysis Group of the Mathematical Institute. His supervisors are Terry Lyons and Zhongmin Qian. He has a Master’s Degree in Mathematics from Tongji University, and studied finance as an undergraduate in Jilin University. His research focuses on stochastic analysis and financial mathematics. Specifically, he is interested in backward stochastic differential equations and applications of the cubature method and rough path theory. He is also interested in credit risk modelling using utility indifference valuation.

stude n ts arnaud lionnet is a DPhil student at the Mathematical Institute and a member of the Stochastic Analysis Group. His research interests are in the analysis and application of stochastic models.

diaa noureldin is a DPhil student in Economics at the University of Oxford, where he also studied for an MPhil in Economics. He holds a Bachelorâ&#x20AC;&#x2122;s Degree in Economics from the American University in Cairo. Diaaâ&#x20AC;&#x2122;s research interests lie in the field of financial econometrics. His MPhil thesis focused on modelling the dynamics of the term structure of interest rates using copula methods. For his DPhil research, he is currently studying a new class of models for forecasting multivariate volatility using high-frequency data.

cavit pakel is interested in the field of financial econometrics and, specifically, in volatility modelling. He is also interested in the nuisance parameter issue and bias reduction in the likelihood framework. His current research focuses on elimination of bias in GARCH panels, a model that enables univariate volatility modelling using a panel of asset returns, as opposed to considering a single time-series only. As such, this structure makes it possible to model volatility using a smaller than usual number of observations in the time-series dimension.

dan wang is a visiting student from the University of Chicago. Her research interests include stochastic calculus and econometrics.

sumudu watugala is interested in the areas of international finance, financial markets, contagion, and volatility. Her current work focuses on how interlinks between countries such as trade and capital flows affect markets and economies, especially during periods of financial crisis. Her undergraduate and previous postgraduate study was in computer science, engineering, and finance at MIT. Sumudu worked in the finance industry, specialising in volatility and derivatives, prior to joining Oxford for her doctoral studies.

yuan xia is a DPhil student at the Mathematical Institute. His research interests include numerical methods in finance, currently focusing on Multilevel Monte Carlo method for jump processes.

weijun xu is a first year DPhil student in the Stochastic Analysis Group. Before joining Oxford, he completed a Degree in Economics and Mathematics at Shanghai Jiaotong University, and a Masterâ&#x20AC;&#x2122;s Degree in Statistics at Harvard University. Weijun is interested in various problems in probability theory. His current interest lies in exploring the relationship between paths and signatures, analogous to that of functions and their Fourier series. He is currently focused on inversion of signature.

yifei zhong is a second year DPhil student in the Mathematical and Computational Finance group at the Mathematical Institute. He completed a Bachelor of Science Degree at Peking University in China and a Master of Science degree at the National University of Singapore, specialising in mathematical finance. Currently, his research is focused on optimal stopping time and applied partial differential equations. He is also interested in behavioural finance and time inconsistent problems.

mathematics

Wei Pan, DPhil Student in the Stochastic Analysis Group, University of Oxford.

Faculty

Research Interests Stochastic Analysis and Mathematical Finance, in Particular Volatility Surface Dynamics Implied by Different Pricing Frameworks

Greg Gyruko, Departmental Lecturer at the Mathematical and Computational Finance Group, University of Oxford.

Jan Tudor, DPhil Student in the Stochastic Analysis Group, University of Oxford.

Research Interests Rough Paths Based and Probabilistic Numerical Methods and their Applications in Computational Finance

Research Interests Non-Linear Stochastic Evolution Equations Motivated by Navier-Stokes

Michael Monoyios, University Lecturer in Financial Mathematics at the Mathematical Institute, University of Oxford.

Danyu Yang, DPhil Student at the Mathematical Institute, University of Oxford.

Research Interests Optimal Hedging in Incomplete Markets, Transaction Costs and Singular Control, Parameter Uncertainty in Investment and Hedging, Insider Trading and Information Problems Christoph Reisinger, University Lecturer in Mathematical Finance at the Mathematical Institute, University of Oxford. Research Interests Modelling of Financial Markets and the Development, Analysis and Implementation of Efficient Methods for Derivative Pricing Zuoquan Xu, Nomura Junior Research Fellow at the Mathematical Institute, University of Oxford.

Research Interests Rough Path Theory, Extreme Events in Enforced Nonlinear Systems, Stochastic Differential Equations

sa誰d business school Faculty Tim Jenkinson, Professor of Finance at the Sa誰d Business School, University of Oxford. Research Interests Initial Public Offerings, Private Equity, Securitisation, Regulation and the Cost of Capital

Research Interests Mathematical Finance, Stochastic Control, Optimisation and Applied PDE

Colin Mayer, Peter Moores Dean and Peter Moores Professor of Managements Studies at the Sa誰d Business School, University of Oxford.

Students

Research Interests Corporate Finance, Corporate Governance, Corporate Taxation, Regulation of Financial Institutions

Horatio Boedihardjo, DPhil Student at the Mathematical Institute, University of Oxford. Research Interests Schramm-Loewner Evolution in Riemann Surfaces Stephen Buckley, DPhil Student in the Stochastic Analysis Group, University of Oxford. Research Interests Random Walks in Random Environments Hualei Chang, DPhil Student in the Mathematical and Computational Finance Group, Mathematical Institute, University of Oxford. Research Interests Mathematical Finance, Stochastic Control, Optimisation Alice Dub, DPhil Student at the Mathematical Institute, University of Oxford. Research Interests Stochastic Control, in Particular the Merton Problem of Optimal Investment with Intermediate Consumption Ni Hao, DPhil Student in the Stochastic Analysis Group, University of Oxford. Research Interests Rough Paths Theory and Cubature Xiaodong Luo, DPhil Student at the Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford. Research Interests Nonlinear Time Series Analysis, Nonlinear Dynamical System Theory, Statistical Modelling and Inference

Thomas Noe, Ernest Butten Professor of Management Studies at the Sa誰d Business School, University of Oxford. Research Interests Corporate Finance, Financial Security Design, Game Theory, Artificial Agent Economies

engineering science Student Nauman Shah, DPhil Student in the Pattern Analysis and Machine Learning Group, Department of Engineering Science, University of Oxford. Research Interests Analysis of Multivariate Financial Time Series using a Variety of Pattern Recognition, Signal Processing and Machine Learning Methods

ass o c iate m e m bers law

external

Faculty

Nicholas Beale, Managing Director, Sciteb Ltd.

Wolf-Georg Ringe, DAAD Lecturer in Law at the Institute of European and Comparative Law, University of Oxford.

Andrea Calรฌ, Lecturer, Brunel University.

Research Interests Law and Finance, Company Law, Conflict of Laws and European Law

Research Interests Knowledge Representation and Reasoning, Database Theory, Web Information Systems, Information Integration, Logics and Databases Thomas Flury, Quantitative Research Analyst, AHL.

physics

Research Interests Time Series Econometrics, Financial Econometrics and Parameter Estimation with Particle Filters

Faculty

Matthias Hagmann-Von Arx, AHL.

Nick Jones, Systems Biology Fellow at the Department of Physics, University of Oxford.

Jeremy Large, Research Economist, AHL.

Research Interests Non-Trivial Temporal Correlations Present in the Complex Signals that emerge from Natural Systems and how these Signals Couple to Underlying Network Dynamics

statistics Faculty Bernard Silverman, Chief Scientific Adviser to the Home Office and a Professor of Statistics at the University of Oxford. Research Interests Computational Statistics, Smoothing Methods, Functional Data Analysis, Multiresolution Analysis in Statistics and the Analysis of Very High Dimensional Data

Anthony Ledford, Chief Scientist, AHL. Research Interests Extreme Value Theory, Modelling Financial Time Series, Automated Trading and Execution Systems, Market Microstructure and High Frequency Trading Asger Lunde, Professor of Economics, School of Economics and Management, Aarhus University. Research Interests Time Series Econometrics, Financial Econometrics, and the Econometrics of Marketing Andrew Patton, Associate Professor of Economics, Duke University. Research Interests Financial Econometrics, Forecasting, Volatility and Dependence Models, Hedge Funds Torsten Schรถneborn, Quantitative Analyst, AHL.

Student Cornelius Probst, DPhil Student at the Department of Statistics. Research Interests Bayesian Statistics under Computational and Temporal Constraints: Sequential Monte Carlo with Data Streaming Methods. Topics in Computational Statistics such as GPU computing. High-Frequency Financial Data such as Limit Order Book Data

computer science Student Andrรกs Salamon, DPhil Student at the Computing Laboratory, University of Oxford. Research Interests Constraint Satisfaction, Applications of Computational Complexity to Quantitative Finance, Limitations of Parallel Programming

Research Interests Market Microstructure, Optimal Trade Execution, Optimal Investment under Transaction Costs Jonathan Tawn, Professor of Statistics at Lancaster University. Research Interests Extreme Value Theory

g o v er n a n c e advisory board Rene Carmona is the Paul M. Wythes ’55 Professor of Engineering and Finance at Princeton University.

Robert Engle is the Michael Armellino Professor of Finance at New York University Stern School of Business.

He is a member of the Department of Operations Research and Financial Engineering and Director of Graduate Studies at the Bendheim Center for Finance.

He was awarded the 2003 Nobel Prize in Economics for his research on the concept of autoregressive conditional heteroskedasticity (ARCH).

Peter Carr is a Managing Director at Morgan Stanley heading up market modelling.

Hans Föllmer is Professor of Mathematics at Humboldt University.

He is also the Executive Director of the Master’s in Math Finance program at New York University’s Courant Institute. Peter has won awards from Wilmott Magazine for “Cutting Edge Research’’ and from Risk Magazine for “Quant of the Year’’.

He is renowned for fundamental contributions to statistical mechanics, stochastic analysis and mathematical finance. He has been awarded the Georg Cantor Medal by the German Mathematical Society and an honorary doctorate from the University Paris-Dauphine.

management commitee Professor Roger Goodman Head of the Social Sciences Division (Chair) Professor Guy Houlsby Head of Department of Engineering Science Dr Anthony Ledford Chief Scientist, Man Research Laboratory Professor Jim Malcomson Head of Department of Economics Professor Colin Mayer Dean of Saïd Business School Miss Lucy Mullins Head of Administration, Oxford-Man Institute Professor Brian Ripley Department of Statistics Professor Bill Roscoe Director, Oxford University Computing Laboratory Professor Neil Shephard Director, Oxford-Man Institute Professor Anne Trefethen Director of Oxford eResearch Centre Professor Nick Woodhouse Head of Mathematical Institute

C I N D c o n fere n c e Contemporary Issues and New Directions in Quantitative Finance Inaugural Conference of an Annual Series 9th -11th July 2010 This was the first conference of an Annual Series to be organised by the Institute. The aim of these conferences is to promote current and emerging research directions in Quantitative and Mathematical Finance. The conferences will provide a platform to present new ideas, identify and formalise new problems, debate about the relevance and importance of these new areas and plant the seed for innovative interdisciplinary research collaborations. The exchange of ideas will benefit, on one hand, mathematicians who will get exposed to new modelling issues in economics and finance and, on the other, economists and finance scholars who will learn about mathematical techniques and approaches for the quantitative problems emerging in the new models. The ultimate goal is to create an environment for fruitful and innovative dialogues among mathematicians, economists and finance scholars on new ideas and topics.

Speakers Day 1 José Scheinkman (Princeton) “Bubbles and Trading” Discussion led by Peter Bank (TU Berlin) Gustavo Manso (MIT) “Information Percolation in Segmented Markets” Discussion led by Mihai Sirbu (UT Austin) Day 2 Noah Williams (UW-Madison) “Persistent Private Information” Discussion led by Huyên Pham (Paris VI-VII) Harrison Hong (Princeton) “The Disagreement Approach to Asset Pricing” Discussion led by Chris Rogers (Cambridge)

Every year three themes will be selected. This year the themes were “Information Percolation in Financial Markets”, “Financial Bubbles” and “Principal-Agent Problem and Contract Theory”.

Darrell Duffie (Stanford) “Information Percolation with Equilibrium Search Dynamics” Discussion led by Rene Carmona (Princeton)

Scientific Committee:

Philip Protter (Cornell) “How to Detect an Asset Bubble” Discussion led by Dmitry Kramkov (Carnegie Mellon)

R. Carmona D. Duffie P-L. Lions J. Scheinkman T. Zariphopoulou X.Y. Zhou

Day 3 Jaksa Cvitanic (Caltech) “Contract Theory in Continuous Time: An Overview” Discussion led by Jin Ma (USC) Irene Gamba (UT-Austin) “Kac N-particle Models for Multi-agent Interactions in the Dynamics of Information” Discussion led by Jean-Pierre Fouque (UCSB) Margaret Meyer (Oxford) “Gaming and Strategic Ambiguity in Incentive Provision” Discussion led by Mark Davis (Imperial)

seminar series highlights 20th October 2009, Geert Bekaert (Columbia University) “What Segments Equity Markets?” Joint with Saïd Business School Geert Bekaert gave a talk on world equity market segmentation. He and his co-authors found decreased levels of segmentation in many developing countries, although the level of segmentation is still significant. They identify a country’s political risk profile and its stock market development as two additional local segmentation factors as well as the US corporate credit spread as a global segmentation factor.

27th October 2009, Walter Schachermayer (University of Vienna) “The Fundamental Theorem of Asset Pricing for Continuous Processes under Small Transaction Costs” Walter Schachermayer announced a new proof of the classical Bichteler-Dellacherie theorem and its direct connections to arbitrage. He and his co-authors constructed a novel proof which is based on a discrete-time Doob-Meyer decomposition. He also discussed the connection of these new results to a new characterisation of semi-martingales in terms of a variant of the so-called ‘no free lunch’, a fundamental notion in derivative pricing.

30th October 2010, Mark Davis (Imperial College, London) “Jump-Diffusion Risk-Sensitive Asset Management” Joint with Nomura Centre for Financial Mathematics Mark Davis and his co-author considered a portfolio optimisation problem in which asset prices are represented by SDEs driven by Brownian motion and a Poisson random measure, with drifts that are functions of an auxiliary diffusion ‘factor’ process. The criterion, following earlier work by Bielecki, Pliska, Nagai and others, is risksensitive optimisation (equivalent to maximising the expected growth rate subject to a constraint on variance). By using a change of measure technique introduced by Kuroda and Nagai, Davis and his co-author showed that the problem reduces to solving a certain stochastic control problem in the factor process, which has no jumps. The main result of the paper is that the Hamilton-Jacobi-Bellman equation for this problem has a classical solution. The proof uses Bellman’s “policy improvement” method together with results on linear parabolic PDEs due to Ladyzhenskaya et al.

5th February 2010, Wei Xiong (Princeton University) “Rollover Risk and Credit Risk” Joint with Nomura Centre for Financial Mathematics Wei Xiong and his co-author modelled a firm’s rollover risk generated by conflict of interest between debt and equity holders. Specifically, when the firm faces losses in rolling over its maturing debt, its equity holders are willing to absorb the losses only if the option value of keeping the firm alive justifies the cost of paying off the maturing debt. Their model shows that both deteriorating market liquidity and shorter debt maturity can exacerbate this externality and cause costly firm bankruptcy at higher fundamental thresholds. Xiong discussed the implications of this model on liquidity-spillover effects, the flight-toquality phenomenon, and optimal debt maturity structures.

se m i n ar high l ights

2nd March 2010, Michael Brandt (Duke University) “What Does Equity Sector Orderflow tell us about the Economy?”

25th May 2010, Ivar Ekeland (University of British Columbia) “Portfolio Management under Time Inconsistency”

Joint with Saïd Business School

Joint with Nomura Centre for Financial Mathematics

Michael Brandt gave a talk on using empirical measures of portfolio rebalancing to back out investors’ views, specifically their views about the state of the economy. He and his co-authors show that aggregate portfolio rebalancing across equity sectors is consistent with sector rotation, an investment strategy that exploits perceived differences in the relative performance of sectors at different stages of the business cycle. They find that the empirical foot-print of sector rotation has predictive power for the evolution of the economy, future stock market returns, and future bond market returns, even after controlling for relative sector returns. They conclude that, contrary to many theories of price formation, trading activity contains information that is not entirely revealed by resulting relative price changes.

Ivar Ekeland gave a talk on portfolio management under timeinconsistency. This issue is very important in portfolio choice, for there is strong evidence that individuals discount future utilities at nonconstant rates. The notion of optimality then disappears, because of time inconsistency and rational behaviour, then, centres around equilibrium strategies. Ekeland discussed these issues and proposed a solution approach to a family of investment models with hyperbolic discounting (the discount rate increases with time). He showed how such discounting may explain some well-known puzzles in portfolio management.

11th May 2010, Hélène Rey (London Business School) “Information Asymmetries in Global Institutional Investment” Joint with Saïd Business School Hélène Rey spoke on examining the dynamics of international portfolios with a unique data set on the stock allocations of approximately 6,500 international equity funds domiciled in four different currency areas during a five year period. She and her co-author find strong support for portfolio rebalancing behavior aimed at stabilising exchange rate risk and equity risk exposure around desired levels.

Full seminar listings can be found at www.oxford-man.ox.ac.uk/events

thomas flury Learning and Filtering via Simulation: Smoothly Jittered Particle Filters

inference frameworks such as Relative Maximum Entropy, and provides coherent updates when the Bayesian rule is problematic. We make use of loss functions for selecting posterior probability measures and indeed throughout we emphasise the use of loss functions as fundamental building blocks for inference.

Thomas Flury and Neil Shephard (2009) Abstract A key ingredient of many particle filters is the use of the sampling importance resampling algorithm (SIR), which transforms a sample of weighted draws from a prior distribution into equally weighted draws from a posterior distribution. We give a novel analysis of the SIR algorithm and analyse the jittered generalisation of SIR, showing that existing implementations of jittering lead to marked inferior behaviour over the basic SIR algorithm. We show how jittering can be designed to improve the performance of the SIR algorithm. We illustrate its performance in practice in the context of three filtering problems.

chris holmes Two-Sample Bayesian Nonparametric Hypothesis Testing Chris Holmes, François Caron, Jim Griffin and David Stephens (2009) Abstract In this article we describe Bayesian nonparametric procedures for twosample hypothesis testing. Namely, given two sets of samples iid iid y (1) ~ F (1) and y (2) ~ F (2) , with F (1) , F (2) unknown, we wish to evaluate (2) the evidence for the null hypotheses H0 : F (1) = = F versus the (1) ≠ (2) alternative H1 : F F . Our method is based upon a nonparametric Polya tree prior centered either subjectively or using an empirical procedure. We show that the Polya tree prior leads to an analytic expression for the marginal likelihood under the two hypotheses and hence an explicit measure of the probability of the null Pr (H0 l { y (1), y (2) } ).

General Bayesian Updating Chris Holmes and Stephen Walker (2010) Abstract Bayesian inference provides a coherent strategy for updating belief probabilities, but only under certain conditions; which include the well known problem that observations must be realisations from the prior probability model. Moreover, only information of a specific type can be used to update beliefs; namely observations for which a probability model can be assigned. To address these problems, we propose a general decision theoretic approach for updating belief probability measures. This encompasses traditional Bayesian ethodology, as well as other

arend janssen Arbitrage in Order Driven Markets Arend Janssen (2009) Abstract In this paper we construct a mathematical model of an order driven market where traders can submit limit orders and market orders to buy and sell securities. We adapt the notion of no free lunch of Harrison and Kreps and Jouini and Kallal to our setting and we proof a noarbitrage theorem for the model of the order driven market.

anthony ledford An Alternative Point Process Framework for Modelling Multivariate Extreme Values Alexandra Ramos, Anthony Ledford (2009) Abstract Classical techniques for analysing multivariate extremes can often be framed in terms of the point process representations of de Haan (1985). Amongst other things, this representation provides a characterisation of the limiting distribution of the normalised componentwise maxima of independent and identically distributed unit Fréchet variables, i.e. the class of multivariate extreme value distributions. The dependence structures accommodated within this class correspond only to asymptotic dependence or to exact independence, and so are rather restrictive. In this paper, an alternative limiting point process representation is studied that holds regardless of whether the underlying data generation mechanism is asymptotically dependent or asymptotically independent. Through the use of the usual pseudo-polar coordinates, we characterise the intensity function of this point process in terms of the coefficient of tail dependence h ∈ (0, 1] and a non-negative measure that has to satisfy a simple normalisation condition but is otherwise arbitrary. We use this point process representation to derive an analogue of the standard componentwise maxima result that holds for both asymptotically dependent and asymptotically independent cases. We illustrate our results using a flexible parametric example and provide methods for simulating from both the limiting point process and the limiting componentwise maxima distribution.

w o r k i n g papers jan obłój

Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models

On Azéma-Yor Processes, their Optimal Properties and the Bachelier-Drawdown Equation

Cavit Pakel, Neil Shephard, Kevin Sheppard (2009)

Laurent Carraro, Nicole El Karoui and Jan Obłój (2009)

We investigate the properties of the composite likelihood (CL) method for (T x NT ) GARCH panels. The defining feature of a GARCH panel with time series length T is that, while nuisance parameters are allowed to vary across NT series, other parameters of interest are assumed to be common. CL pools information across the panel instead of using information available in a single series only. Simulations and empirical analysis illustrate that in reasonably large T CL performs well. However, due to the estimation error introduced through nuisance parameter estimation, CL is subject to the “incidental parameter” problem for small T .

Abstract We study the class of Azéma–Yor processes defined from a general semimartingale with a continuous running supremum process. We show that they arise as unique strong solutions of the Bachelier stochastic differential equation which we prove is equivalent to the Drawdown equation. Solutions of the latter have the drawdown property: they always stay above a given function of their past supremum. We then show that any process which satisfies the drawdown property is in fact an Azéma–Yor process. The proofs exploit group structure of the set of Azéma–Yor processes, indexed by functions, which we introduce. Secondly we study in detail Azéma–Yor martingales defined from a non-negative local martingale converging to zero at infinity. We establish relations between Average Value at Risk, Drawdown function, Hardy-Littlewood transform and its generalised inverse. In particular, we construct Azéma–Yor martingales with a given terminal law and this allows us to rediscover the Azéma–Yor solution to the Skorokhod embedding problem. Finally, we characterise Azéma–Yor martingales showing they are optimal relative to the concave ordering of terminal variables among martingales whose maximum dominates stochastically a given benchmark.

neil shephard Learning and Filtering via Simulation: Smoothly Jittered Particle Filters Thomas Flury and Neil Shephard (2009) Abstract A key ingredient of many particle filters is the use of the sampling importance resampling algorithm (SIR), which transforms a sample of weighted draws from a prior distribution into equally weighted draws from a posterior distribution. We give a novel analysis of the SIR algorithm and analyse the jittered generalisation of SIR, showing that existing implementations of jittering lead to marked inferior behaviour over the basic SIR algorithm. We show how jittering can be designed to improve the performance of the SIR algorithm. We illustrate its performance in practice in the context of three filtering problems.

Abstract

kevin sheppard Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models Cavit Pakel, Neil Shephard, Kevin Sheppard (2009) Abstract We investigate the properties of the composite likelihood (CL) method for (T x NT ) GARCH panels. The defining feature of a GARCH panel with time series length T is that, while nuisance parameters are allowed to vary across NT series, other parameters of interest are assumed to be common. CL pools information across the panel instead of using information available in a single series only. Simulations and empirical analysis illustrate that in reasonably large T CL performs well. However, due to the estimation error introduced through nuisance parameter estimation, CL is subject to the “incidental parameter” problem for small T .

john armour Armour, J., Black, B., Cheffins, B. R. and Nolan, R. C., 2010. Private Enforcement of Corporate Law: An Empirical Comparison of the UK and US. Journal of Empirical Legal Studies, 6, pp. 687-722. Armour, J., Deakin, J., Mollica, V. and Siems, M., 2010. Law and Financial Development: What we are Learning from Time Series Evidence. Brigham Young University Law Review, pp. 1435-1500. Armour, J., 2009. Enforcement Strategies in UK Corporate Governance: A Roadmap and Empirical Assessment. In Armour, J. and Payne, J. eds. Rationality in Company Law: Essays in Honour of D D Prentice. Oxford: Hart Publishing, pp. 71-119. Armour, J., 2009. What has the Financial Crisis Taught us About Insolvency Law? In Essers, P., Raaijmakers, G., van der Sangen, G., Verdam, A. and Vermeulen, E., Met Recht: Liber Amicorum for Theo Raaijmakers. Deventer: Kluwer, pp. 1-9. Armour, J., Deakin, S., Lele, P. and Siems, M., 2009. How Do Legal Rules Evolve? Evidence from a Cross-Country Comparison of Shareholder, Creditor and Worker Protection. American Journal of Comparative Law, 57, pp. 579-629. Winner, European Corporate Governance Institute Prize for ‘Best Law Working Paper 2009’. Armour, J., Deakin, S., Sarkar, P., Siems, M. and Singh, A., 2009. Shareholder Protection and Stock Market Development: A Test of the Legal Origins Hypothesis. Journal of Empirical Legal Studies, 6, pp. 343-381. Winner, European Corporate Governance Institute Prize for ‘Best Law Working Paper 2008’ for working paper version, ECGI Law WP108/2008. Armour, J. and Lele, P., 2009. Law, Finance, and Politics: The Case of India. Law and Society Review, 43, pp. 491-526. Armour, J. and Payne, J. eds., 2009. Rationality in Company Law: Essays in Honour of D D Prentice. Oxford: Hart Publishing. Kraakman, R., Armour, J., Davies, P. L., Enriques, L., Hansmann, H. B., Hertig, G., Hopt, K. J., Kanda H. and Rock, E. B., 2009. The Anatomy of Corporate Law, 2nd ed, Oxford and New York: OUP.

andrea calì Calì, A. and Martinenghi, D., 2010. Querying Incomplete Data over Extended ER Schemata. Theory and Practice of Logic Programming,10(3), pp. 291-329. Calì, A. and Martinenghi, D., 2010 Querying the Deep Web. In EDBT, 13th International Conference on Extending Database Technology. Lausanne, Switzerland, 22nd – 26th March 2010. Switzerland: EDBT, pp. 724-727. Calì, A., Calvanese, D. and Martinenghi, D., 2009. Dynamic Query Optimization under Access Limitations and Dependencies. Journal of Universal Computer Science, 15, pp.33-62. Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. A General Datalog-Based Framework for Tractable Query Answering over Ontologies. In ACM, 28th Symposium on Principles of Database Systems 2009, Providence, Rhode Island, USA, 29th June-1st July 2009. New York: ACM Press, pp. 77-86. Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In ACM, Invited paper in the 12th International Conference on Database Theory 2009. St. Petersburg, Russia, 23rd-25th March 2009. New York: ACM Press, pp. 14-30.

Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In SEBD, 11th Italian Symposium on Advanced Database Systems. Camogli, Italy, 21st-24th June 2009. Genova: SEBD. Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Tractable Query Answering over Ontologies with Datalog±. In CEUR Proceedings Volume 477, 22nd International Workshop on Description Logics 2009, Oxford, UK, 27th-30th July 2009. CEUR Electronic Proceedings Vol. 477. Aachen, Germany: CEUR. Calì, A., Lukasiewicz, T., Predoiu, L. and Stuckenschmidt, H., 2009. Tightly Coupled Probabilistic Description Logic Programs for the Semantic Web. Journal on Data Semantics, 12, pp. 95-130. Calì, A. and Torlone, R., 2009. Checking Containment of Schema Mappings (Preliminary Report). In: CEUR Proceedings Volume 450, 3rd Alberto Mendelzon International Workshop on Foundations of Data Management. Arequipa, Peru, 12th -15th May 2009. Aachen, Germany: CEUR.

thomas cass Cass, T. and Friz, P., 2010. Densities for Rough Differential Equations under Hörmander’s Condition, Annals of Mathematics, 171, pp. 2115-2141. Cass, T., Friz, P. and Victoir, N., 2009. Non-Degeneracy of Wiener Functionals arising from Rough Differential Equations, Transactions of the American Mathematical Society, 361 (6), pp. 3359-3371.

thomas flury Shephard, N. and Flury, T., 2010. Bayesian Inference based only on Simulated Likelihood: Particle Filter Analysis of Dynamic Economic Models. Econometric Theory. Forthcoming.

christo fogelberg Fogelberg, C. and Palade, V., 2009. Evaluating Clustering Algorithms for Genetic Regulatory Network Structural Inference. In Research and Development in Intelligent Systems XXVI, AI-2009 Proceedings. Cambridge, UK, 15th-17th December 2009. London: Springer-Verlag. Fogelberg, C., Palade, V., 2009. Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap. In: Abraham, A. et al eds. Foundations of Computational Intelligence, Berlin: Springer-Verlag, pp. 3-34.

mike giles Giles, M., 2010. Crank-Nicolson Time-Marching. Encyclopedia of Quantitative Finance. UK: John Wiley and Sons. Lee, A., Yau, C., Giles, M., Doucet, A., Holmes, C., 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods. Journal of Computational and Graphical Statistics. Forthcoming. Giles, M., 2009. Multilevel Monte Carlo for Basket Options. Winter Simulation Conference 2009. Austin, Texas, 13th – 16th December 2009. USA: WSC. Giles, M., 2009. ‘Vibrato’ Monte Carlo Method. In Monte Carlo and QuasiMonte Carlo Methods 2008. Springer, 2009, pp. 369-382. Giles, M. and Waterhouse, B.J., 2009. Multilevel Quasi-Monte Carlo Path Simulation, In Albrecher, H., Runggaldier, W. And Schachermayer, W. Eds Advanced Financial Modelling: Volume 8 of Radon Series on Computational and Applied Mathematics. Berlin: Walter de Gruyter, pp.165-181.

pub l i c ati o n s georg gottlob Calì, A., Gottlob, G. and Lukasiewicz, T., 2010. Tractable Query Answering over Ontologies with Datalog±. Description Logics. Forthcoming. Abiteboul, S., Gottlob, G., and Manna, M., 2009. Distributed XML Design. In ACM, 28th Symposium on Principles of Database Systems 2009, Providence, Rhode Island, USA, 29th June-1st July 2009. New York: ACM Press, pp.247-258. Baumgartner, R., Gottlob, G., Herzog, M., 2009. Web Data Extraction for Online Market Intelligence. In VLDB, 35th International Conference on Very Large Databases, Lyon, France, 24th – 28th August 2009. France: VLDB. Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. A General Datalog-Based Framework for Tractable Query Answering over Ontologies. In ACM, 28th Symposium on Principles of Database Systems 2009, Providence, Rhode Island, USA, 29th June-1st July 2009. New York: ACM Press, pp. 77-86. Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Datalog±: A Powerful Family of Languages for Query Answering over Ontologies. In ACM, 12th International Conference on Database Theory 2009. St. Petersburg, Russia, 23rd-25th March 2009. New York: ACM Press. Calì, A., Gottlob, G. and Lukasiewicz, T., 2009. Datalog±: A Unified Approach to Ontologies and Integrity Constraints. In ICDT, 12th International Conference, St. Petersburg, Russia, March 23rd -25th 2009. Russia: ICDT, pp. 14-30. Campi, A., Gottlob, G., Hoye, B., 2009. Wormholes of Communication: Interfacing Virtual Worlds and the Real World. In IEEE, 23rd International Conference on Advanced Information Networking and Applications, Bradford, UK, 26th-29th May 2009. UK: IEEE. Gottlob, G., Greco, G. and Francesco Scarcello, 2009. Tractable Optimization Problems through Hypergraph-Based Structural Restrictions. In ICALP, 36th Internatioanl Colloquium: Automata, Languages and Programming, Rhodes, Greece, 5th-12th July 2009. Berlin: Springer, pp. 16-30. Gottlob, G., Miklós, Z., and Schwentick, T., 2009. Generalized Hypertree Decompositions: NP-Hardness and Tractable Variants. Journal of the ACM, 56 (6). Gottlob, G., Pichler, R., and Savenkov, V., 2009. Normalization and Optimization of Schema Mappings. In VLDB, 35th International Conference on Very Large Databases, Lyon, France, 24th – 28th August 2009. France: VLDB. Gottlob, G., Tien Lee, S. and Valiant, G., 2009. Size and Treewidth Bounds For Conjunctive Queries. In ACM, 28th Symposium on Principles of Database Systems 2009, Providence, Rhode Island, USA, 29th June-1st July 2009. New York: ACM Press, pp. 45-54.

lajos gergely gyurkó

ben hambly Aleksandrov, N. and Hambly, B., 2010. A Dual Approach to Multiple Exercise Option Problems under Constraints. Mathematical Methods in Operations Research, 71, pp. 503-533. Hambly, B. and Kumagai, T., 2010. Diffusion on the Scaling Limit of the Critical Percolation Cluster in the Diamond Hierarchical Lattice. Communications in Mathematical Physics, 295, pp. 29-69. Hambly, B. and Lyons, T., 2010. Uniqueness for the Signature of a Path of Bounded Variation and the Reduced Path Group. Annals of Mathematics, 171, pp. 109-167. Hambly, B., 2010. Asymptotics for Functions Associated with Heat Flow on the Sierpinski Carpet. Canadian Journal of Mathematics. Forthcoming. Barlow, M. and Hambly, B., 2009. Parabolic Harnack Inequality and Local Limit Theorem for Percolation Clusters. Electronic Journal of Probability, 14, pp. 1-26. Hambly, B., Howison, S. and Kluge, T., 2009. Modelling Spikes and Pricing Swing Options in Electricity Markets. Quantitative Finance, 9, pp. 937-949.

vicky henderson Henderson. V., 2010, Is Corporate Control Effective When Managers Face Investment Timing Decisions in Incomplete Markets? Journal of Economic Dynamics and Control, 34 (6), pp. 1062-1076. Henderson, V. and Hobson, D., 2010. Optimal Liquidation of Derivative Portfolios. Mathematical Finance. Forthcoming. Grasselli, M. and Henderson, V., 2009. Risk Aversion and Block Exercise of Executive Stock Options. Journal of Economic Dynamics and Control, 33, pp.109-127. Henderson, V. and Hobson, D., 2009. Utility Indifference Pricing - An Overview. In R. Carmona, ed. Indifference Pricing: Theory and Applications, Princeton: Princeton University Press, pp.44-74.

chris holmes Lee, A. and Holmes, C., 2010. Discussion of Particle Markov Chain Monte Carlo Methods by C. Andrieu, A. Doucet and R. Holenstein. Journal of the Royal Statistical Society: Series B, 72 (3), pp. 269-342. Lee, A., Yau, C., Giles, M., Doucet, A. and Holmes, C., 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods. Journal of Computational and Graphical Statistics. Forthcoming.

Gyurkó, L.G. and Lyons, T.J., 2010. Rough Paths Based Numerical Algorithms in Computational Finance. Mathematics in Finance, AMS, Contemporary Mathematics .

Yau, C., Papaspiliopoulos, O., Roberts, G.O., Holmes, C., 2010. Bayesian Nonparametric Hidden Markov Models. Journal of the Royal Statistical Society: Series B. Forthcoming.

Gyurkó, L.G. and Lyons, T.J., 2010. Efficient and Practical Implementations of Cubature on Wiener Space. Stochastic Analysis 2010, London: Springer. Forthcoming.

Anjum, S., Doucet, A. and Holmes C., 2009. A Boosting Approach to Structure Learning of Graphs with and without Prior Knowledge. Bioinformatics 25, pp. 2929-2936. Antonyuk, A. and Holmes, C . 2009. On Testing for Genetic Association in Case-Control Studies when Population Allele Frequencies are Known. Genetic Epidemiology. 33, pp. 371-378.

Holmes, C and Jasra, A., 2009. Antithetic Methods for Gibbs Sampling. Journal of Computational and Graphical Statistics. 18, pp. 401-414.

Staniczenko, P., Lewis, O., Jones, N. and Reed-Tsochas, F., 2010. Structural Dynamics and Robustness of Food Webs. Ecology Letters, 13, pp.891-899.

Klingelhoefer, J., Moutsianas, L. and Holmes, C., 2009. Approximate Bayesian Feature Selection on a Large Meta-Dataset Offers Novel Insights on Factors that Effect siRNA Potency. Bioinformatics, 25, pp. 1594-1601.

Fenn, D. J., Porter, M. A., McDonald, M., Williams, S., Johnson, N. F. and Jones, N. S., 2009. Dynamic Communities in Multichannel Data. Chaos, 19.

Lemieux, J., Gomez-Escobar, N., Feller, A., Carret, C., Amambua-Ngwa , A., Pinches, R., Day, F., Kyes, S., Conway, D., Holmes, C. and Newbold, C., 2009. Statistical Estimation of Cell-Cycle Progression and Lineage Commitment in ‘Plasmodium Falciparum’ Reveals a Homogeneous Pattern of Transcription in ‘ex vivo’ Culture. National Academy of Sciences, 5th May 2009, 106 (18), pp.7559-7564. Webb, A., Hancock, J. and Holmes, C . 2009. Phylogenetic Inference under Recombination using Bayesian Stochastic Topology Selection. Bioinformatics, 25, pp. 197-203.

sam howison Cartea, A. and Howison, S., 2009. Option Pricing with Levy-Stable Processes Generated by Levy-Stable Integrated Variance. Quantitative Finance, 9, pp.397-409. Coulon, M. and Howison, S., 2009. Stochastic Behaviour of the Electricity Bid Stack: From Fundamental Drivers to Power Prices. Journal of Energy Markets, 2, pp. 29-69.

Smith, D., Onnela, J. and Jones, N., 2009. Master-Equation Analysis of Accelerating Networks. Physical Review E, 79. Staniczenkko, P., Lee, C. and Jones, N. S., 2009. Rapidly Detecting Disorder in Rhythmic Biological Signals. Physical Review E, 79.

jeremy large Large, J., 2010. Estimating Quadratic Variation when Quoted Prices Change by a Constant Increment. Journal of Econometrics. Forthcoming. Large, J., 2009. A Market-Clearing Role for Inefficiency on a Limit Order Book. Journal of Financial Economics, 91, pp.102-117.

ada lau Lau, A. and McSharry, P., 2010. Approaches for Multi-Step Density Forecasts with Application to Aggregated Wind Power. Annals of Applied Statistics. Forthcoming.

anthony lee

Fenn, D., Howison, S., Johnson, N.F. and Williams, S., 2009. The Mirage of Triangular Arbitrage in the Spot Foreign Exchange Market. International Journal of Theoretical Applied Finance. 12, pp. 1105-1123.

Lee, A. and Holmes, C., 2010. Discussion of Particle Markov Chain Monte Carlo Methods by C. Andrieu, A. Doucet and R. Holenstein. Journal of the Royal Statistical Society: Series B, 72 (3), pp. 269-342.

Hambly, B., Howison, S. and Kluge, T., 2009. Modelling Spikes and Pricing Swing Options in Electricity Markets. Quantitative Finance, 9, pp. 937-949.

Lee, A., Yau, C., Giles, M., Doucet, A., Holmes, C., 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods. Journal of Computational and Graphical Statistics. Forthcoming.

tim jenkinson Jenkinson, T.J., 2009. Private Equity. In the EEAG Report on the European Economy.

anthony ledford

Jenkinson, T.J. and Jones, H., 2009. Competitive IPOs. European Financial Management. 15 (4), pp. 733-756

Ramos, A. and Ledford, A., 2010. An Alternative Point Process Framework for Modelling Multivariate Extreme Values. Communications in Statistics – Theory and Methods. Forthcoming.

Jenkinson, T.J. and Jones, H., 2009. IPO Pricing and Allocation: a Survey of the Views of Institutional Investors. The Review of Financial Studies, 22, pp.1477-1504.

Ramos, A. and Ledford, A., 2009. A New Class of Models for Bivariate Joint Tails. Journal of the Royal Statistical Society: Series B, 71, Part 1, pp.219-241.

nick jones Agarwal, S., Deane, C., Porter, M. and Jones N., 2010. Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks. PLoS Computational Biology, 6 (6). Lewis, A., Jones, N., Porter, M. and Deane, C., 2010. The Function of Communities in Protein Interaction Networks at Multiple Scales. BMC Systems Biology, 4. Little, M. and Jones, N., 2010. Sparse Bayesian Step-Filtering for HighThroughput Analysis of Molecular Machine Dynamics. In IEEE, ICASSP 2010. Dallas, Texas, 14th-19th March 2010. USA: IEEE.

gechun liang Liang, G., Lin, J., Wu, S. and Zheng, H., 2010. The Valuation of the Basket CDSs in a Primary-Subsidiary Model. Asia Pacific Journal of Operational Research. Forthcoming

christian litterer Litterer, C. and Lyons, T., 2010. Introducing Cubature to Filtering. In D. Crisan and B. Rozovsky, eds. Oxford Handbook of Non-Linear Filtering. Oxford: Oxford University Press. Forthcoming.

pub l i c ati o n s asger lunde Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., 2010. Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading. Journal of Econometrics. Forthcoming. Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., 2010. Subsampling Realised Kernels. Journal of Econometrics. Forthcoming. Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N.S., 2009. Realised Kernels in Practice: Trades and Quotes. Econometrics Journal. 2009, 12, 3, pp.C1-C32. Lunde, A. and Zebedee, A., 2009. Intraday Volatility Responses to Monetary Policy Events. Financial Markets and Portfolio Management, 23, pp. 383-399

terry lyons Hambly, B. and Lyons, T., 2010. Uniqueness for the Signature of a Path of Bounded Variation and the Reduced Path Group. Annals of Mathematics, 171, pp. 109-167. Litterer, C. and Lyons, T., 2010. Introducing Cubature to Filtering. In D. Crisan and B. Rozovsky, eds. Oxford Handbook of Non-Linear Filtering. Oxford: Oxford University Press. Forthcoming. Qian, Z., Liang, G. and Lyons, T., 2010. Backward Stochastic Dynamics on a Filtered Probability Space. The Annals of Probability. Forthcoming.

robert may May, R and Arinaminpathy, N., 2010. Systemic Risk: the Dynamics of Model Banking Ecosystems. Journal of the Royal Society: Interface, 6, pp. 823-838.

michael monoyios Danilova, A, Monoyios, M and Ng, A. 2010. Optimal Investment with inside Information Parameter Uncertainty. Mathematics and Financial Economics, 3, pp. 13-38. Monoyios, M., 2009. Optimal Investment and Hedging Under Partial and Inside Information. H Albrecher, W Runggaldier and W Schachermayer, eds. Advanced Financial Modelling, Berlin: Walter de Gruyter.

per mykland Lin, M., Mykland, P.A. and Chen, R., 2010. On Generating Monte Carlo Sample of Continuous Diffusion Bridges. Journal of the American Statistical Association, 105, pp. 820-838. Ait-Sahalia, Y., Mykland, P. and Zhang, L., 2010. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise. Journal of Econometrics. Forthcoming. Mykland, P., 2010. A Gaussian Calculus for Inference from High Frequency Data. Annals of Finance. Forthcoming. Mykland, P. and Zhang, L., 2010. The Econometrics of High Frequency Data. In M. Kessler, A. Lindner and M. Sorensen eds. Statistical Methods for Stochastic Differential Equations. London: Chapman and Hall. Forthcoming.

Zhang, L., Mykland, P.A. and Ait-Sahalia, Y., 2010. Edgeworth Expansions for Realized Volatility and Related Estimators. Journal of Econometrics. Forthcoming. Ait-Sahalia, Y. and Mykland, P.A., 2009. Estimating Volatility in the Presence of Market Microstructure Noise: A Review of the Theory and Practical Considerations. In T. Andersen, R. Davis, J.P. Kreiss, and T.H. Mikosch, eds., Handbook of Financial Time Series. Berlin: Springer-Verlag, pp. 577-598. Jacod, J., Li, Y., Mykland, P.A., Podolskij, M. and Vetter, M., 2009. Microstructure Noise in the Continuous Case: The Pre-Averaging Approach. Stochastic Processes and Applications, 119, pp. 2249-2276. Mykland, P.A., 2009. Options Pricing Bounds and Statistical Uncertainty. In Y. Ait-Sahalia and L.P. Hansen, eds. Handbook of Financial Econometrics. Oxford and Amsterdam: North-Holland, pp. 135-195. Mykland, P.A. and Zhang, L., 2009. Inference for Continuous Semimartingales Observed at High Frequency. Econometrica, 77 (5), pp.1403-1445.

sergey nadtochiy Carmona, R. and Nadtochiy, S., 2010. Tangent Levy Market Models. Finance and Stochastics. Forthcoming. Carmona, R. and Nadtochiy, S., 2010. Tangent Models as a Mathematical Framework for Dynamic Calibration, IJTAF. Forthcoming. Carmona, R. and Nadtochiy, S., 2009. Local Volatility Dynamic Models. Finance and Stochastics, 13, pp. 1-48.

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jan obłój Obłój, J., 2010.The Skorokhod Embedding Problem and its Applications in Mathematical Finance. In Cont, R. ed., Encyclopedia of Quantitative Finance. UK: John Wiley and Sons, pp. 1653-1657. Obłój, J. and Cox, A.M.G., 2010. Robust Hedging of Double No-Touch Barrier Options. Finance and Stochastics. Forthcoming. Obłój, J., Cox, A.M.G. and Hobson, D., 2010. Time-Homogeneous Diffusions with a Given Marginal at a Random Time. ESAIM Probability and Statistics (Special Volume in Honour of Marc Yor). Forthcoming. Obłój, J. and Pistorius, M., 2009. On an Explicit Skorokhod Embedding for Spectrally Negative Lévy Processes. Journal of Theoretical Probability, 22 (2), pp. 418-440.

cavit pakel Pakel, C., Shephard N. and Sheppard K., 2010. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models. Statistica Sinica. Forthcoming.

andrew patton Patton, A. and Timmermann, A., 2010. Generalized Forecast Errors, a Change of Measure, and Forecast Optimality. In T. Bollerslev, J.R. Russell and M.W. Watson, eds. Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle, Oxford: Oxford University Press.

wolf-georg ringe Ringe, W.G., 2010. Company Law and Free Movement of Capital, Cambridge Law Journal, 69, pp.378-409.

Patton, A., 2010. Volatility Forecast Comparison using Imperfect Volatility Proxies. Journal of Econometrics. Forthcoming.

Ringe, W.G., 2010. Sparking Regulatory Competition in European Company Law - The Impact of the Centros Line of Case-Law and its Concept of ‘Abuse of Law’. In R. de la Feriaand and S. Vogenauer eds. Prohibition of Abuse of Law - A New General Principle of EU Law. Oxford: Hart Publishing. Forthcoming.

Patton, A. and Timmerman, A., 2010. Monotonicity in Asset Returns: New Tests with Applications to the Term Structure, the CAPM and Portfolio Sorts. Journal of Financial Economics. Forthcoming.

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Patton, A., and Timmermann, A., 2010, Predictability of Output Growth and Inflation: A Multi-Horizon Survey Approach. Journal of Business and Economic Statistics. Forthcoming. Patton, A., and Timmermann, A., 2010, Why do Forecasters Disagree? Lessons from the Term Structure of Cross-Sectional Dispersion. Journal of Monetary Economics. Forthcoming. Patton, A., 2009. Are ‘Market Neutral’ Hedge Funds Really Market Neutral? Review of Financial Studies, 22, pp. 2495-2530. Patton, A., 2009. Copula-Based Models for Financial Time Series. In T.G. Andersen, R.A. Davis, J.P. Kreiss and T. Mikosch, eds. Handbook of Financial Time Series. Berlin: Springer-Verlag, pp. 767-785. Patton, A. and Sheppard, K., 2009. Evaluating Volatility and Correlation Forecasts. In T.G. Andersen, R.A. Davis, J.-P. Kreiss and T. Mikosch, eds. Handbook of Financial Time Series. Berlin: Springer-Verlag, pp. 801-838. Patton, A. and Sheppard, K., 2009. Optimal Combinations of Realised Volatility Estimators. International Journal of Forecasting, 25 (2), pp. 218-238.

zhongmin qian Qian, Z. and Chen, G. Q., 2010. A study of the Navier-Stokes Equations with the Kinematic and Navier Boundary Conditions. Indiana University Mathematics Journal. Forthcoming. Qian, Z., Liang, G. and Lyons, T., 2010. Backward Stochastic Dynamics on a Filtered Probability Space. The Annals of Probability. Forthcoming. Qian, Z., 2009. An Estimate for the Vorticity of the Navier-Stokes Equation. Comptes Rendus Mathematique, 347, pp.89-92. Qian, Z., 2009. Ricci Flow on a 3-Manifold with Positive Scalar Curvature. Bulletin de la Société Mathématique de France, 133, pp. 145-168. Qian, Z., Chen, G. Q. and Osborn, D., 2009. The Navier-Stokes Equations with the Kinematic and Vorticity Boundary Conditions on Non-Linear Boundaries. Acta Mathematica Scientia, 29 (4), pp.919-948.

tarun ramadorai Ramadorai, T., 2010. Institutional Investors, in H. Kent Baker and J. Nofsinger eds. Behavioral Finance: Investors, Corporations, and Markets. Hoboken, NJ: John Wiley and Sons. Forthcoming. Ramadorai, T., 2010. The Secondary Market for Hedge Funds and the Closed Hedge Fund Premium. Journal of Finance. Forthcoming.

stephen roberts Garnett, R., Osborne, M., Reece, S., Rogers, A. and Roberts, S., 2010. Sequential Bayesian Prediction in the Presence of Changepoints and Faults. The Computer Journal. Garnett, R., Osborne, M. and Roberts, S., 2010. Bayesian Optimization for Sensor Set Selection, In IPSN 2010, 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, Stockholm, Sweden, 12th16th April 2010. New York: ACM Press. Kaufman, M., and Roberts, S., 2010. Coordination vs. Information in Multiagent Decision Processes. In AAMAS 2010, 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto Canada 10th-14th May 2010. Canada: AAMAS. Lowne, D., Roberts, S. and Garnett, R., 2010. Sequential Non-stationary Dynamic Classification with Sparse Feedback. Pattern Recognition, 43 (3), pp. 897-905. McInerney, R., Roberts, S. and Rezek, I., 2010. Sequential Bayesian Decision Making for Multi-armed Bandit. In AAMAS 2010, 9th International Conference on Autonomous Agents and Multiagent Systems, Toronto Canada 10th-14th May 2010. Canada: AAMAS. Osborne, M., Garnett, R. and Roberts, S., 2010. Active Data Selection for Sensor Networks with Faults and Changepoints. In IEEE, 24th International Conference on Advanced Information Networking and Applications, Perth, Australia, 20th-23rd April 2010. Australia: IEEE. Reece, S. and Roberts, S., 2010. An Introduction to Gaussian Processes for the Kalman Filter Expert. In Fusion 2010, 13th International Conference on Information Fusion, Edinburgh, UK, 26th-29th July 2010. UK: Fusion. Reece, S. and Roberts, S., 2010. The Near Constant Acceleration Gaussian Process Kernel for Tracking. IEEE Signal Processing Letters, 17 (8), pp. 707-710. Yoon, J., and Roberts, S., 2010. Robust Measurement Validation in Target Tracking using Geometric Structure. IEEE Signal Processing Letters, 17 (5), pp. 493-496. Lee, S. and Roberts, S. 2010. Sequential Dynamic Classification Using Latent Variable Models. The Computer Journal 2010. Forthcoming. Mann, R., Freeman, R., Osborne, M., Garnett, R., Meade, R., Armstrong, C., Biro, D., Guilford, T, and Roberts, S., 2010. Gaussian Processes for Prediction of Homing Pigeon Flight Trajectories. In AIP 29th Bayesian Inference and Maximum Entropy Methods in Science and Engineering Oxford, USA 5th-10th July 2009. Forthcoming.

pub l i c ati o n s Ebden, M., Stranjak, A. and Roberts, S. 2009. Visualizing Uncertainty in Reliability Functions with Application to Aero Engine Overhaul. Journal of the Royal Statistical Society C, 59 (1), pp. 163-173. Garnett, R.,Osborne, M. and Roberts, S., 2009. Sequential Bayesian Prediction in the Presence of Changepoints. In 26th International Conference on Machine Learning, Montreal, Canada, 14th-18th June 2009. ACM International Conference Proceeding Series, 382. New York: ACM Press. Osborne, M., Garnett, R. and Roberts, S., 2009. Gaussian processes for Global Optimization. In 3rd International Conference on Learning and Intelligent Optimization, Trento, Italy 18th-22nd January 2009. Reece, S., Roberts, S., Claxton, C., and Nicholson, D., 2009. Multi-Sensor Fault Recovery in the Presence of known and unknown Fault Types. In Fusion 2009, 12th International Conference on Information Fusion, Seattle, Washington, 6th-9th July 2009. USA: Fusion. Tsui, C., Gan, J. Q., and Roberts, S., 2009. A Self-paced Brain-Computer Interface for Controlling a Robot Simulator: An Online Event Labelling Paradigm and an Extended Kalman Filter Based Algorithm for Online Training. Medical and Biological Engineering and Computing, 47 (3), pp. 257-265. Yoon, J., Roberts, S., Dyson, M., and Gan, J., 2009. Adaptive Classification for Brain Computer Interface Systems using Sequential Monte Carlo Sampling. Neural Networks: Special Issue on Brain Machine Interfaces, 22, pp. 1286-1294.

andrás salamon Cooper, M. C., Jeavons, P. G. and Salamon, A. Z., 2010. Generalizing Constraint Satisfaction on Trees: Hybrid Tractability and Variable Elimination. Artificial Intelligence, 174 (9-10), pp. 570-584.

torsten schöneborn Schöneborn, T., 2010. A guided tour of new results on Trade Execution in Illiquid Markets. Blaetter der DGVFM, 31 (1), pp. 79-90. Schied, A., Schöneborn, T. and Tehranchi, M., 2010. Optimal Basket Liquidation for CARA Investors is Deterministic. Applied Mathematical Finance. Forthcoming. Schied, A. and Schöneborn, T., 2009. Risk Aversion and the Dynamics of Optimal Liquidation Strategies in Illiquid Markets. Finance and Stochastics, 13 (2), pp. 181-204.

neil shephard Barndorff-Nielsen, O.E., Kinnebrock, S. and Shephard, N., 2010. Measuring Downside Risk - Realised Semivariance. In T. Bollerslev, J. Russell and M. Watson eds., Edited Volume in honour of Robert F. Engle. Oxford: Oxford University Press, pp. 117-136.. Shephard, N., 2010. Deferred Fees for Universities, Economic Affairs, 30 (2), pp. 40-44. Shephard, N., 2010. Deferred Fees for Universities. Submission to 2nd Round of the Lord Browne Review on Higher Education Funding and Student Finance. Shephard, N., 2010. Modelling and Measuring Volatility. Encyclopedia of Quantitative Finance. UK: John Wiley and Sons.

Shephard, N., 2010. Submission to Higher Education Funding and Student Finance, 1st Round of the Lord Browne Review. Shephard, N. and Sheppard, K., 2010. Realising the Future: Forecasting with High Frequency based Volatility (HEAVY) models. Journal of Applied Econometrics, 25 (2), pp. 197-231. Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., 2010. Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading. Journal of Econometrics. Forthcoming. Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., 2010. Subsampling Realised Kernels. Journal of Econometrics. Forthcoming. Pakel, C., Shephard N. and Sheppard K., 2010. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models. Statistica Sinica. Forthcoming. Shephard, N. and Flury, T., 2010. Bayesian Inference based only on Simulated Likelihood: Particle Filter Analysis of Dynamic Economic Models. Econometric Theory. Forthcoming. Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N.S., 2009. Realised Kernels in Practice: Trades and Quotes. Econometrics Journal, 12 (3), pp. C1-C32. Castle, J.L. and Shephard, N., eds., 2009. The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry. Oxford University Press. Koopman, S.J., Shephard, N. and Creal, D., 2009. Testing the Assumptions behind Importance Sampling. Journal of Econometrics, 149, pp.2-11. Shephard, N. and Andersen, T.G., 2009. Stochastic Volatility: Origins and Overview. In T.G. Andersen, R.A. Davis, J.-P. Kreiss and T. Mikosch, eds. Handbook of Financial Time Series, Springer, pp. 233-254.

kevin sheppard Pakel, C., Shephard N. and Sheppard K., 2010. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models. Statistica Sinica. Forthcoming. Patton, A. and Sheppard, K., 2009. Evaluating Volatility and Correlation Forecasts. In T.G. Andersen, R.A. Davis, J.-P. Kreiss and T. Mikosch, eds. Handbook of Financial Time Series, Berlin: Springer Verlag, pp. 801-838. Patton, A. and Sheppard, K., 2009. Optimal Combinations of Realised Volatility Estimators. International Journal of Forecasting, 25 (2), pp. 218-238.

pierre tarrès Benaïm, M. and Tarrès, P., 2010. Dynamics of Vertex-Reinforced Random Walks. Annals of Probability. Forthcoming.

mungo wilson Wilson, M. and Pollet, J., 2010. Average Correlation and Stock Market Returns. Journal of Financial Economics. Forthcoming.

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Man Group plc Man is a world-leading alternative investment management business that is listed in the FTSE 100 Index (EMG). With a broad range of funds for institutional and private investors globally, we are known for performance, innovative product design and investor service. Our strategy is to offer a broad range of robust alternative investment products to private investors and institutions worldwide. Strong longterm performance and our 20+ year track record are key to attracting and retaining these investors. We serve two principle markets: Private investors and Institutions Our global scale differentiates our business, and our key areas of expertise include people, information technology and risk management. Our investment management expertise extends from single managers such as Man AHL to fund of funds managers such as Man Multi-Manager.

About AHL

AHL Oxford, MRL

AHL is a world-leading quantitative investment manager with an extensive

The combined MRL-OMI working environment in Oxford has been

history of performance and innovation. A pioneer in the application of

purpose-designed to encourage frequent interaction between the two

systematic trading, we have been serving institutional and private clients

groups of researchers. AHL and the University aim to create a stimulating

since 1987 and currently manage funds of over USD 20 billion*.

environment of research and innovation where ideas flourish and

Man provides AHL with centralised product structuring, distribution, client service and operational support. This allows AHL to focus

practitioners from a wide spectrum of disciplines can bring their skills into collaboration and learn from each other.

exclusively on research and trading model development. This unique

Although AHL Oxford and the Institute have independent aims and

position gives AHL far greater transparency and regulatory oversight

separate research programs, the regular contact and dialogue between

than privately owned managers.

them has significantly benefitted both parties. “The interaction between

AHL is based in London, Oxford and Hong Kong. Our Oxford office is home to the Man Research Laboratory (MRL) which is co-located with the Oxford-Man Institute of Quantitative Finance (OMI) – our unique collaboration with Oxford University.

our Research Lab and the Institute has put us at the cutting edge in our field. Looking at what we’ve already achieved, we’re really excited about the prospects for the future.” Anthony Ledford, Chief Scientist, AHL. AHL Oxford has grown significantly since we opened in 2007 and has become a vibrant and thriving part of AHL’s wider research group. We have continued our long standing principle of recruiting exceptionally strong researchers from both industry and quantitative academic disciplines including mathematics, statistics, engineering, computing, the applied sciences and econometrics. AHL Oxford’s research output has made significant commercial impacts within AHL’s business, spanning everything from algorithms that automatically check high-frequency market data through to new trading and order execution models that manage billions of dollars.

* FUM USD21.2 billion as at 30 June 2010

www.oxford-man.ox.ac.uk

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