Oxford Man Institute Annual Report 2011 by Annabel Goss

A n n ua l R ep o rt Ox f ord- M an I n s ti tu te of Quantitative Fi n an ce

SEPT10AUG11

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

welcome

Welcome to the fourth Annual Report from the Oxford-Man Institute (OMI), highlighting current research activities and the progress that has been made developing interaction among research disciplines. In this report I am pleased to be able to share with you details of the research interests of the Institute’s members, associate members and students. OMI is fast gaining a reputation as a global centre of excellence in the study of quantitative finance and alternative investments, and the work of our colleagues in this field continues to draw keen interest from academics and industry practitioners around the world. In the subsequent pages you will have an opportunity to learn more about our members, and this year we have included extended discussion on the research of three colleagues; Tarun Ramadorai (page two) discusses his interest in international financial contagion, whilst articles on Ben Hambly (page six) and Kevin Sheppard (page four) explain their respective research concerning the volatility of commodities and financial markets. OMI’s events programme is an important facet of the Institute, enabling members and students to better position themselves to contribute to the development of quantitative finance. Over the past year we have hosted numerous seminars, conferences and workshops, as well as a Summer School. We also initiated a series of thematic workshops preceded by relevant tutorials to provide our students with the opportunity to broaden and increase their knowledge of specific subjects. Details of some of these events can be found in this report. The past academic year has not only proven to be very successful, but it has been a time of change for OMI. In July our founding Director, Professor Neil Shephard stepped down and Professor Terry Lyons was appointed as his successor. I speak on behalf of all my colleagues when I say it is an emotional moment to see Neil stepping down as Director, and

we are all very grateful for the work he has completed over the last four years. His vision and leadership have been instrumental in securing OMI’s current status and success. We are very pleased that he will continue to be involved in the Institute in his new role as leader of Financial Econometrics and Statistics. We are also delighted to welcome Terry Lyons as the new Director. Terry has been with OMI since its inception and has played an active role in the Institute’s life, supervising students, organising seminars, serving on the Executive Committee and integrating the stochastic analysis group. I know that Terry is delighted to be the Institute’s new Director and is looking forward to building on OMI’s global reputation. It is very difficult to capture the contributions, scientific value, dedication and academic stature of both Neil and Terry, but we have included a dedicated insert in this report which we hope will provide some insight into both Directors’ involvement in the Institute. I would like to take this opportunity, on behalf of all my colleagues, to thank Man Group Plc for their continued funding and support – without which, the progress highlighted herein would not have been possible.

Thaleia Zariphopoulou Man Professor of Quantitative Finance August 2011

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taru n ra m a d o ra i containing contagion The old maxim has never seemed more accurate: when the US sneezes, the rest of us catch a cold. What starts out as a blip in an obscure derivatives market can quickly spiral into a financial crisis that affects everyone, from the biggest governments to the poorest citizens. We need to understand how these panics spread to prevent them in the future. And that’s where thinkers like Tarun Ramadorai come in. An economist by training, Ramadorai is a Reader at the Saïd Business School and a member of the Oxford-Man Institute. He spends his professional life thinking about questions that combine theoretical complexity with immediate relevance to policy and regulation. The study of international financial contagion has been one of his major interests in recent years. He wants to understand how a crisis moves from market to market, often striking in areas with no obvious connection to the original source of the problem. After the financial turmoil of recent years, it’s an immensely topical subject. It helps explain how panic rippled out from a downturn in the US housing market to strike at great swathes of the international financial system, endangering major banks and insurers, and ultimately even disrupting governments’ ability to borrow. Ramadorai believes he’s uncovered some of the hidden conduits which transmit financial stress around the world. One of them turns out to be emerging market investment funds based in major financial centres. When there are unexpected losses in these centres, frightened investors pull money out of all risky assets, including emerging market funds. Funds must liquidate assets to raise the cash to pay these withdrawals, so stock exchanges thousands of miles away suffer a wave of selling, even though the original trouble was completely unconnected. Because these markets tend to be comparatively illiquid, with low daily trading volumes, this selling has a disproportionate impact.

“You wouldn’t expect that India and China would suffer so badly when London and New York did, but that was what happened during the last crisis,” Ramadorai comments. “Paradoxically, this kind of fire-sale activity seems to hit bigger and more liquid emerging markets the hardest – precisely because of their liquidity, fund managers try to sell more assets there.”

The research even suggests ways to predict where the risk is greatest, by examining where emerging market investors own a lot of assets in common. For example, if UK-based funds that are heavily invested in India also own lots of shares in Egypt, then both will suffer when London does, while other countries nearby may remain relatively untouched.

“It’s a real concern; the idea of spreading your investments between different markets is to reduce risk through diversification, but it turns out that by buying into these markets, these investors are actually creating a new source of correlation between them,” says Ramadorai. The effects are predictable and tradeable. The research suggests an investor who followed the strategy rigorously could have made high risk-adjusted returns over recent years. The findings may be even more relevant to regulators as Ramadorai believes restricting these vectors of financial contagion could help them control the spread of panic in a crisis. His other long-term interest lies in hedge funds. These are among the big financial success stories of recent decades; they aim to beat the market with flexibility and sophisticated trading techniques. Once thought of as the preserve of the ultra-rich, in recent years hedge funds’ popularity has grown rapidly to take in pension funds, asset managers, university endowments and even private savers. This widening appeal has brought vast amounts of new cash into the industry, but it also creates risk. Wealthy hedge fund investors can probably look after themselves, but newer investors may be less sophisticated and need more protection from regulators.

taru n ra m a d o ra i Hedge funds are no longer peripheral market players; they are a major component of the international financial system. A big hedge fund collapse could now have serious consequences for the wider economy. But there’s still little information available about what these funds are doing to get their returns. What risks are they running, and how well do their investors understand them? Ramadorai and his co-author have developed a way to find out.

“Hedge funds are very frequent traders, but their reporting is almost entirely voluntary and even those that do provide information usually only do so quarterly,” Ramadorai explains. On top of that, they need to report only long positions; short bets go undisclosed. “We wanted to get a sense of what they do over much shorter periods, like a single day on which the markets crash,” he adds. The solution is to use statistical analysis to analyse readily available data on general market trends alongside the much lower-frequency data on hedge fund returns, in order to understand the relationships between them. “We end up with the best possible explanation of how their returns vary with general market movements, and with other factors like liquidity, volatility and the availability of leverage,” Ramadorai explains. Testing the method’s predictions on the few funds that do provide daily updates on their activities seems to confirm its accuracy.

Another surprising discovery is that while analysis based on monthly data might suggest hedge funds have low risk exposures, higher-frequency analysis shows that at other times they are exposed to much greater risk. Broadly, they seem to run the most risk right after they have reported, and the lowest just before doing so. More generally, Ramadorai is sceptical of most hedge funds’ claims to be able to beat the market consistently. Some can, but only a minority, and only by taking clear risks, he argues. These funds are quickly spotted by smart investors and deluged with money; early success often proves hard to replicate at larger scales. That success comes at a price, too; fees are steep, and have continued to rise in the last decade. When this is taken into account, returns don’t look as impressive. “Our research shows that hedge fund families that have done well in the past do seem to be able to charge higher fees,” Ramadorai notes. “But is their performance really better? In general there seems to be no real difference, after fees, between the best and worstperforming funds.” There’s an argument that hedge funds need closer supervision. If they had to report what they’re doing more often and in more detail, it would be easier for regulators to ensure they’re not creating build-ups of risk that could threaten the whole system. It’s a delicate balance; too much oversight could threaten the agility that’s among hedge funds’ main strengths. Too little could mean the next one to implode takes its bank counterparties down with it, or imperils a major pension scheme.

What do the results tell us? One clear conclusion is that when the market becomes volatile, hedge funds generally retreat from risky assets and switch into safer ones such as shortmaturity bonds – just like most other investors.

Ramadorai argues the problem is being addressed, at least partly. “There’s certainly a case for better reporting, but I think this is beginning to happen already – hedge funds are gradually being drawn into the regulatory net,” he says.

This casts some doubt on their claim to be providing vital liquidity in a crisis, buying when others are fleeing for safety. It also suggests that those who invest in hedge funds to diversify their portfolio’s risks could get a surprise if real trouble appears.

All this research is being put to good use. Earlier this year Ramadorai joined the newly-formed European Securities and Markets Authority (ESMA), becoming a member of the Group of Economic Advisors of its Committee for Economic and Markets Analysis.

Do regulators need to take further steps to stop the spread of crisis? Are there new indicators they should be monitoring for signs of trouble? It’s still early days for ESMA, but before too long the questions Ramadorai and his colleagues discuss at its biannual meetings could become big news for the whole industry.

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k e v i n s h eppar d good volatility, bad volatility When journalists and commentators say markets are ‘volatile’, they don’t mean it in a good way. Technically, of course, when a price rockets upwards it’s being just as volatile as when it crashes, but you’d never know it from reading the financial press. Could this intuitive feeling that volatility is in itself a bad thing point to a deeper insight? Kevin Sheppard thinks so. He’s a financial econometrician, interested in measuring how financial markets behave at ever-greater levels of detail. That involves painstaking attention to tick-by-tick financial data, as well as plenty of computational firepower. His office at the Oxford-Man Institute (OMI) hosts numerous humming servers and hard drive arrays – a necessity if you want to tangle with datasets describing tens of millions of trades a day.

“You hear a lot about volatility in the news, and it’s always a bad thing,” Sheppard explains. “People say the markets are volatile when they’ve fallen. They blame volatility when they lose, but they don’t give it any credit when they win. I did some research on this, and I couldn’t find a single positive use of the term in the financial press.

From a statistical point of view, it’s 50-50 – volatility could mean something is rising or falling, but I wondered if there’s more truth to the idea that volatility is associated more with negative returns – if volatility really could be bad for society as a whole.” He set about finding out by analysing high-frequency data to extract a different metric, called ‘realised semivariance’, which is like volatility but also includes information on the direction prices are moving. Sheppard and fellow OMI researcher Andrew Patton calculated it in a variety of different markets using prices sampled every five minutes. They were particularly interested in sudden, discontinuous movements, known as jumps, in an asset’s price, which generally happen when unexpected new information becomes available – for example, when a central bank changes interest rates. By definition, such a price jump forms a spike in volatility. The researchers looked at any five-minute periods where a price rose or fell significantly relative to the five-minutes immediately before or afterwards. This allowed them to decompose volatility into two components – the everyday volatility that forms a constant backdrop to all market activity, and the ‘jump volatility’ that’s created by unexpected news. The use of realised semivariance provided further guidance by separating out good jumps, where the price heads upwards because, say, a share buyback is announced, and bad ones, where long-term investors become worse off.

“We were trying to understand how the market processes these events, when there’s a major positive or negative surprise, and what the implications are for future volatility,” Sheppard explains. The results were eye-opening. “We found there’s a huge asymmetry between how good and bad surprises are received. The impact of good news is transitory, usually lasting a few days at most, whereas the effects of bad news lasts up to three months. So when people consistently associate volatility with bad consequences, they’re right!” In fact, after a few initial flickers, good news actually seems to reduce volatility. Investors are happy; not only have they made money from the good news, but their portfolio’s returns are smoother for the next few days. Bad news, by contrast, tends to leave sustained turbulence in its wake. This kind of asymmetry only appears when looking at sudden jumps in the market; smaller, steadier price movements have similar effects whether they are up or down.

k e v i n s h eppar d Until recently, such phenomena were barely visible, because most people focused on daily price data. It’s only by zooming in to watch how the market reacts to information over much shorter periods that researchers can start to understand its behaviour. “Working with high-frequency data is like having a microscope,” Sheppard says. “It lets us see what’s going on in the market in a level of detail that was once impossible.” He has built on this work by creating sophisticated models that use this ‘realized’ data to forecast volatility – more accurately than previous models, over many time horizons, and in asset classes ranging from US equities to currencies and emergingmarket stocks. The financial industry is slowly adopting such models, which should help traders and investors manage their risks more effectively. As assets’ liquidity improves, the models’ range of possible applications will grow still further. High-frequency econometrics has been growing steadily in popularity since the mid-1990s. In part this has been made possible by the widespread availability of rich and detailed financial datasets. Vastly increased trading volumes on exchanges all over the world also help. More markets now have enough liquidity for effects like these to be measurable – it’s hard to get a detailed idea of a stock’s volatility if it trades just a few dozen times a day. Sheppard cites a day during the recent financial crisis when the S&P 500 index rose by about 10%, then crashed 15% before rallying in the last hour to close almost unchanged. Examining only daily price data misses the dramatic swings in the price, and a risk manager could come away with the impression it was an uneventful day’s trading. Likewise, as we emerged from the most turbulent period of the recent crisis, volatility fell significantly. But someone using only daily data wouldn’t have noticed until much later. Due to the noise that using this data introduces into analysts’ calculations, to get a statistically meaningful result it’s necessary to look back over a much longer period. This in turn means it takes much longer to spot emerging trends.

Using more frequent data means analysts can look at two or three days’ worth of prices, rather than a couple of months. In early September 2008, their view wouldn’t have been clouded by the lingering presence of August’s (relatively low) volatility; they would have seen what was going on in the present far more clearly and been able to act promptly as volatility changed.

These findings could be important to risk managers at banks, hedge funds and other financial institutions. For example, they could give traders in the options market, who are essentially betting on changes in volatility, a more up-to-date idea of the dangers they face. More specifically, they could have helped financial institutions assess their risks more accurately in the run-up to, and the wake of, the crisis. It’s possible some could have weathered it better as a consequence. One investment bank claimed afterwards that for two days in a row during the crisis, market movements were so extreme that they qualified as ‘six-sigma events’. By definition, these events, when prices move more than six times as violently as usual, practically never happen. To have two in a row is almost inconceivable.

The bank was trying to claim it couldn’t possibly have foreseen the events that lost it so much money – they were one-in-a-trillion freak events. But is this true, or was it just looking at the wrong things? Sheppard suspects the latter. “If you look at how volatility was changing, those events don’t look all that surprising at all,” he says. The bank only believed they were so incredibly unlikely because it didn’t understand how volatility was rising, which was due to the fact that the data its risk managers were using wasn’t frequent enough and hid new developments in a dangerous way.

“In normal times, a 10% drop in the market is very rare,” Sheppard comments. “But in the middle of a crisis it’s not rare at all.” If his work helps drive home that message, he’ll have done us all a favour.

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b e n h a m b ly the mathematics of power Commodities are among the most fertile fields for innovation in finance today, and Ben Hambly is helping to create the mathematical framework that makes it all possible. Hambly, a Professor of Mathematics and associate faculty member at the Oxford-Man Institute (OMI), is a specialist in the mathematics of probability. Throughout his career he has sought out new and exciting financial markets in need of firmer theoretical underpinnings. The burgeoning trade in commodities and the exotic derivatives that are springing up as a result are the latest area to attract his interest. Once thought of as stolid and uninteresting compared to more fashionable assets like equities and credit, commodities have seen an upsurge in appeal in recent years. New participants have poured into markets such as metals, oil and electricity, and interest is increasing at a pace.

“Things are developing incredibly fast,” Hambly says. “Already there are lots of fascinating problems to study, and areas where we don’t yet have a good mathematical model of what’s going on.”

One reason for the peak in interest is the enormous volatility that commodity markets offer and the opportunities they create for agile traders. A five percent price move over the course of a day is considered violent and exceptional in stocks, bonds or foreign exchange, but it’s nothing in commodities. Wholesale electricity prices can spike tenfold over just a few hours. This is partly because what’s being traded isn’t just a financial instrument, but something that’s needed in the real world. If someone doesn’t get the shares they want to buy immediately, it’s no catastrophe. But if the wholesale power market fails to connect buyers with sellers, homes go dark and factories shut down. Likewise, if the price of government bonds spike, people might simply buy less, but if a natural disaster causes an oil refinery to shut down, prices will rocket. Electricity is especially volatile because it can’t readily be stored. At any given time, the market has to find a price at which all the power being produced can be used. Banks are desperately trying to create bespoke products that let traders profit from violent price swings, or power producers and users manage the risks they bring. As a result financial engineering in commodity markets is growing ever more complex. Hambly is working on the mathematics behind these exotic financial structures. For example, he and colleagues have created a robust mathematical framework for pricing ‘swing options’, which give power consumers some rights to buy electricity at a fixed price - allowing them to hedge the risk of sudden brief increases in their running costs, but also letting them benefit if prices fall sharply. Other innovations to which his methods apply include ‘tolling agreements’ – exotic derivatives that effectively let market participants set up as virtual power stations, turning production on and off as conditions change and looking to profit from the difference between fuel inputs and power output.

It’s not just the financial industry that’s driving the market’s growth; the industries that produce and consume commodities are moving in too. Energy generators are realising they need mathematical finance techniques if they’re to accurately value the power stations they own, or efficiently finance the construction of new ones. Likewise, mining and energy extraction firms are realising that the methods they use to value the reserves that they plan to exploit in the future are crude, and that using more sophisticated techniques could transform how they do business. Hambly is giving them the tools to rethink the energy market.

b e n h a m b ly One unsettling feature of working on the cutting edge is that today’s hot new market can turn into tomorrow’s pariah. Hambly’s previous research includes considerable work on how to price complex structured credit products, whose value depends on a large portfolio of underlying assets. Structures like collateralised debt obligations (CDOs) and mortgage-backed securities enjoyed explosive growth for a few years, but plenty of investors lost out when the market crashed in 2008 and now few new deals are done.

Hambly built a dynamic model that greatly improved on the methods being used to value these deals at the time. It used stochastic partial differential equations to understand the losses that could be expected when buying them. Unfortunately, this radical improvement arrived just when the bottom fell out of the market. He is resigned, though. “Obviously this wasn’t quite what I was expecting, but it’s a risk you take when you work in these areas. In retrospect, the market became far too dependent on simplistic models, and too divorced from the assets behind these structures. But fundamentally default risk is still there and in theory CDOs were a sensible way of sharing it out.” OMI provides a supportive environment for those working at the frontiers of knowledge. It brings together people from different backgrounds, including academics from a variety of fields as well as finance professionals. A recent OMI seminar on energy markets, for example, provided Hambly with the opportunity to exchange insights with fellow academics and senior quants, as well as traders from investment banks and hedge funds. For Hambly it was a chance to compare models and pricing methods, and discuss the trends that are driving the market’s development feeds into new and interesting financial phenomena.

A major issue of concern to practitioners is the increase in the level of unhedgeable risk in the markets. “Everyone wants to manage risk, but lots of these risks are impossible to hedge,” he comments. “If you’re in the oil market, how do you hedge a political risk like the Arab spring? How could traders in natural gas have protected themselves from the technological advances in shale gas production that have sent prices diving? A lot of this is hard to deal with mathematically,” he admits. “But it’s still vital to know about it if you’re interested in these markets.”

He still works on a broad repertoire of research interests outside finance. One long-running project draws on the theory of fractals – mathematical constructs like the famous Mandelbrot set, which display complex structures at all scales so that that no matter how far you zoom in, intricate new forms always become apparent. It turns out these are a great way to understand how particles move through different kinds of soil. That said, mathematical finance takes up a great deal of Hambly’s time. His success in the field might seem a little surprising, given that he fell into it almost by accident. In the mid-1990s, when it was an emerging and little-studied field, Hambly was a young lecturer at the University of Edinburgh. Senior managers decided to position the university to take advantage of the fast-growing field and launched a new MSc course, which Hambly was invited to run. Back then it was unusual for a PhD student to decide to go into the financial industry; these days it’s become the default career choice for mathematicians with a doctorate who don’t want to remain in academia.

“I had to learn a lot, very quickly!” Hambly recalls. “But it wasn’t long before I realised that many of the problems in mathematical finance were extremely interesting. It’s the combination of challenging mathematics and potential financial impact that makes this such an exciting area to be in.”

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m e m b ers mike giles

greg gyurko

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 Massachusetts Institute of Technology (MIT).

joined OMI in 2007 as one of the first student members of the Institute. He obtained a DPhil at the University of Oxford and is currently a Departmental Lecturer in the Mathematical Institute, where he is a member of the Mathematical and Computational Finance Group.

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 â&#x20AC;&#x2DC;Quant of the Yearâ&#x20AC;&#x2122; 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.

Greg is the course director of the MSc in Mathematical and Computational Finance, and is actively involved in organising the Practitioner Lecture series and the Mathematical Finance Internal Seminar series. Gregâ&#x20AC;&#x2122;s research interests relate to the theory and applications of Rough Paths Theory, as well as the development and software implementation of probabilistic numerical methods for approximating solutions to stochastic differential equations and certain types of partial differential equations.

More recently, he has developed the multilevel Monte Carlo method for the pricing of financial options, and is active in the exploitation of GPUs (graphical processing units) for high performance computing in a variety of application areas.

georg gottlob 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.

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.

georg

greg

He was a Professor at the University of Technology, Vienna from 1988-2005, 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.

ben hambly

ben mike

m e m b ers vicky henderson

sam howison

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.

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.

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.

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.

hanqing jin completed his PhD in Financial Engineering in 2004 at the Chinese University of Hong Kong. He is a University Lecturer at the Mathematical Institute, is on the editorial board of Mathematical Methods of Operations Research and is also a member of the Mathematical and Computational Finance Group. His research interests include portfolio selection, behavioural finance, applied stochastic analysis and optimisation. He has previously worked on stochastic control, portfolio selection with transaction costs and behavioural portfolio selection. He is currently working on time consistency of dynamic decisions.

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.

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chris

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m e m b ers shin kanaya

terry lyons

is a Postdoctoral Research Fellow at the Department of Economics. He earned a Bachelor’s and Master’s degree from the University of Tokyo, majoring in economics, and a PhD in Economics from the University of Wisconsin-Madison in 2008.

is the newly appointed Research Director of the OxfordMan Institute. He is the Wallis Professor of Mathematics at the University of Oxford, a Fellow of the Royal Society and one of the UK’s leading mathematicians, having made a number of contributions to stochastic analysis.

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 working on the following projects: nonparametric testing of the stationarity for continuous-time Markov processes, and nonparametric estimation for mixed frequency time series data.

His interest in stochastic analysis relates particularly to the control of non-linear systems driven by rough paths. Prime examples of such systems are provided by stochastic differential equations and stochastic systems.

gechun liang joined the Oxford-Man Institute as a Postdoctoral Research Fellow in the Michaelmas Term of 2010. Prior to that, he was a student member of the Institute whilst completing a DPhil in Mathematics at the Mathematical Institute under the supervision of Professor Terry Lyons. He has a Master’s Degree in Mathematics from Tongji University, and studied finance as an undergraduate in Jilin University.

josé martinez is a Lecturer in Finance at the Saïd 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. José 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.

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ter ry

His research interests are mainly focused on mathematical finance and applied probability. He is especially interested in backward stochastic differential equations and credit risk modelling.

His research on ‘rough paths’ has founded a new field, stimulating an enormous amount of work, allowing breakthroughs in many areas such as numerical analysis. He has a deep understanding of the role of risk in financial markets where he is known for his work on managing uncertainty in volatility, and for developing cubature methods as new tools allowing more efficient numerical modelling.

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m e m b ers sergey nadtochiy

han ozsoylev

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.

is a Lecturer in Financial Economics at the Saïd Business School. Before joining the University of Oxford, he earned his PhD in economics from the University of Minnesota and BSc in Mathematics from Bilkent University. He has held visiting appointments at the University of California Berkeley and Johns Hopkins University. Han’s research primarily focuses on financial market imperfections, such as those generated by asymmetric information, imperfect competition, behavioural biases, and bounded memory. He has studied information sharing amongst stock market investors and, in particular, how social and information networks affect asset prices and investor welfare. He is also interested in questions related to financial fragility, liquidity and market manipulation.

jan obłój

tarun ramadorai

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.

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 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.

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.

Recent areas of focus include: robust pricing and hedging of exotic derivatives via the Skorokhod embedding problem, comparative performance of robust and classical hedging methods, portfolio optimisation under pathwise constraints and hedge-funds managers’ incentive schemes, inverse problems for utility maximisation.

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han

tarun

His current research is concerned with the construction of socalled ‘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 semi-static 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.

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m e m b ers steve roberts

kevin sheppard

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.

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.

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

neil shephard is Head of Financial Econometrics and Statistics at the Oxford-Man Institute and a 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.

is a Research Fellow in Finance and Economics at the Saïd Business School. Ruediger came to the Saïd 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.

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

mungo

rued iger

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 – 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

neil

kevin

steve

m e m b ers thaleia zariphopoulou

xunyu zhou

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.

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.

lan zhang is a Reader in Finance. Her research focuses on market microstructure, statistical arbitrage and high frequency financial econometrics. She has developed a number of influential methods for analysing high frequency financial data, including the two-scale and multi-scale realised volatility estimators (TSRV, MSRV) to handle market microstructure.

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.

Recently Lan analysed the general theoretical properties of local constancy approximation in continuous semimartingales. Her current work includes the analysis of limit order books observed in real time, robust estimation of high frequency quantities and its application to portfolio management and options trading.

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lan

Lan has published widely in leading journals including Econometrica, Review of Financial Studies, Journal of Econometrics, Journal of American Statistical Association, Bernoulli, and Annals of Statistics. She is an Associate Editor of the following academic journals: Statistics and Its Interface, Annals of Applied Statistics, and Econometric Theory. She is on the advisory board of the International Center for Futures and Derivatives at the University of Illinois at Chicago. She completed her undergraduate degree at Peking University in China and obtained her Masterâ&#x20AC;&#x2122;s and PhD degree from the University of Chicago.

xunyu

thaleia

stu d e n ts bahman angoshtari

vladimir cherny

is a second 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.

is a second year DPhil student at the Mathematical Institute. His research interests lie broadly in stochastic analysis and optimisation theory with their applications to mathematical finance.

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.

sylvestre burgos is studying for a DPhil in Mathematics within the Mathematical and Computational Finance Group. He holds a BSc in Mathematics from the University Paris VI, an MSc in Engineering from the Ecole Centrale Paris and an MSc in Mathematical and Computational Finance from the University of Oxford.

martin gould is a second year DPhil student in Mathematics. He holds an MASt (Part III) in Mathematics from the University of Cambridge and a BSc in Mathematics from the University of Warwick. His primary research interest is the limit order book, and in particular in developing a dynamic stochastic model of limit order trading that is better able to explain the diffusive nature of the return series in foreign exchange markets. He hopes to be able to extend his model to gain insight into how prices are affected by the release of macroeconomic news by central governments and to examine how changes in limit order arrival flows propagate through the network of different currency pairs.

ni hao is a second year DPhil student in the Stochastic Analysis Group. Ni previously completed a Bachelor’s Degree in Mathematics at Southeast University, China and a Master’s Degree in mathematical and computational finance at the University of Oxford. She is currently working on rough paths theory with her supervisor Professor Terry Lyons, and her research interest is the expected signature of stochastic processes.

ir vladim

bahman

Sylvestre’s research interests lie broadly in the field of numerical methods for computational finance. His research under the supervision of Mike Giles focuses on the computation of Greeks with Multilevel Monte Carlo simulations.

He is working under the supervision of Jan Obłój on implementing methodology of Azema-Yor processes for different optimisation problems in mathematical finance, such as long-term expected utility growth rate maximisation subject to drawdown constraint.

e sylvestr tin mar

ss youne

stu d e n ts richard hills

nathaniel korda

is a DPhil student in Financial Economics from the Saïd Business School, and is interested in the effect of the factors determining liquidity in financial markets. His current research is on liquidity as measured by price impact in market microstructure models (as opposed to transaction costs or bid-ask spreads), and investigating models where this price impact is random, and hence there is liquidity risk.

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.

He has previously worked for various technology companies, and spent two years in Credit Derivatives Technology at Morgan Stanley. He has a MEng in Engineering from the University of Oxford, and an MPhil in Finance from Cambridge.

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. Arend’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.

sigrid källblad

ada lau is studying for a DPhil in Mathematics. Her research interests include time series forecasting, spatiotemporal correlation modelling and latent Gaussian processes. Ada 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. She has submitted her thesis on “Probabilistic Wind Power Forecasts: From Aggregated Approach to Spatiotemporal Models”.

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.

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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.

ada

anth y on

arend

is a second year DPhil student in the Mathematical and Computational Finance Group. Sigrid works under the supervision of Professor Thaleia Zariphopoulou and her research interests are in stochastic control and portfolio optimisation.

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.

nathaniel

sigrid

ard rich

stu d e n ts arnaud lionnet

cavit pakel

is a DPhil student at the University of Oxford. His interests in mathematics include functional analysis and probability theory and he is very interested in complex and dynamical systems, especially when they involve randomness (markets, population evolution, meteorology, etc).

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.

More specifically he is interested in stochastic differential equations, Malliavinâ&#x20AC;&#x2122;s calculus and rough paths. He specialises in backward stochastic differential equations, which he finds interesting for two of their fields of application: their connections with some kinds of partial differential equations on the one hand and some problems of mathematical finance on the other (option pricing, risk measures).

kasper lund-jensen is a DPhil student in Economics at Nuffield College. Prior to his doctoral studies he completed a BSc in Economics at the University of Copenhagen and a MSc in Finance and Economics at the London School of Economics. Kasperâ&#x20AC;&#x2122;s research interests lie in the areas of financial econometrics and economic forecasting. Currently, his research is focused on out-of-sample equity premium predictability and combination forecasts.

diaa noureldin is a DPhil student in Economics. He is interested in financial econometrics, particularly modelling and forecasting volatility and dependence in financial time series. He is interested in developing methods suitable for large dimensional systems and high-frequency data. Diaa previously studied for an MPhil in Economics at the University of Oxford, and holds a BA and MA in Economics from the American University in Cairo. In Michaelmas 2011, he will join the Department of Economics at the University of Oxford as a Postdoctoral Research Fellow.

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.

daniel schwarz Daniel Schwarz is a DPhil Student at the Mathematical Institute and a member of the Mathematical and Computational Finance Group. Previously he obtained a Master of Mathematics (MMath) degree from the University of Oxford. His current research is focused on the stochastic modelling of energy markets. In particular he has been developing models for spot and derivative prices in carbon emission and electricity markets and worked on the pricing of spark and dark spread options, which are routinely used to value power plants. In addition, Daniel is interested in the asymptotic analysis of these models, which provides intuition for the underlying dynamics and leads to approximations that are useful for the calibration to market data.

michael streatfield is interested in hedge funds and investment management. He is a third year doctorate student supervised by Tarun Ramadorai. In his research work he has been analysing the determinants of hedge fund management and incentive fees and in particular exploring how hedge fund management companies set prices for the future funds they launch.

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r kaspa

iel dan

mi chae l

diaa

arnaud

His future research involves analysing the impact of the recent crisis on hedge fund reporting. Prior to his DPhil, Michael worked in the investment industry for 15 years in London and South Africa.

OM I b i d s ‘ a u r ev oir’ t o i ts f ou nd i ng D ir ect or In July this year Neil Shephard stepped down as Director of the Oxford-Man Institute (OMI). Neil has played an integral role in the formation and continued development of OMI. We spoke to him to gain a closer insight into his aspirations for the Institute and the reasons behind his decision to step down as Director. How did your involvement in the Institute first come about? I was already part of an existing interdisciplinary network in financial research at the University of Oxford when Man Group approached us in 2006 about the opportunity to create the Institute. I lead the team responsible for putting together the University’s pitch. Our vision for the Institute was very closely aligned to Man’s aspirations and when the agreement was formalised, the University invited me to be the Institute’s first Director, which I was very happy to accept.

What was your vision for the Institute when you first opened its doors in September 2007? Our aims for the Institute were very clear when it was first founded and we continue to strive to achieve them. We’re here to be excellent at academic work in quantitative aspects of finance. We want to generate new ideas that are applicable to financial problems, to write good papers and be respected by our peers around the world for what we are doing. We’re also dedicated to training the next generation of researchers and attracting the highest calibre of young people to come and work here.

Did you have any personal objectives when you took on the role of Director of the Institute? I wanted it to be more than just a space where people can focus and develop their academic ideas. I wanted it to increase their aspirations. People can often be very self-limiting. They’ll have ideas, but feel constrained about what they can actually achieve. I wanted the Institute to encourage people to raise their game and to overcome barriers – to seek out opportunities. My aim was to create an exciting place that people would want to come to.

Do you feel you’ve achieved that aim? Absolutely - I think the strongest indicator of the Institute’s success is the calibre of people we’re attracting to the UK from around the world. We have an extraordinary group of post doc researchers here. They’re second to none in the world and I’m so proud that they’ve chosen to spend the most crucial time in their career here. Our newest recruit who joins us in September 2011 had something like 14 offers from some of the best universities around the globe and he’s chosen to come here. It’s a very real indicator to the world that we’re a centre of excellence!

Part of your initial vision was to generate new ideas in the area of quantitative finance – do you feel you’ve achieved that objective over the last four years? Some very good ideas have been generated at the Institute. Two of note include Mike Giles’ work on harnessing graphics cards to complete calculations quickly using simulated complicated derivatives, and the software my colleague Tarun Ramadorai’s been developing to provide a unified view of hedge fund return databases. They’re extremely exciting and receiving a lot of recognition.

What has the Institute enabled you to achieve? OMI has enabled me to take my work to the next stage of empirical relevance. I could have completed my research in a more abstract, theoretical way, but it has enabled me to take it to a more applied situation. To some degree it’s because I’ve had the opportunity to talk to Man’s commercial team, but it’s also down to the fact that we have better data resources here, a very good computational infrastructure with fantastic compute servers and specialist graphics card machines, better funding and an extremely efficient administration team that I’m very proud of. They do a great job, which leaves us to focus on our research!

What has prompted your decision to step down as Director? My family and I are relocating to London and I feel very strongly that the Institute’s Director should be here full-time, which will become impossible for me based in London. I also feel that it’s time for OMI to move on and for some fresh ideas to be injected into it. I hope to be able to contribute to the Institute’s successes in the future, but it’s time for someone else to step up and to take it to the next level.

What would you like to see the Institute achieve in the future? I’d like to see it go from strength to strength: to continue on its path to becoming recognised as the world’s centre of excellence in our academic field, which I think we’re well on the way to achieving. I’d like to see more effective use of our interdisciplinary strengths and more research projects across disciplines, which I know my successor is keen to pursue. I’m extremely proud of what has been achieved, but there’s a lot more that can be done and I’m happy to be stepping down to let someone else take up the mantle.

Man Gr oup’ s collid ing world s Tim Wong, CEO, AHL and Anthony Ledford, Chief Scientist, AHL discuss their vision of bringing together the commercial and academic worlds of quantitative finance, and choosing Oxford as the place for it all to happen. What was the reasoning behind the collaboration with the University of Oxford? [Tim Wong] In 2006, we decided that we wanted to create a collaboration with a leading research institution in the UK or Europe to help us develop the business. We had a vision of bringing together the commercial and academic worlds of quantitative finance as, in our experience, these two worlds never seemed to meet in a meaningful way. After discussions with several top academic institutions it was clear that Oxford was the natural choice. They had an appetite to do it, they saw opportunity, and they had a unique expertise in the departments and disciplines we were interested in.

Why is this collaboration so innovative? [TW] The closeness of the academic and commercial relationship is unique – it’s something that a lot of people have talked about creating, but few have realised. [Anthony Ledford] You have leading academics and leading commercial practitioners working together on a daily basis. It is also a multi-disciplinary environment attracting people from different subject areas including econometrics, mathematics, statistics, computer science and engineering. By drawing on expertise from related fields, you bring new, creative ideas to both practical and theoretical problems.

What have been the key commercial benefits to Man? [AL] Man has been able to develop part of its tail protect strategy using models that were first developed and openly published by academics in the Oxford-Man Institute (OMI). The initial research was focused on building a forecasting model, but we saw huge potential in this and incorporated it into something that was a marketable trading product. That occurred because we have a network of quants within Man who meet up regularly in Oxford and gain exposure to the work of the academics there. [TW] We’ve also brought both expertise and people from Oxford into our electronic trading division where most of the underlying machinery relies on market micro-structure methods – a field of practice where Oxford has much to offer.

Has the OMI achieved what you’d hoped it would? [TW] Yes, it has. The last four years have represented only the first phase of the collaboration and I think we have in many ways exceeded our objectives by achieving an ‘openness’ with the academics. [AL] They have really embraced this, and we are delighted by the people we’ve come across, and also by the fact that we have been able to recruit key talent into our business as well.

Will there be a change of direction with the appointment of Professor Terry Lyons as the new OMI Director? [AL] Neil was instrumental in helping us develop the right model of collaboration between the University and Man, and also in persuading academics from various departments to make this multi-disciplinary institute a reality. There will be some change of emphasis as Neil comes from an econometrics background and Terry comes from a mathematics background, but the core principles of the Institute will remain the same. [TW] The aim now is to build on this foundation and establish OMI as the world’s leading quantitative finance institute and also to see whether we can derive more fruits for Man through this collaboration. [AL] In the long run, I’d like to see the Institute’s research encompass three things: research which is academically worldleading, research which has wide market and industry systemic benefit, and research which will benefit Man specifically. OMI is established in the first area and now it’s about building our ideas in the other two over the next period.

What are the plans for the future? [TW] We recently presented back to the management board to discuss funding for OMI and we are pleased to announce that this has been renewed until 2015. We’re looking for opportunities to expand in Oxford in ways that benefit the wider Group. Work has been very AHL-focused up until now, but we’d like to have representation from our other businesses – GLG, Multi-Manager, and MSS.

A multi-disciplinar In his first interview since his appointment his goals for building on the Institute’s suc You must be delighted to have been offered the Directorship having been involved in OMI since its inception? The last four years have been a very exciting time for us all. There’s been a fantastic scale of achievement under Neil’s guidance and leadership. From our initial work in putting together the bid, we’ve become a substantial research institute, which independent assessment confirms has a very wide and respected international reputation as a leading research institute in quantitative finance.

It certainly seems to attract a very international group of academics from varied disciplines, is that something you want to build on? The Institute’s main objective is to address the key problems associated with financial markets and risk in a way that has significant impact, and I believe this requires a truly multi-disciplinary effort. It also requires world-class researchers, which, thanks to the funding we receive from Man, we are able to attract and recruit. The problems are often too complex for a single discipline to resolve, but we are fortunate that my predecessor has drawn together a wide range of individuals with the right variety of expertise to identify and work on joint projects.

There are already a number of collaborative projects that have been undertaken at OMI, is this something you’re keen to pursue? One of my main goals is to create a framework that encourages and enables collaborations to happen, which is a challenge. There is an intrinsic contradiction between multi-disciplinary work and disciplinary excellence. To really succeed in multi-disciplinary projects you need people who are absolute masters of their field. But there’s a tension between people’s need to work on their own and contributing to broader goals. I don’t underestimate the challenge, but it’s absolutely key to really innovative research, so it’s essential that we make the effort and succeed in this objective.

ry challenge for the new Director as Director of the Oxford-Man Institute (OMI), Professor Terry Lyons discusses cessful foundation. Isn’t there a concern that this will detract from a member’s individual research? It’s very important that the early stage researchers have an opportunity to build a strong disciplinary foundation. They should not have to worry about having to prove their work as applied to anything. I want to nurture students in their disciplines, but let them benefit from being part of a multi-disciplinary team. However, I see no reason why, as researchers develop - and we’re fortunate enough to have a number of world-class researchers across the spectrum at OMI - that we cannot identify projects where expertise in different disciplines can be brought together to create something that is absolutely cutting edge and I expect my colleagues to want to jump at those opportunities.

How do you intend to achieve this multi-disciplinary focus? The ingredients for an environment that will nurture collaborations are rather intangible. A good collaboration can be triggered through a casual conversation, so I think it’s important to enable conversations to transpire on a large scale. The OMI environment already has many aspects that are effective at achieving this, such as our common dining area, our large number of graduate and post doc students and faculty, along with joint seminars, and I’d certainly like to see these develop. I’d also like to see members get together and articulate current projects that are already achieving something quite special, but could benefit from involvement from other disciplines. We then need to make sure that we use our resources to facilitate these projects in moving forward. Through our collaboration with Man we have tremendous resources available to us, which we should capitalise on to ease those early stages and ensure we get quality output. A lot of it is about making sure there are no impediments to getting started.

OMI enjoys a unique relationship with Man Group. Aside from the funding they provide, how does the relationship benefit members of OMI? To do quality research in an area such as finance, really does require a detailed engagement between practitioners and academics. We’re extraordinarily fortunate to have this collaboration with Man. Sharing a physical environment is hugely beneficial. We have lunch together and they attend our seminars: we exchange scepticisms and use their insight to sharpen and focus our thinking. It’s a very rewarding resource that shapes and refines the quality of our research. They’ve also benefitted as they have taken on a number of technicalities that we have developed and used them in their own research.

What other aspirations do you have for the Institute as you step into the role of Director? I would very much like to see jointly funded research projects with the industrial community. I also believe we should use our strengths to get involved in projects that have a public interest, such as effective and novel ways to understand and measure the risk involved in positions held by banks and other financial intermediaries – in which case we could leverage our core funding to add considerable value to any government/research council funded projects we undertake. We’d like OMI to be seen as a portal for the whole of the UK’s academic research in the area of quantitative finance. We welcome the engagement of our colleagues and practitioners in the UK and we’re already moving towards closer engagement with them by taking advantage of Man Group’s fantastic new offices and lecture theatre in London. It’s an exciting time and I’m very pleased to be involved. It’s hard to imagine that in just four years we could have moved so far and engaged the quality of people and projects that are here at OMI. Now it’s time to build on that solid foundation.

stu d e n ts nithum thain

weijun xu

is a MRes student in Computer Science. He holds a Bachelor’s in Mathematics from Queen’s University and a Master’s in Mathematics from McGill.

is a DPhil student in the Stochastic Analysis Group under the supervision of Terry Lyons at the University of Oxford. Before joining Oxford, he completed a Bachelor’s Degree in Economics and Mathematics at Shanghai Jiaotong University and a Master’s Degree in Statistics at Harvard.

Nithum’s research interest is in algorithmic game theory, particularly in financial and economic models that apply game theoretic structures to practical phenomena. For his current research, he is considering multi-agent coordination strategies.

kaiwei wang is a first year DPhil student in the Mathematical and Computational Finance Group at the Mathematical Institute. His research is focused on behavioural finance and time inconsistent problems.

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 focuses on numerical methods in finance, and he is currently working on a Multilevel Monte Carlo method for jump processes. He is also interested in other topics in financial mathematics, such as volatility modelling.

His research interests lie in the area of probability. He is currently working on the problem of inversion of signature for paths of bounded variation. Together with Professor Terry Lyons, he has developed methods to invert the signature for axis paths, which can only move parallel to the axes. Now he is trying to solve the inversion problem for general paths of bounded variation.

danyu yang is working with Professor Terry Lyons on rough path theory and its applications. She is interested in extracting nontrivial information of the path from its signature. She is currently working on the potential application of rough path theory to Harmonic analysis, especially to the convergence problem pioneered by the celebrated theorem of Carleson.

yifei zhong is a third year DPhil student in the Mathematical and Computational Finance group of the Mathematical Institute. He is supervised by Xunyu Zhou and Hanqing Jin. 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. His research is currently focused on optimal stopping time and applied PDEs. He is also interested in behavioural finance and time inconsistent problems.

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yifei

kaiwei nithum

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ass o c i ate m e m b ers horatio boedihardjo

nick jones

DPhil Student at the Mathematical Institute, University of Oxford.

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

Research Interests: Schramm-Loewner Evolution in Riemann Surfaces

andrea calì Lecturer, Brunel University. Research Interests: Knowledge Representation and Reasoning, Database Theory, Web Information Systems, Information Integration, Logics and Databases

tom cass Postdoctoral Research Assistant at the Mathematical Institute, University of Oxford. Research Interests: Stochastic Analysis, Probability Theory and Mathematical Finance

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

dmitry kramkov Professor at Carnegie-Mellon University, Pittsburgh and part time Professor at the University of Oxford. Research Interests: Computational Finance – Financial Derivatives, Optimal Investment, Numerical and Software Implementations of Financial Algorithms

jeremy large Research Economist, AHL and Fellow of St. Hugh’s College.

samuel cohen

anthony ledford

Junior Research Fellow at St. John’s College.

Chief Scientist, AHL.

Research Interests: Stochastic Analysis and Mathematical Finance

Research Interests: Extreme Value Theory, Modelling Financial Time Series, Automated Trading and Execution Systems, Market Microstructure and High Frequency Trading

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

thomas flury

asger lunde Professor of Economics, School of Economics and Management, Aarhus University. Research Interests: Time Series Econometrics, Financial Econometrics, and the Econometrics of Marketing

Quantitative Research Analyst, AHL. Research Interests: Time-series Econometrics, Financial Econometrics and Parameter Estimation with Particle Filters

matthias hagmann-von arx

colin mayer Professor of Management Studies, Saïd Business School, University of Oxford.

Head of Equities Strategies, AHL.

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

Research Interests: Non and Semi-parametric Econometrics, Empirical Finance, Systematic Trading Strategies

michael monoyios

tim jenkinson

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

Professor of Finance at the Saïd Business School, University of Oxford. Research Interests: Initial Public Offerings, Private Equity, Securitisation, Regulation and the Cost of Capital

Research Interests: Optimal Hedging in Incomplete Markets, Transaction Costs and Singular Control, Parameter Uncertainty in Investment and Hedging, Insider Trading and Information Problems

ass o c i ate m e m b ers per mykland

christoph reisinger

Robert M. Hutchins Distinguished Service Professor, Department of Statistics, The University of Chicago.

University Lecturer in Mathematical Finance at the Mathematical Institute, University of Oxford.

Research Interests: High Frequency Financial Econometrics

Research Interests: Modelling of Financial Markets and the Development, Analysis and Implementation of Efficient Methods for Derivative Pricing

thomas noe Ernest Butten Professor of Management Studies and Fellow of Balliol College.

torsten schรถneborn

Research Interests: The Application of Game Theory to the Design of Financial Securities and Corporate Governance Systems. The Interaction Between Product and Financial Markets and the Effect of Financial Markets on Managerial Incentives.

Quantitative Analyst, AHL.

wei pan DPhil Student in the Stochastic Analysis Group, University of Oxford. Research Interests: Application of Cubature Method to Various Option Pricing Problems

andrew patton Associate Professor of Economics, Duke University. Research Interests: Financial Econometrics, Forecasting, Volatility and Dependence Models, Hedge Funds

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.

zhongmin qian University Lecturer in the Mathematical Institute and Fellow at Exeter College. Research Interests: Rough Path Analysis and Non-linear Partial Differential Equations

Research Interests: Market Microstructure, Optimal Trade Execution, Optimal Investment under Transaction Costs

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

suresh sundaresan Chase Manhattan Bank Professor of Financial Institutions, Columbia University. Research Interests: Central Bank Liquidity Provision, Hedge Funds, Asset Allocation

lukas szpruch Nomura Research Fellow at the Mathematical and Computational Finance Group within the Mathematical Institute. Research Interests: Theoretical and Applied Probability Theory, Stochastic Analysis and Numerical Methods for Stochastic Processes

pedro vitori DPhil Student in Mathematics, University of Oxford. Research Interests: Stochastic Analysis, Optimal Control and Mathematical Finance

jan hendrik-witte DPhil Student at the Mathematical Institute, University of Oxford. Research Interests: The Development of Unconditionally Stable Finite Difference Schemes for the Numerical Solution of Nonlinear Partial Differential Equations in Finance

people

E v e n ts Hedge Fund Conference 19th November 2010 Organising Committee Andrew Patton, Duke University and Oxford-Man Institute and Tarun Ramadorai, University of Oxford

Speakers David Hsieh, Duke University; Wei Jiang, Columbia University; Philippe Jorion, University of California, Irvine; Robert Kosowski, Tanaka Business School, Imperial College; Tarun Ramadorai, University of Oxford; Oliver Scaillet, HEC Geneva

Adam Smith Asset Pricing Workshop 25th March 2011 Organising Committee Christian Julliard, London School of Economics; Anna Pavlova, London Business School; Tarun Ramadorai, University of Oxford; Raman Uppal, London Business School; Mungo Wilson, University of Oxford; Kathy Yuan, London School of Economics

Advances in Portfolio Theory and Investment Management 13th-14th May 2011 Organising Committee Ioannis Karatzas, Columbia and INTECH; Alex Schied, University of Mannheim; Thaleia Zariphopoulou, University of Oxford

Stochastic Portfolio Theory Speakers Erhan Bayraktar, University of Michigan, Ann Arbor; Robert Fernholz, INTECH; Kostas Kardaras, Boston University; Vassilios Papathanakos, INTECH; Johannes Ruf, Columbia University; Winslow Strong, University of California, Santa Barbara

Portfolio Management under Forward Criteria Speakers Nicole El Karoui, École Polytechnique; Marek Musiela, BNP Paribas; Sergey Nadtochiy, University of Oxford; Michael Tehranchi, University of Cambridge

Speakers

Optimal Execution of Trades

Anisha Ghosh, Carnegie Mellon University; Christian Julliard, London School of Economics; Alex P. Taylor, Manchester Business School; Bryan Kelly, University of Chicago; Seth Pruitt, Federal Reserve Board of Governors; Snehal Banerjee, Northwestern University; Jeremy Graveline, University of Minnesota; Jules van Binsbergen, Northwestern University and Stanford GSB; Michael Brandt, Duke University; Ralph Koijen, University of Chicago; Doron Avramov, Hebrew University of Jerusalem and University of Maryland; Tarun Chordia, Emory University; Gergana Jostova, George Washington University; Alexander Philipov, George Mason University; Anders Anderson, Institute for Financial Research (SIFR); Jose Vicente Martinez, University of Oxford; Frederico Belo, University of Minnesota; Vito Gala, London Business School; Jun Li, University of Minnesota

Speakers

This event was hosted at Saïd Business School with funding contributed from Oxford-Man Institute.

Aurélien Alfonsi, ENPC; Charles-Albert Lehalle, Crédit Agricole Cheuvreux; Alex Schied, University of Mannheim; Sasha Stoikov, Cornell University

e v e n ts The New Commodity Markets 14th-15th June 2011 René Carmona, Princeton University and Thaleia Zariphopoulou, University of Oxford

OMI and OCCAM Joint Workshop on Stochastic Differential Equations: Numerical Algorithms and Applications 8th-10th August 2011

Speakers

Organising Committee

Knut Kristian Aase, NHH; Fred Espen Benth, University of Oslo; Álvaro Cartea, Universidad Carlos III; Umut Cetin, LSE; Hélyette Geman, Birbeck; Ben Hambly, University of Oxford; Sam Howison, University of Oxford; Vincent Kaminski, Rice University; Rüdiger Kiesel, University of Duisburg-Essen; Lars Lochstoer, Columbia University; Ronnie Sircar, Princeton University; Nizar Touzi, École Polytechnique; Wei Xiong, Princeton University

Lajos Gergely Gyurko, University of Oxford; Lukas Szpruch, University of Oxford; Konstantinos Zygalakis, University of Oxford

Organising Committee

Speakers David F. Anderson, University of Wisconsin, Madison; Radek Erban, University of Oxford; Peter Friz, TU Berlin; Mike Giles, University of Oxford; Desmond J. Higham, University of Strathclyde; Arnulf Jentzen, Princeton University; Peter E. Kloeden, Goethe Universitat; Terry Lyons, University of Oxford; Stéphane Menozzi, Université Paris VII, Denis Diderot; Christoph Resinger, University of Oxford; Erik von Schwerin, KAUST; Eric Vanden-Eijnden, NYU

Workshops and Courses

This event was jointly funded with the Oxford Centre for Collaborative and Applied Mathematics (OCCAM).

Stochastic Portfolio Theory, 12th May 2011 Ioannis Karatzas, Columbia University and INTECH An overview of stochastic portfolio analysis building on the work of E.R. Fernholz, A. Banner, C. Kardara, S. Pal, V. Papathanakos, T. Ichiba, D. Fernholz and J.Ruf.

Risk Measures, June 2011 Fred Delbaen, ETH, Zurich Two sets of two lectures around the topic of risk measures. As a real expert in this field, these lectures attracted a large audience.

New Commodity Markets, 13th June 2011 René Carmona, Princeton University A general introduction to the commodity markets, emphasising the physical nature of the interests underlying the contracts and derivatives, including discussion of the growing role of commodity indexes, the impact of the recent regulations, and some of the newest markets. A second lecture concentrated on specific mathematical models, their analysis and implementations using the examples of spread options.

LMS-EPSRC Summer School, 18th- 22nd July 2011 Dr Michael Monoyios, University of Oxford This event was jointly funded with the London Mathematical Society.

events

stu d e n t c o l l a b o rat i o n students members strike up more than just a collaboration… Students play a significant role in the life of the Institute. Over 20 scholarships are awarded each year to DPhil students at the University of Oxford who are researching topics connected with quantitative finance. Nathan Korda and Anthony Lee both completed their DPhils in the summer and have been members of the Institute for the duration of their doctorates. For them, the Institute has provided an environment that has nurtured their understanding of quantitative finance, broadened the scope of their research and afforded many opportunities that would otherwise have been unavailable to them. Student members of OMI are awarded personal desk space in the Institute, access to its computational resources, a research allowance of £2000 per annum, as well as admission to its common room, catering and busy conference and seminar programme. In return, students are expected to spend around half their working week at the Institute, but as Korda and Lee explain, the obligation is no hardship.

“The Institute’s resources are exceptional - far better than what’s currently available through my department. I get great desk space here, a lovely double computer screen, and access to any kind of server I want to use,” says Korda. “Because its run like a business, we’ve got dedicated IT and administration teams that are extremely efficient, which makes life very easy for us.”

Lee adds, “I work in computational statistics, and it can often be very hard to get what you need computationally, but the IT department here is fantastic. If you ever need anything you always know exactly who to go to and they respond very quickly.” Both students recognise that the funding they’ve received from the scholarship has played a significant part in advancing their research. It has not only provided the opportunity for them to travel to conferences and explore collaborations, but in the case of Korda, it has enabled him to maintain essential contact with his supervisor. He explains, “This year my supervisor moved to Toulouse and without the scholarship funds I wouldn’t have been able to afford to travel to see him. Having the extra £2,000 a year gives us more freedom and opportunity in our research, and the fact that lunch and dinner is provided really eases the pressure on my personal finances and my time.” Of course, OMI’s purpose built building was designed to encourage interaction between the Institute’s academic researchers and the commercially focused Man Research Laboratory. For students at the Institute, the opportunity to gain real industry insight from the Man Group is invaluable. As Korda explains, interaction is practically unavoidable,

“Sharing common areas and lunch and coffee breaks with the Man Group gives you exposure to people working in the industry that wouldn’t be available to you otherwise. It’s given me a chance to learn about the banking industry and what their work involves.” The interdisciplinary nature of the Institute has also had a major impact on both students’ research. The opportunity to work alongside academics from different research fields within quantitative finance has not only enhanced their personal research, but has led to a collaboration between the two students, that is unlikely to have materialised otherwise.

stu d e n t c o l l a b o rat i o n “The interdisciplinary nature of the Institute has opened up the opportunity for me to talk to people in different fields. You’d be missing an opportunity if you just allowed yourself to focus on your research in isolation when you’ve got such high calibre people involved in similar fields, all under one roof,” says Lee. Over the last two years Korda and Lee, a probabalist and statistician respectively, have been collaborating on a research project. Sharing an office in the Institute’s original building three years ago sparked a common interest for a strategy in the exploitation and exploration trade-off business. As Korda explains, the opportunity to discuss his research problem with a statistician is unlikely to have transpired outside of the Institute, “We would not have achieved this collaboration without being here. It’s been a slow burning project since we first shared an office three years ago and it’s certainly something we’ll continue to work on together when we leave.”

Korda’s personal research project has also been heavily influenced by his interaction with Lee. “My conversations with Anthony made me pay much more attention to the statistical side of my problem. So much so, I’ve decided that I’m going to an applicable statistical institute in Lille to do my post doc in September, as I have realised that it is important for me to focus much more on the statistical side of my research.”

Lee has also benefitted from the opportunity to collaborate with Korda. He explains, “It’s been really helpful to talk in depth with someone working in probability. It’s very beneficial to talk to someone who has a keener attention to mathematical rigour than I do - at times!”

Through the Institute, the two students have struck up more than just a research collaboration. Their shared love of music has resulted in them forming a jazz band, which has performed at the Institute on more than one occasion. Asked whether the band is open to members of the Institute, Korda jokes, “Some people have suggested that they’d like to sing with us, but they never put themselves forward forcibly enough!” So, if any members do fancy a turn in front of the microphone, now’s your chance - the Institute may have helped form a lifelong academic association, but the band will have to disband when Korda leaves for Lille and Lee starts his post doc at Warwick in September 2011.

feature

v i s i t o rs The Institute has hosted a number of visitors over the past year – many come to give seminars while others come to work on collaborative projects with OMI members.

faculty

graduate students

Long-term visitors:

Fred Delbaen, Department of Mathematics, ETH, Zurich

Heather Battey, University of Cambridge

Ronnie Sircar, Operations Research & Financial Engineering, Princeton University

Kai Du, School of Mathematical Sciences, Fudan University

Mingyu Xu, Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CN)

Short-term visitors: Marco Avellaneda, Professor of Mathematics, Courant Institute of Mathematical Sciences, NYU Leopoldo Bertossi, School of Computer Science, Carleton University Francis Caron, University of British Columbia Robert J Elliot, RBC Financial Group Professor of Finance, Haskayne School of Business, University of Calgary, Alberta

Andrea Karlová, Stochastic Informations, Institute of Information Theory and Automation (UTIA) Henning Marxen, Department of Mathematics, Technische Universität Kaiserslautern Jian Su, University of Illinois, Chicago Jin Zhang, University of Illinois, Chicago

Short-term visitors: Yunjiang Jiang, Department of Mathematics, Stanford University Phillip Monin, The University of Texas at Austin

Ioannis Karatzas, Eugene Higgins Professor of Applied Probability, Columbia University and INTECH

practitioners

Marcin Kacperczyk, Assistant Professor of Finance, Leonard N. Stern School of Business, New York University

Long-term visitors:

Eva-Maria Lütkebohmert-Holtz, Head of the Research Group for Quantitative Finance Pricing of Risks in Incomplete Markets, University of Freiburg

Tim Hoggard, visiting research fellow Sushant Vale, Tata Consulting Services

Klaus Ritter, Computational Stochastics, Department of Mathematics, Technische Universität Kaiserslautern Boris Rozovsky, Ford Foundation Professor of Applied Mathematics, Brown University Olivier Scaillet, Professor of Finance and Statistics, Swiss Finance Institute George Tauchen, William Henry Glasson Professor of Economics and Finance, Duke University

“IC ha Institute an integrate C the st mathematics, C sta but the reality is is I superb; the facil I had a good cha presentations by Ph departments, C and a Cwith several

25 “I was very impressed with the level of scholarship and the research at OMI. The researchers and faculty were eager to exchange ideas and discuss their work. It was intellectually rewarding to visit the Institute.” Marco Avellaneda

George Tauchen

“My talk at OMI was very well attended by mathematicians of various ages. I got a very positive reaction and enjoyed some interesting discussions on several mathematical questions of joint interest.”

“OMI is a very good place to do research. There is a lot going on: many visitors, many seminars and plenty of opportunities to meet people. That is what makes OMI interesting – the presence of “Practitioners” and/or “quants” enables discussions on problems that are not always treated in academic surroundings. Having so many visitors and university people around ensures there is always an audience for good discussion.” Fred Delbaen

Etienne Pardoux

ad heard about the nd how well it manages to trengths of the University in atistics, economics and finance, even better. The physical setup lities and amenities world-class. ance to participate in a series of h.D candidates over in the maths a chance to discuss research issues faculty and visitors at OMI.”

Ioannis Karatzas

“I immensely enjoyed my visit to OMI. I was able to meet and interact with several researchers, either directly in my field or in allied fields. The interdisciplinary character of OMI was most rewarding. I found the seminars by external visitors extremely interesting and helpful. For instance I learned from one speaker, the details of the “Billion Prices” project at MIT, and I was honoured to join the dinner group for that speaker. The hospitality was warm and thoughtful. I would truly enjoy visiting again.”

“My visit was intellectually stimulating and delightful, with a lunch, seminar, and meetings with the faculty. I was impressed by the range of issues which the faculty was working on, and the excellent research facilities and environment at OMI.” Suresh Sundaresan

visitors

w o r k i n g papers sam cohen

tim jenkinson

Cohen, S.N., Ji, S. and Peng, S. 2011. Sublinear Expectations and Martingales in Discrete Time.

Jenkinson, T.J. and Stucke, R. 2011. Who Benefits from the Leverage in LBOs?

Cohen, S.N. 2011. Representing Filtration Consistent Nonlinear Expectations as G-Expectations in General Probability Spaces.

Jenkinson, T.J., Axelson, U., Stromberg, P. and Weisbach, M. 2010. Borrow Low, Buy High? The Determinants of Leverage and Pricing in Buyouts.

Cohen, S.N., Elliott, R.J. and Siu, T.K. 2011. Backward Stochastic Difference Equations for Dynamic Convex Risk Measures on a Binomial Tree.

dmitry kramkov

martin gould

Kramkov, D. and Predoiu, S. 2011. Integral Representation of Martingles and Endrogenous Completeness of Financial Models.

Gould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. 2011. Limit Order Books.

Kramkov, D and Bank, P. 2011. A Model for a Large Investor Trading at Market Indifference Prices.

Gould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. 2011. Statistical Properties of Forging Exchange Limit Order Books.

anthony lee

georg gottlob Gottlob, G. 2011. On Minimal Constraint Networks. Benedikt, M., Gottlob, G. and Senellart, P. 2011. Determining Relevance of Accesses at Runtime (Extended Version).

jan hendrik witte Witte, J.H. and Reisinger, C. 2010. On the Penalisation Error for American Options in a Jump Model. Witte, J.H. and Reisinger, C. 2010. On the Use of Policy Iteration as an Easy Way of Pricing American Options. Witte, J.H. and Reisinger, C. 2010. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance.

ni hao Hao, N. and Lyons, T. 2011. Expected Signature of Brownian Motion up to the First Exit Time of the Domain.

vicky henderson Henderson, V., Sun, J. and Whalley, E. 2011. Portfolios of American Options under General Preferences: Results and Counterexamples. Henderson, V. and Hobson, D. 2009. Risk Aversion, Indivisible Timing Options and Gambling. Henderson, V. 2009. Partial Liquidation and the Disposition Effect.

Lee, A., Caron, F. Doucet, A. and Holmes, C. 2011. A Hierarchical Bayesian Framework for Constructing Sparsity-Inducing Priors. Lee, A., May, B.C., Korda, N. and Leslie, D. N. 2011. Optimistic Bayesian Sampling in Contextual-Bandit Problems. Lee, A., Caron, F., Doucet, A. and Holmes, C. 2011. Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors.

anthony ledford Ledford, A. and Ramos, A. 2011. Markov Modelling of the Within Time Series Dependence. Presented at Environmental Risk and Extreme Events, Ascona.

colin mayer Mayer, C. 2011. Mobile Banking and Financial Inclusion: The Regulatory Lessons. Mayer, C. 2011. Savings as Forward Payments: Innovations on Mobile Money Platforms. Mayer, C. 2010. Regulatory Sanctions and Reputational Damage in Financial Markets.

thomas noe Noe, T., Banerjee, S. and Bhattacharyya, S. 2011. Pumping up the SEO: The Rewards of Uninformed Speculation.

han ozsoylev Ozsoylev, H., Walden, J., Yavuz, D. and Bildik, R. 2011. Investor Networks in the Stock Market. Mimeo.

wei pan Pan, W. Application of Cubature Method to TARN Option Pricing. Pricing Digital Option Using Cubature Method.

w o r k i n g papers tarun ramadorai

ruediger stucke

Ramadorai, T. and Patton, A. 2011. On the High-Frequency Dynamics of Hedge Fund Risk Exposures. Internet Appendix.

Stucke, R., Harris, B. and Jenkinson, T. 2010. A White Paper on Private Equity Research and Data.

Ramadorai, T. 2010. On the Dynamics of Hedge Fund Risk Exposures.

Stucke, R. 2010. Does Private Equity Underperform or Outperform Public Equity?

Ramadorai, T. and Streatfield, M. 2011. Money for nothing? Understanding Variation in Reported Hedge Fund Fees.

Stucke, R. and Higson, C. 2010. The Private Equity Performance Puzzle.

Ramadorai, T., Jotikasthira, P. and Lundblad, C. 2011. Asset Fire Sales and Purchases and the International Transmission of Financial Shocks. Ramadorai, T., Watugala, S. and Albuquerque, R. 2011. Trade Credit and International Return Comovement.

suresh sundaresan Sundaresan, S. and Wang, Z. 2011. On the Design of Contingent Capital with Market Trigger.

sumudu watugala

Ramadorai, T., Acharya, V. and Lochstoer, L. 2010. Limits to Arbitrage and Hedging: Evidence from Commodity Markets.

Watugala, S. W., Albuquerque, R. and Ramadorai, T. 2011. Trade Credit and International Return Comovement.

christoph reisinger

weijun xu

Reisinger, C. and Giles, M. 2011. Stochastic Finite Differences and Multilevel Monte Carlo for a Class of SPDEs in Finance.

Xu, W. And Jiang, Y. 2010. On Number of Turns in Reduced Random Lattice Paths.

Reisinger, C. and Bujok, K. 2011. Valuation of Basket Credit Derivatives in Structural Jump-Diffusion Models.

yuan xia

Reisinger, C. and Gupta, A. 2011. Robust Calibration of Financial Models Using Bayesian Estimators.

Xia, Y. and Giles, M. 2010. Multilevel Path Simulation for JumpDiffusion SDEs.

daniel schwarz

thaleia zariphopoulou

Schwarz, D. and Howison, S. 2011. Structural Modelling of Carbon Emission Markets.

Zariphopoulou, T., Musiela, M. and Sokolova, E. 2010. Indifference Valuation under Forward Valuation Criteria: The Case Study of the Binomial Model.

Schwarz, D. and Howison, S. 2011. Asymptotic Analysis of Pricing Models for Carbon Emission Markets. Schwarz, D., Carmona, R. and Coulon, M. 2011. Structural Modelling of Clean Spread Options and the Valuation of Power Plants.

lukas szpruch Szpruch, L. and Giles, M. 2011. A Note on Milstein Fundamental Theorem for Non-linear SDEs. Szpruch, L. and Giles, M. 2011. Efficient Multilevel Monte Carlo Simulations of Non-Linear Financial SDEs without a Need of Simulation of Levy Areas. Szpruch, L. and Mao, X. 2011. Strong Convergence and Stability of Numerical Methods for Non-Linear Stochastic Differential Equations under Monotone Condition. Szpruch, L. and Mao, X. 2011. Strong Convergence Rates for Backward Euler-Maruyama Method for Dissipative-type Stochastic Differential Equations with Super-Linear Diffusion Coefficients.

Zariphopoulou, T., Leung, T. and Sircar, R. 2011. Forward Indifference Valuation of American Options, submitted for publication. Zariphopoulou, T. and Nadtochiy, S. 2011. A Class of Homothetic Forward Investment Process with Non-Zero Volatility, submitted for publication. Zariphopoulou, T. and Kallblad, S. 2011. On the Forward and Backward Portfolio Problem in Log-Normal Markets. Zariphopoulou, T., Kallblad, S. and Malamud, S. 2011. Qualitative Properties of Optimal Portfolios in Log-Normal Markets. Zariphopoulou, T. and Kallblad, S. 2011. Forward Optimal Portfolios.

lan zhang Zhang, L., Li, Y., Mykland, P., Renault, E. and Zheng, X. 2011. Realized Volatility when Sampling Times can be Endogenous. In revision for Econometric Theory. Zhang, L. 2011. What You Donâ&#x20AC;&#x2122;t Know Cannot Hurt You: On the Detection of Small Jumps. Zhang, L. and Mykland, P. 2011. Between Data Cleaning and Inference: Pre-Averaging and Other Robust Estimators of the Efficient Price.

papers

pu b l i c at i o n s sylvestre burgos

thomas flury

Burgos, S. Expected. 2010. Computing Greeks Using Multilevel Path Simulations, Monte Carlo and Quasi-Monte Carlo Methods, Springer Verlag, to appear.

Flury, T. 2010. Econometrics of Dynamic Non-Linear Models in Macroeconomics and Finance; DPhil thesis, University of Oxford.

andrea calì Cali, A. and Pieris, A. 2011. On Equality-Generating Dependencies in Ontology Querying (extended abstract), Proc. of SEBD. Cali, A., Gottlob, G. and Pieris, A. 2011. New Expressive Languages for Ontological Query Answering, Proc. of AAAI, to appear. Cali, A., Gottlob, G. and Pieris, A. 2011. Querying Conceptual Schemata with Expressive Equality Constraints, Proc. of ER, to appear. Cali, A., Gottlob, G. and Pieris, A. 2011. An Ontological Query Answering under Expressive Entity-Relationship Schemata, Information Systems Journal, to appear. Cali, A., Gottlob, G., Kifer, M., Lukasiewic, T. and Pieris, A. 2010. Ontological Reasoning with F-Logic Lite and its Extentions. Proc. of AAAI.

mike giles Giles, M., Klingbeil, G. and Erban, R. 2011. Fat vs. Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactionism, Transactions on Parallel and Distributed Systems, to appear. Giles, M., Klingbail, G. and Erban, R. 2011. Parallel Stochastic Simulation for the Systems Biology Toolbox 2 for MAT- LAB, Bioinformatics, to appear (subject to minor revisions). Giles, M. 2011. Approximating the Erfinv Function, GPU Compute Gems, volume 2, Morgan Kaufmann, to appear. Giles, M., Bradley, T., Du Toit, J., Tong, R. and Woodhams, P. 2011. Parallelisation Techniques for Random Number, GPU Computing Gems, 1, Morgan Kaufmann, to appear.

georg gottlob

Cali, A., Gottlob, G. and Pieris, A. 2010. Query Rewriting Under Non-Guarded Rules, Proc. of AMW.

Gottlob, G., Sellers, A. J., Furche, T., Grasso, G. and Schallhart, C. 2011.Taking the OXPath Down the Deep Web. EDBT, 542-545.

Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering Under Expressive Entity-Relationship Schemata. Proc. of ER,. 347-361.

Gottlob, G., Aschinger, M., Drescher, C., Jeavons, P. and Thorstensen, E. 2011. Structural Decomposition Methods and What They are Good For. STACS, 12-28.

Cali, A., Gottlob, G., Pieris, A., Marnette, B. and Lukasiewicz, T. 2010. Datalog±: A Family of Logical Knowledge Representation and Query Languages for New Applications. Proc. of LICS,. 228-242. Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering under Non-Guarded Rules in Datalog±:. Proc. Of RR,. 1-17. Cali, A., Gottlob, G. and Pieris, A. 2010. Advanced Processing for Ontological Queries. PVLDB 3 (1),. 554-565.

thomas cass Cass, T., Litterer, C. and Lyons, T. Rough Paths on Manifolds. (New Trends in Stochastic Analysis and Related) Topics, Worlds Scientific Press, to appear. Cass, T. 2009. Smooth Densities for Stochastic Differential Equations with Jumps. Stochastic Process. Appl, no.5, 1416-1435.

sam cohen Cohen, S.N. and Elliott, R.J. Existence, Uniqueness and Comparisons for BSDEs in General Spaces, in Annals of Probability, to appear. Cohen, S.N. and Elliott, R.J. Backward Stochastic Difference Equations and Nearly-Time-Consistent Nonlinear Expectations, SIAM Journal of Control and Optimization, 49, 125-139. Cohen, S.N., Elliott, R.J. and Pearce, C.E.M. A General Comparison Theorem for Backward Stochastic Differential Equations, Advances in Applied Probability, 42(3), 878-898.

Gottlob, G., Sellers, A. J., Furche, T., Grasso, G. and Schallhart, C. 2011. OXPath: Little Language, Little Memory, Great Value. WWW (Companion Volume), 261-264. Gottlob, G., Pichler, R. and Savenkov, V. 2011. Normalization and Optimization of Schema Mappings. VLDB J, 20(2), 277-302. Gottlob, G., Cali, A., Kifer, M., Lukasiewic, T. and Pieris, A. 2010. Ontological Reasoning with F-Logic Lite and its Extentions. Proc. Of AAAI. Gottlob, G., Cali, A. and Pieris, A. 2010. Query Rewriting under Non-Guarded Rules. Proc. of AMW. Cali, A., Gottlob, G. and Pieris, A. 2010. Query Answering under Expressive Entity-Relationship Schemata. Proc. of ER, 347-361. Gottlob, G., Cali, A., Pieris, A., Marnette, B. and Lukasiewicz, T. 2010. Datalog±: A Family of Logical Knowledge Representation and Query Languages for New Applications. Proc. Of LICS, 228-242. Gottlob, G., Cali, A. and Pieris, A. 2010. Query Answering under Non-Guarded Rules in Datalog±:. Proc. of RR, 1-17. Gottlob, G., Cali, A. and Pieris, A. 2010 Advanced Processing for Ontological Queries. PVLDB 3 (1), 554-565.

ben hambly Hambly, B.M., Bush, N., Haworth, H., Jin, L. and Reisinger, C. Stochastic Evolution Equations in Portfolio Credit Modelling, SIAM J. Fin. Math, to appear.

pu b l i c at i o n s Hambly, B.M., Biggins, J.D., and Jones, O.D. 2011. Multifractal Spectra for Random Self-Similar Measures via Branching Processes, Adv. Appl. Prob, 43, 1-39.

Lau, A., Baaquie, B. E., Cao, Y. and Tang, P. 2011. Path Integral for Equities: Dynamic Correlation and Empirical Analysis, Physica A, to appear.

Hambly, B.M. 2011. Asymptotics for Functions Associated with Heat Flow on the Sierpinski Carpet, Canadian J. Math, 63, 153-180.

anthony ledford

Hambly, B.M. and Croydon, D.A. 2010. Spectral Asymptotics for Stable Trees, Elec. J. Probab, 15, 1772-1801.

vicky henderson Henderson, V. and Hobson, D. 2011. Optimal Liquidation of Derivative Portfolios, Mathematical Finance, to appear. Henderson. V. 2010. Is Corporate Control Effective When Managers Face Investment Timing Decisions in Incomplete Markets?, Journal of Economic Dynamics and Control, 34 (6), 1062-1076.

jan hendrik witte

Ledford, A. and Ramos, A. 2011. An Alternative Point Process Framework for Modelling Multivariate Extreme Values, Communications in Statistics - Theory and Methods, 40, (12), 2205 – 2224.

anthony lee Lee, A. 2010. Comment on Particle Markov Chain Monte Carlo Methods. J. Royal Statistical Soc. B. Lee, A. 2010. On the Utility of Graphics Cards to Perform Massively Parallel Simulation with Advanced Monte Carlo Methods. JCGS.

Witte, J.H. and Reisinger, C. 2011. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance, SIAM Journal of Numerical Analysis, 49(1), 213—231.

gechuan liang

chris holmes

Liang, G. and Jiang, L. A Modified Structural Model for Credit Risk, IMA Journal of Management Mathematics, to appear.

Holmes, C. ,Yau, C., Papaspiliopoulos, O. and Roberts, G. 2011. Bayesian Non-Parametric Hidden Markov Models with Applications in Genomics, J Royal Stat Soc, Series B 73 (Part 1), 33-57. Holmes, C., Hjort, N., Muller, P. and Walker, S. 2010. Bayesian Nonparametrics. Cambridge University Press.

tim jenkinson Jenkinson, T.J., Abrahamson, M., and Jones, H. 2011. Why don’t U.S. Issuers Demand European Fees for IPOs?, Journal of Finance, to appear.

Liang, G., Lyons, T. and Qian, Z. Backward Stochastic Dynamics on a Filtered Probability Space, Annals of Probability, to appear.

jeremy large Large, J. 2011. Estimating Quadratic Variation when Quoted Prices Change by a Constant Increment, Journal of Econometrics, 160, 2-11.

terry lyons Lyons, T., Cass, T. and Litterer, C. 2011 Integrability Estimates for Gaussian Rough Differential Equations, 1-23, arXiv: 1104.1813

Jenkinson, T.J. and Sousa, M. 2011. Why SPAC Investors should listen to the Market?, Journal of Applied Finance, to appear.

Lyons, T., Cass, T. and Litterer, C. 2011. Rough Paths on Manifolds, New Trends in Stochastic Analysis and Related Topics, A volume in Honour of Prof K.D. Elworthy, arXiv: 1102.0998v1

hanqing jin

Lyons, T. and Hao, N. 2011. Expected signature of two dimensional Brownian Motion up to the first exit time of the domain. Pgs. 1-21 arXiv: 1101.5902

Jin, H., Dai, M. and Liu, H. 2011. Illiquidity, Position Limits, and Optimal Investment for Mutual Funds, Journal of Economic Theory, to appear. Jin, H. and Zhou, X. 2011. Greed, Leverage, and Potential Losses: A Prospect Theory Perspective, Mathematical Finance, to appear. Jin, H., Zhang, S., Hanqing, J., Zhang, S. and Yu Zou, X. 2011. Behavioural Portfolio Selection with Bounded Loss, Acta Mathematica Sinica. Jin, H., Dai, M., Zhong, Y. and Yu Zhou, X. 2010. Buy Low and Sell High. Contemporary Quantitative Finance, 317-334.

ada lau Lau, A. and McSharry, P. 2010. Approaches for Multi-Step Density Forecasts with Application to Aggregated Wind Power, Annals of Applied Statistics, 4, (3), 1311–1341.

Lyons, T., Liang, G. and Qian, Z. 2010. A Functional Approach to FBSDEs and Its Application in Optimal Portfolios, arXiv: 1011.4499 Lyons, T. and Litterer, C. 2010. High order recombination and an application to cubature on Wiener space, arXiv: 1008.4942

michael monoyios Monoyios, M. and Ng, A. 2011. Optimal Exercise of an Executive Stock Option by an Insider, International Journal of Theoretical and Applied Finance, 1483-106. Monoyios, M., Ng, A. and Danilova, A. 2010. Optimal Investment with Inside Information and Parameter Uncertainty, Mathematics and Financial Economics, 3, 13-38.

research

pu b l i c at i o n s Monoyios, M. 2010. Utility-Based Valuation and Hedging of Basis Risk with Partial Information, Applied Mathematical Finance, 17, 519-551.

josé martinez Martinez, J. and Sandleris, G. 2011. Is it Punishment? Sovereign Defaults and the Declines in Trade, Accepted, Journal of International Money and Finance, to appear. Martinez, J. 2010. Information Misweighting and the Cross Section of Stock Recommendations, Journal of Financial Markets, to appear.

per mykland Mykland, P. A., Lin, M. and Chen, R. 2010. On Generating Monte Carlo Samples of Continuous Diffusion Bridges, Journal of the American Statistical Association, 105, 820-838. Mykland, P.A., Ait-Sahalia, Y., and Zhang, L. 2011. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise, Journal of Econometrics, 160, 160-165. Mykland, P.A., Zhang, L., and Aït-Sahalia, Y. 2011. Edgeworth Expansions for Realized Volatility and Related Estimators, Journal of Econometrics, 160, 190-203. Mykland, P.A., and Zhang, L. 2011. The Double Gaussian Approximation for High Frequency Data, Scandinavian Journal of Statistics, to appear.

thomas noe Noe, T. 2010. Where Did all the Dollars Go? The Effect of Cash Flows on Capital and Asset Structure, Forthcoming in the Journal of Financial and Quantitative Analysis, to appear. Noe, T. 2009. Tunnel-Proofing the Executive Suite: Transparency, Temptation, and the Design of Executive Compensation, Review of Financial Studies, 22, 4849-4880 (lead article). Noe, T. 2009. Stock Market Liquidity and Firm Performance: Wall Street Rule or Wall Street Rules? (with Fang, V. and Tice, S.), Journal of Financial Economics, 94, 150-169.

diaa noureldin Noureldin, D., Shephard, N. and Sheppard, K. 2011. Multivariate High-Frequency-Based Volatility (HEAVY) Models. Journal of Applied Econometrics, to appear.

han ozsoylev Ozsoylev, H. and Werner, J., 2011. Liquidity and Asset Prices in Rational Expectations Equilibrium with Ambiguous Information, Economic Theory, to appear.

cavit pakel Pakel, C., Shephard N. and Sheppard K., 2011. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models, Statistica Sinica, 21, 307-329.

andrew patton Patton, A. and Timmermann, A. Predictability of Output Growth and Inzation: A Multi-Horizon Survey Approach, Journal of Business and Economic Statistics, to appear. Patton, A. 2011. Data-Based Ranking of Realised Volatility Estimators, Journal of Econometrics, 161(2), 284-303. Patton, A. 2011. Volatility Forecast Comparison using Imperfect Volatility Proxies, Journal of Econometrics, 160(1), 246-256. Patton, A. and Timmerman, A. 2010. Why do Forecasters Disagree? Lessons from the Term Structure of Cross-Sectional Dispersion, Journal of Monetary Economics, 57(7), 803-820. Patton, A. and Timmerman, A. 2010. Monotonicity in Asset Returns: New Tests with Applications to the Term Structure, the CAPM and Portfolios Sorts, Journal of Financial Economics, 98(3), 605-625.

stephen roberts Roberts, S., Yoon, J.W., Dyson, M. and Gan, J. 2011. Bayesian Inference for an Adaptive Ordered Probit Model: an Application to Brain Computer Interfacing, Neural Network, to appear. Roberts, S., Karastergiou, A., Johnston, S., Lee, H., Weltevrede, P. and Kramer, M. 2011. A Transient Component in the Pulse Profile of PSR J0738-4042, Monthly Notices of the Royal Astronomical Society, to appear. Roberts, S. and Ebden, M. 2011. Graph Marginalization for Rapid Assignment in Widearea Surveillance, Adhoc Networks Journal 9(2), 180-8, to appear. Roberts, S., Psorakis, I. and Ebden, M. 2011. Overlapping Community Detection using Bayesian Non-Negative Matrix Factorization, Physical Review E, in press. Roberts, S., Fox, C. 2011. A Tutorial on Variational Bayesian Inference. Artificial Intelligence Review, Spinger, in press. Roberts, S. Yoon, J.W. Dyson, M. and Gan, J. 2011. Bayesian Inference for an Adaptive Ordered Probit model: an Application to Brain Computer Interfacing, Neural Networks, in press. Roberts, S., Karastergiou, A., Johnston, S., Lee, H., Weltevrede, P. and Kramer, M. 2011. A Transient Component in the Pulse Profile of PSR J0738-4042, Monthly Notices of the Royal Astronomical Society, B0736-40. Roberts, S. and Ebden, M. 2011. Graph Marginalization for Rapid Assignment in Wide-Area Surveillance, Adhoc Networks Journal 9(2), 180-8. Roberts, S., Reece, S., Nicholson D. and Lloyd, C. 2011. Determining Intent using Hard/Soft Data and Gaussian Process Classifiers, Proceedings of Fusion. Pickup, L., Capel, D., Roberts, S. and Zisserman, A. 2010. Multiframe Super-Resolution from a Bayesian Perspective, In Super-Resolution Imaging, Chapter 9, CRC Press, 247-284. Roberts, S. and Reece, S. 2010. The Near Constant Acceleration Gaussian Process Kernel for Tracking, IEEE Signal Processing Letters, 17(8), 707-710.

pu b l i c at i o n s Roberts, S., Ebden, M. and Stranjak, A. 2010. Visualizing Uncertainty in Reliability Functions with Application to Aero Engine Overhaul, Journal of the Royal Statistical Society C. Volume 59, part 1, 163-173.

Ramadorai, T. 2010. The Secondary Market for Hedge Funds and the Closed Hedge Fund Premium, Journal of Finance, to appear. Internet Appendix.

Roberts, S. Garnett, R. Osborne, M. A. Reece, S. and Rogers, A. 2010. Sequential Bayesian Prediction in the Presence of Changepoints and Faults, The Computer Journal, 53(9), 1430-1446.

neil shephard

Roberts, S. and Yoon, J. W. 2010. Robust Measurement Validation in Target Tracking using Geometric Structure, IEEE Signal Processing Letters, 17(5), 493-496. Roberts, S. and Lee, S. M. 2010. Sequential Dynamic Classification using Latent Variable Models, The Computer Journal, 53, 1415-1429. Roberts, S., Lowne, D. and Garnett, R. 2010. Sequential NonStationary Dynamic Classification with Sparse Feedback, Pattern Recognition, 43, (3)0, March 2010, 897-905. Psorakis, I., Roberts, S. and Sheldon, B. 2010. Soft Partitioning in Networks via Bayesian Non-Negative Matrix Factorization, Proceedings of NIPS 2010 workshop on community detection. Reece,S., Mann,R., Rezek. I. and Roberts, S. 2010. Gaussian Process Segmentation of Co-Moving Animals, 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering , Chamonix, France. Kaufman, M. and Roberts, S. 2010. Coordination vs. Information in Multi-Agent Decision Processes, Proceedings of AAMAS 2010. McInerney, R., Roberts, S. and Rezek, I. 2010. Sequential Bayesian Decision Making for Multi-Armed Bandit, Proceedings of AAMAS 2010. Roberts, S. and Reece, S. 2010. An Introduction to Gaussian Processes for the Kalman Filter Expert, Proceedings of Fusion 2010. Roberts, S., Garnett, R. and Osborne, M. A. 2010. Bayesian Optimization for Sensor Set Selection, Proceedings of IPSN 2010, Stockholm. Roberts, S. and Ebden, M. 2010. Graph Marginalization for Rapid Assignment in Wide-Area Surveillance. International Conference on Ad Hoc Networks, Niagara Falls, Canada, LNICST (Lecture Notes of the Institute for Computer Sciences, SocialInformatics and Telecommunications Engineering), 28, 691-703.

christoph reisinger Reisinger, C., Bush, N. Hambly, B.M. Haworth, H. and Jin, L. 2011. Stochastic Evolution Equations in Portfolio Credit Modelling, SIAM Journal on Financial Mathematics, to appear. Reisinger, C. and Witte, J.H. 2011. A Penalty Method for the Numerical Solution of Hamilton-Jacobi-Bellman (HJB) Equations in Finance, SIAM Journal of Numerical Analysis, 49(1), 213-231.

tarun ramadorai Ramadorai, T. 2011. Capacity Constraints, Investor Information, and Hedge Fund Returns, Journal of Financial Economics, to appear. Previously entitled “Investor Interest and Hedge Fund Returns.”

Shephard, N., Noureldin, D. and Sheppard, K. 2011. Multivariate High-Frequency-Based Volatility (HEAVY) Models, Journal of Applied Econometrics, to appear. Shephard, N. and Flury, T. 2011.Bayesian Inference Based only on a Simulated Likelihood, Econometric Theory, to appear. Shephard, N., Barndorff-Nielsen, O. E., Lunde, A. and Hansen, P.R. 2011. Subsampling Realised Kernels, Journal of Econometrics, 160, 204-219. Shephard, N. Multivariate Realised Kernels: Consistent Positive Semi-Definite Estimators of the Covariation of Equity Prices with Noise and Non-Synchronous Trading, Journal of Econometrics, to appear. Shephard, N. Nuisance Parameters, Composite Likelihoods and a Panel of GARCH Models, forthcoming Statistica Sinica. Shephard, N. Bayesian Inference Based only on Simulated Likelihood: Particle Filter. Shephard, N. 2010. Deferred Fees for Universities, Economic Affairs, 30, (2), 40-44. Shephard, N. and Barndorff-Nielsen, O.E. 2010. Volatility, in Encyclopedia of Quantitative Finance, edited by Rama Cont, John Wiley and Sons Ltd, Chichester, UK, 1898-1901. Shephard, N., Barndorff-Nielsen, O. E. and Kinnebrouck, S. 2010. Measuring Downside Risk: Realised Semivariance, in Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle, edited by T. Bollerslev, J. Russell and M. Watson (eds), Oxford University Press, 117-136. Shephard, N. 2010. Realising the Future: Forecasting with High Frequency Based Volatility (HEAVY) Models, Journal of Applied Econometrics, 25, 197-231.

torsten schöneborn Schöneborn, T. and Martin, R. 2011. Mean Reversion Pays, but Costs. RISK, 96-101.

suresh sundaresan Sundaresan, S., Tonetti, C., Bartolini, L. and Hilton, S. 2011. Collateral Values by Asset Class: Evidence from Primary Securities Dealers, Financ. Stud, (2011) 24(1), 248-278. Sundaresan, S., Asvanunt, A. and Broadie, M. 2011 Managing Corporate Liquidity: Strategies and Pricing Implications, International Journal of Theoretical and Applied Finance, 14, (3), 369-406.

nithum thain Thain, N., Mirrokni, V., and Vetta A., 2011. On the Implications of Lookahead Search in Game Playing.

research

pu b l i c at i o n s zhongmin qian Qian, Z. and Tudor, J. 2011. Differential Structure and Flow Equations on Rough Path Space, arXiv:1102.561, to appear. Qian, Z. Tudor, J. and Cass, T. 2011. Non-Linear Evolution Equations Driven by Rough Paths, arXiv:0911.0281, to appear. Qian, Z. and Ying, J. 2011. Martingale Representations for Diffusion Processes and Backward Stochastic Differential Equations, arXiv:0910.4911, to appear in Sem de Probab. Qian, Z., Zheng, W. and Duan, X.L. 2011. On Local Linear Approximations to Diffusion Processes, to appear in International Journal of Mathematics and Mathematical Sciences.

Zhang, L. and Mykland, P.A. 2010. The Econometrics of High Frequency Data. Statistical Methods for Stochastic Differential Equations, to appear.

yifei zhong Dai, M. and Zhong, Y. 2010. Penalty Methods for ContinuousTime Portfolio Selection with Proportional Transaction Costs, Journal of Computational Finance, 13(3), 1-31. Dai, M. and Zhong, Y. 2010. Optimal Stock Selling/Buying Strategy with Reference to the Ultimate Average, Mathematical Finance, to appear.

thaleia zariphopoulou

Dai, M, Jin, H., Zhong, Y. and Zhou, X. 2010. Buy Low and Sell High, Contemporary Quantitative Finance: Essays in Honour of Eckhard Platen, Springer, 317-334.

Zariphopoulou, T. and Sircar, R. 2010.Utility Valuation of Credit Derivatives and Applications to CDOs, Quantitative Finance, 10 195-208.

Dai, M, Zhong, Y. and Kwok, Y.K. 2011. Optimal Arbitrage Strategies on Stock Index Futures under Position Limits. Journal of Futures Markets, 31, 394-406.

Zariphopoulou, T., Sokolova, K. and Musiela, M. 2010. Indifference Valuation in Incomplete Binomial Models, Mathematics in Action, 3(2), 1-36.

Bian, B., Dai, M., Jiang, L., Zhang, J. and Zhong, Y. 2011. Optimal Decision for Selling an Illiquid Stock, Journal of Optimization Theory and Application, to appear.

Zariphopoulou, T. and Musiela, M. 2010. Portfolio Choice Under Space-Time Monotone Performance Criteria, SIAM Journal on Financial Mathematics,1, 326-365.

xunyu zhou

Zariphopoulou, Z. and Zitkovic, G. 2010. Maturity-Independent Risk Measures, SIAM Journal on Financial Mathematics,1, 266-288. Zariphopoulou, T. and Musiela, M. 2010. Stochastic Partial Differential Equations and Portfolio Choice, Contemporary Quantitative Finance, Springer-Verlag, 195-215. Zariphopoulou, T. and Musiela, M. 2010. Initial Investment Choice and Optimal Future Allocations Under Time-Monotone Performance Criteria, International Journal of Theoretical and Applied Finance, 14(1), 61-81.

lan zhang Zhang, L. 2011. Estimating Covariation: Epps Effect, Microstructure Noise, Journal of Econometrics, 160, 33-47. Zhang, L., Mykland, P.A., and Ait-Sahalia, Y. 2011. Edgeworth Expansions for Realized Volatility and Related Estimators, Journal of Econometrics, 160, 190-203. Zhang, L. Mykland, P.A. and Ait-Sahalia, Y. 2011. Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise, Journal of Econometrics, 160, 160-175. Zhang, L., Kang, Z.X. and Chen, R. 2010. Forecasting Return Volatility in the Presence of Microstructure Noise, Statistics and its Interface, 3 (2), 145-158. Zhang, L. Implied and Realized Volatility: Empirical Model Selection. 2010. Annals of Finance, to appear. Zhang, L., and Chen, R. Kang, Z.X. 2010. Forecasting Return Volatility in the Presence of Microstructure Noise, Statistics and Its Interface. 3 (2), 145-158.

Zhou, X. and Jin, H. 2011. Greed, Leverage, and Potential Losses: A Prospect Theory Perspective, to appear in Mathematical Finance. Zhou, X., Meyer-Brandis, T. and Øksendal, B. 2011. A Mean-Field Stochastic Maximum Principle via Malliavin Calculus, Stochastics, (A Special Issue for Mark Davis’ Festschrift), to appear. Zhou, X. and He, X. 2011. Portfolio Choice Under Cumulative Prospect Theory: An Analytical Treatment, Management Science, 57, 315-331. Zhou, X. and He, X. 2011. Portfolio Choice via Quantiles, Mathematical Finance, 21 (2011), 203-231. Zhou, X. Jin, H. and Zhang, S. 2011. Behavioral Portfolio Selection with Loss Control, Acta Mathematica Sinica, 27, 255-274. (A Special Issue Dedicated to Loo-Keng Hua on his 100th Birthday). Zhou, X. and Chiu, C. 2011. The Premium of Dynamic Trading, Quantitative Finance, 11, 115-123. Zhou, X. 2010. Mathematicalising Behavioural Finance, Proceedings of the International Congress of Mathematicians, Hyderabad, India. Zhou, X., Dai, M. , Jin, H. and Zhong, Y. 2010. Buy Low and Sell High, Contemporary Quantitative Finance, Edited by Carl Chiarella and Alexander Novikov, Springer, 317-334. (Essays in Honour of Eckhard Platen). Zhou, X. and Ji, S. 2010. A Generalized Neyman-Pearson Lemma for G-Probabilities. Probability Theory and Related Fields, 148, 645-669.

The last year has been one of transformation for Man Group, a worldleading alternative investment management business, and collaborative partner for OMI. In October 2010, the company acquired GLG Partners to create a broader mix of fund strategies and products for investors. The combined firm now has expertise in a wide range of investment styles including managed futures, equity, credit, emerging markets, global macro and multi-manager – all of which share a relentless focus on investment performance. Since the acquisition, new offerings combining the strengths of the two businesses have been developed. In February 2011, the company announced the launch of Man IP 220 GLG, a structured product which offers investors access to a combination of Man’s flagship managed futures manager, AHL, and a broad range of GLG’s discretionary strategies for the first time. Then in June, Man launched the Man GLG Multi-Strategy fund – a unique combined fund which offers investors access to a range of Man and GLG funds which comply with European UCITS regulations. This latest fund launch was a particular success attracting 100 million in commitments.

Japan fund launch

Alongside this growth in new funds and products, Man has also been investing in its people, technology and operations underpinning trading. In May, Man’s trend following CTA business, AHL, became the first CTA to create a standalone trading team in Hong Kong. AHL staff in Oxford can see this team on large LCD screens via a video link, and the Hong Kong trading team have a similar view of the Oxford team, binding together research and trading teams across the globe.

New hq opens In July, Man also unveiled new company headquarters in Swan Lane in the City of London. The new £250m development on the banks of the Thames houses 957 employees and includes the latest trading and communications technology designed to enable excellence in research, communication and trade execution. Through this pursuit of excellence and its continued focus on performance, Man is strongly positioned for the future, and the commitment to the Oxford-Man Institute of Quantitative Finance remains key.

Perhaps the most significant fund launch in the last year, however, was the successful launch of an AHL open-ended fund in Japan called Nomura Global Trend, which raised an initial US$1.5 billion. The fund began trading at the end of April 2011.

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