Oxford Man Institute of Quantitative Finance Annual Report 2011 to 2012 by Annabel Goss

ANNUAL REPORT O xf o rd - M a n I n s t i t u t e of Quantitative Finance

SEPT11/AUG12

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.

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WELCOME WELCO I am delighted to introduce the fifth Annual Report of the Oxford-Man Institute (OMI). Since beginning in 2007, we have achieved a reputation as one of the leading centres for Quantitative Finance and have welcomed many talented researchers, students and visitors through our doors. I feel very privileged to work with such outstanding colleagues

Statistical analysis of financial data is seen at OMI as a starting

and visitors, and am proud to introduce you to some of their

point for the development of new concepts and theories. To

work and research areas in this report. Short profiles of all

facilitate and promote this approach to quantitative research

our members can be found in the following pages, along with

Terry, together with his collaborators from OMI and Man

extended articles on the research of three of our members:

Group, set up the OMI Data Lab. More information about this

postdoctoral researchers Diaa Noureldin and Johannes Ruf,

initiative can be found in a separate article in this report.

and the Head of Finance at the Said Business School, Tim Jenkinson. These articles provide an insight into important areas of the current research activity of the Institute: financial econometrics, mathematical finance and private equity.

The events programme continues to be a crucial and rewarding element of the Institute’s activities. This year we were honoured to host the annual conference for SoFiE, the international society for Financial Econometrics, which saw 141 of the world’s

This academic year has been busy and challenging one for the

leading econometricians gather in Oxford. Details of this event

Institute’s new Director, Terry Lyons. Under Terry’s leadership,

can be found on page 22. We also hosted a very successful

the Institute has successfully recruited a number of outstanding

summer school in financial econometrics, which was attended by

young researchers in financial econometrics and financial

students from around the world and organised by our members.

economics who join the Institute for this next academic year. We are also delighted to announce that Dr. Marek Musiela will join the Institute in September as the Deputy Director. Marek is a world known finance industry practitioner and a very distinguished scholar, and will be a great asset to the Institute. During his first year, Terry has focused on strengthening and expanding the interdisciplinary activities of the Institute and

On behalf of everybody at the Institute, I would like to thank Man Group Plc for their continued funding and support.

Thaleia Zariphopoulou Man Professor of Quantitative Finance August 2012

building on its disciplinary excellence we have at the Institute. He is, as an example, championing an exciting project with the Bank of England, which engages our young researchers in an interdisciplinary project on a commercial issue. I would also like to add my personal congratulations to Terry on the announcement that he is to be next president of the London Mathematical Society, a highly regarded scholarly role that he will take up in

January 2014 alongside his position as Director of OMI.

10 01 11 02 03

TIM JENKINSON ON Private equity – looking to the future The he rise of private equity was one of biggest shifts in the financial landscape of the 2000s, but the banking crisis brought the industry’s fortunes to an uncomfortable close. It can be hard for outsiders to follow developments in this relatively opaque market, but Professor Tim Jenkinson’s research is helping make sense of a turbulent period.

and the price paid. In theory a company’s value shouldn’t depend on

A member of the Oxford-Man Institute (OMI), Tim Jenkinson is also

Investors are now casting a more sceptical eye over funds before

head of the finance group at the Saïd Business School (SBS) and director

entrusting their money to them. “They realise that when all the stars

of the Oxford Private Equity Institute, set up with OMI’s initial support.

were aligned – for example, if you were investing in 2003-4, you almost

Alongside fellow OMI researcher Ludovic Phalippou, he teaches SBS

couldn’t help making money,” Jenkinson says. “But now the stars are

courses on Private Equity and Entrepreneurial Finance, and says the

far from aligned, it’s a much harder environment to raise funds in.”

industry still attracts strong student interest despite the downturn.

how easy it is to borrow money. But the credit bubble distorted the industry’s decision-making, leading firms to overvalue their targets.

“We were surprised to find how much everything was being influenced by the banks and their willingness to lend,” Jenkinson comments. “This is the private equity industry, yet it’s being driven by debt!”

Of course, private equity wasn’t the only asset class to experience a

The leveraged buyout market grew explosively throughout much

bubble. And despite inflated deal prices, the industry’s returns still

of the last decade, with a buoyant economy driving fast corporate

compare favourably with those of public stocks and other assets

growth, banks eager to lend and investors queueing up to give

over the period. Those who invested steadily would have had major

private equity firms their money. Many new funds were launched;

successes alongside the duds. Other research by Jenkinson and his co-

competition for targets steadily increased, and the prices the winners

authors finds that, on average, leveraged buyout funds produce annual

were paying soared.

returns around 3-4% higher than the public market over their lifetimes.

When markets crashed in 2007, many stressed banks found themselves dependent on state support. They responded by cutting new lending sharply; over the next couple of years, private equity activity shrivelled. “As the banks entered the intensive care ward, funds found themselves sitting on cash but unable to find the debt finance they needed to buy companies out,” Jenkinson explains. One result has been the realisation that what looked like extraordinary investment skill in the boom years was often really a matter of good luck and favourable conditions. Recently Jenkinson coauthored a paper, to be published in The Journal of Finance, which explores the problems the crash exposed in private equity. Entitled ‘Borrow cheap, buy high’, it shows how cheap debt increased both the number of leveraged buyouts

“It’s certainly been an interesting time for the industry. By the mid-2000s, the credit bubble meant deals were being done that only made sense if you assumed the permanent availability of cheap debt. In many cases there was a lot of buyer’s remorse. People who bought at the top of the market paid so much that it’s almost impossible for them to make money.”

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TIM JENKINSON S If funds targeting mature companies have performed better than

are reducing the range of sectors they invest in, focusing on a few

you might expect, though, the other end of the market has been

core areas of expertise. This should lead to more discriminating

squeezed hard. Venture capital investors used to dominate private

acquisitions and better management.

equity. They led the technology boom, financing many startups that went on to become wildly successful. But the dotcom crash put paid to this brief heyday, and the market has stagnated ever since, squeezed at the top by larger buyout funds and at the other end of the market by angel investors.

Jenkinson says there’s little sign of the market returning, despite the unique opportunities it offers. “If you want to access the next Zynga or Facebook, you need to be in venture capital,” he explains. “But for every Facebook there are a thousand disappointments.” Venture capital has shrunk considerably in the last decade; it’s now a small niche in the market. Jenkinson’s research has investigated how this has affected returns. If the supply of capital chasing a finite number of opportunities falls,

dominate private equity.

“If you buy a company now, you need to do something real to improve it; you can’t rely on financial engineering and a benign environment,” Jenkinson says. “More than ever before, investors are trying to evaluate whether private equity firms can work with managers to make companies better.”

Ultimately, Jenkinson thinks the industry will prosper despite its

That’s not the only way the industry could be set to play a more

setbacks. “Investors still need to find ways to make inflation-beating

valuable role than it has so far. Already some

returns,” he points out. “That’s difficult, particularly with stagnant

firms are starting to move into emerging

stock markets and very low interest rates. They’re evaluating funds

markets, and Jenkinson expects this

more carefully, but they’re still looking to allocate a sizeable amount

to continue, as companies look

of their portfolios to private equity.”

beyond illiquid local equity

you might assume that less competition would mean more profit for the remaining players. But you’d be wrong. The results show that returns haven’t improved at all, either in absolute terms or relative to public markets. It’s not hard to see why providing growth capital and funding leveraged buyouts of established companies has come to

Moving into this new world will be painful for some. “It’s been a once-in-20-year shakeout of the industry,” he notes. “The strong will emerge stronger, but the weak won’t raise money again. Those who can’t point to a successful track record will disappear.” The new, more sober approach could have wider benefits. Because they can’t rely on cheap debt and economic growth to provide a

markets to get the funds they need to expand.

“If you want to invest in future African growth, for example, private equity is a very efficient way to do it. It’s quite possible that this is going to be a far more important source of finance for African growth than public capital markets.”

profitable exit, private equity investors are having to focus more on improving the companies they buy, working closely with

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management to make them more efficient and profitable. Many

10 01 11 04 03

DIAA NOURELDIN Tracking the markets at high frequency The state of the financial markets in recent years has made life difficult for everyone, wrongfooting traders and investors with abrupt shifts of direction. But Dr Diaa Noureldin reckons our ability to follow a fast-changing situation in the present has been impeded by an excessive focus on the past. His work can help.

of the markets had to content themselves with prices sampled just once a day. Now they can draw on vast datasets containing the prices of thousands of assets throughout each day’s trading. Looking at prices minute-by-minute, or even at millisecond frequency, turns out to allow previously impossible new insights; Noureldin has shown it provides unprecedented accuracy in forecasting not just individual assets’ volatilities, but also the correlations between them. The latter has become a topic of intense interest in the markets over

Diaa Noureldin joined the Oxford-Man Institute (OMI) in late 2011 as

recent years; part of what made the financial crash so devastating

a postdoctoral research fellow. With a background in econometrics,

was the way assets that were seen as independent of each other

he’s interested in modelling and forecasting the behaviour of

turned out to move in lockstep when trouble hit. But it’s only recently

complex systems such as large portfolios of assets. He specialises in

that researchers have started to provide a solid mathematical basis

an area known as dynamic modelling of multivariate volatility, which

for understanding it.

involves analysing both the volatility of individual asset returns as well as their correlations.

Users like risk managers have already started to use high-frequency data to improve their forecasts of the behaviour of individual assets. But the industry has made less progress so far on bringing them to bear on correlations, and this is precisely where Noureldin’s research sheds new light. “It turns out that incorporating high-frequency data into your forecasts gives really significant gains in accuracy,” he says. The work’s potential for tracking rapid shifts in market behaviour could prove just as valuable as its benefits in long-term forecasting. “After the financial crisis there’s been a lot of interest in how to estimate abrupt changes in assets’ volatility,” Noureldin explains. “We’ve found that using high-frequency data lets our models adapt more quickly to these changes in market conditions, so they are much better in turbulent periods. Models that look only at daily data adapt less

Understanding these problems involves mathematical tools called variance/ covariance matrices. And like many seemingly recondite techniques pursued by OMI researchers, these have very practical applications. For instance, they can be used to choose

quickly, because they put relatively more weight on the distant past.” Models of market volatility are usually premised on the idea that it is persistent – if prices move violently today, they are more likely to do so again tomorrow. All markets show these patterns – they have periods of calm and periods of turbulence.

a risk-minimising portfolio from a large number of assets, and have

But even seemingly quiet times can unexpectedly burst into violent

direct applications in pricing assets and managing risk.

activity, and looking at infrequent data makes these anomalies harder

Noureldin is providing new insights by collaborating with fellow OMI researchers to develop dynamic models that use high-frequency data to forecast the behaviour of financial assets. Not long ago, observers

to spot. Noureldin cites the May 2010 ‘flash crash’, in which the US stock market plunged nearly 10%, then started to rebound quickly, regaining much of the lost ground within minutes. Looking only at daily data would give the impression of placid trading conditions; by

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DIAA NOURELDIN contrast, high-frequency prices show volatility soaring to extreme levels that day. Beyond helping introduce high-frequency data to volatility forecasting, Noureldin has been working to develop new mathematical techniques to make complex problems in this area easier to approach. His aim is to relax some of earlier models’ assumptions, such as ‘stationarity’ – the idea that volatility and correlation are stable over the long term, reverting to their mean values. That isn’t always true, and his research tries to incorporate the awareness that levels of volatility and correlations between assets change over time under the influence of outside factors such as prevailing macroeconomic conditions.

“We know the world isn’t stationary,” Noureldin says. “The classic assumption is that the parameters of the model we use to forecast the market are stable over time. But as we moved into 2007, the long-run equilibrium level of volatility abruptly increased. After the collapse of Lehman Brothers in September 2008, we were suddenly looking at a very different regime of volatilities and correlations. We are trying to find ways to track these changes accurately over time.”

ideally you’d like to understand all of them,” he explains. “But mathematically modelling this is hugely difficult – it’s a problem with hundreds or thousands of dimensions. We’ve taken models that are popular with market practitioners and tried to introduce more flexible dynamics while avoiding the curse of dimensionality. This means the number of parameters in our model rises linearly rather than exponentially as the complexity of the problem increases.” Noureldin works at the cutting edge of applying the insights of financial econometrics to real-world problems, and he doesn’t expect to have any shortage of interesting areas to work on over the next

Better and more frequent price data is part of the answer. But

few years. Taming these problems isn’t going to happen overnight.

working with such large volumes of information is inherently

But if it means we’re better prepared for the next financial crisis than

challenging. Modelling the volatility of and correlation between

for the last, it’ll have been worth the wait.

different assets involves working with larger and larger variance/ covariance matrices, requiring the analysis of more and more parameters. Before long the task becomes computationally intensive and, in practice, intractable. Much of Noureldin’s contribution has of looking at very complex systems without falling foul of this problem, which researchers call the ‘curse of dimensionality’. “A portfolio could potentially contain many, many different assets, and

“A portfolio could potentially contain many, many different assets, and ideally you’d like to understand all of them. But mathematically modelling this is hugely difficult – it’s a problem with hundreds or thousands of dimensions.”

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been in working out ways

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JOHANNES RUF UF Of markets and martingales Imagine you’re at the roulette wheel. You have an even chance of winning or losing, and you start by betting $1. If you win, you stop playing with a $1 profit. If you lose, you bet $2. Win, and you again stop playing a dollar ahead. Lose again and you’re now down $3, so you go back and bet $4. You keep doubling your bets until one of them finally pays off and you walk away with $1. Financial mathematicians call these situations ‘martingales’. A true

risk in a financial asset as a local martingale, what is a fair price for a derivative whose price depends on that asset? If a bank sells a call option conferring the right to buy $1 for £0.70 at the start of 2013, how much should it charge to avoid taking on unwanted risk? The answers could help prevent unexpected losses in volatile periods, so this is an area in which the finance industry is paying close attention. Other researchers have tackled the problem in situations where the underlying asset’s behaviour is described in terms of martingales, but not if it involves local martingales. Ruf is working to generalise the theory so it applies there too.

martingale is a fair game, in that if you play for long enough you’ll

One recent project studied option pricing in the foreign exchange

come away with no more or less than you started with. But the

market. Lately what are called Quadratic Normal Volatility Models

strategy described above is the opposite – a game that looks fair but

have gained favour with the finance industry, proving very flexible

isn’t, a ‘local martingale’. If you can afford to keep doubling your

and easy to use while also closely matching prices seen in the

bets until you win – a big if – you’ll always end up with a small profit.

market. Ruf and his co-authors

Paradoxically, at every step the game is fair – each bet has an equal chance of winning or losing. But the overall picture isn’t fair at all – over the long game, you’re sure to come out ahead. Every true martingale – every game that’s fair over the whole time horizon being considered – is also a local martingale – each move looks fair. But the reverse isn’t true; being locally fair doesn’t make a game fair overall. Until recently true martingales have got much more academic attention, but Dr Johannes Ruf is working to change that. The two kinds are often easy to tell apart in formal mathematical terms, but not generally by looking at data. For example, a stock bubble can be seen as a local martingale – it looks like a fair game but isn’t, as those buying into it will probably lose. But until they burst, bubbles are notoriously hard to spot just by looking at prices. These deceptively simple ideas are central to Dr Johannes Ruf’s research at the Oxford-Man Institute (OMI), illuminating phenomena across the financial markets. “You can model stock prices as having two separate components,” Ruf explains. “The overall trend, which is generally positive, and which is usually easy to handle, and the element of intrinsic risk – will the price fluctuate, and by how much? Until now, most research has assumed this can be modelled as a martingale, but that’s a big assumption. My work weakens it by assuming that the risk is only a local martingale.” Ruf’s been working to apply these concepts to address fundamental questions about hedging and pricing derivatives. If we model the

discovered mathematical tricks that let them improve these models, incorporating insights on local martingales to let them deal with the possibility of

These deceptively simple ideas are central to Dr Johannes Ruf’s research at the Oxford-Man Institute (OMI), illuminating phenomena across the financial markets. “You can model stock prices as having two separate components,” Ruf explains.

10 01 11 04 07

JOHANNES RUF R hyperinflation – the total devaluation of one currency versus another. Until now, the mathematics used in standard asset pricing models meant assuming such hyperinflation couldn’t happen.

Local martingales appear in many different financial models, not just in those for pricing FX options. “We’re pretty good at mathematically describing asset prices in one dimension, looking at one or two stocks at a time for example,” Ruf explains. “But we’re less good at describing the behaviour of systems of assets, like all the stocks in the market.” It’s possible to model this as simply a collection of single stocks, but then the system as a whole tends to start behaving strangely, like one stock completely dominating all the others. “You never see that in reality – at most the biggest stock will be worth about 10% of the market,” Ruf says. “So we know those models are wrong.” An alternative approach is to look for ways of modelling the interactions between the markets’ different components. This area is called ‘Stochastic Portfolio Theory’, and professionals use it to understand very complex questions like the best mix of assets for a mutual fund, say, or how to hedge the liabilities incurred by derivatives trading desks. Once again, local martingales turn out to play a vital role, on which Ruf’s research is casting new light. His interests go far beyond martingales and stochastic portfolio theory, taking in sidelines like the theory of economic learning – how different strategies spread through a group of agents. For instance, imagine a population of 10,000 individuals, all of whom make a

No stranger to life in the finance industry, Ruf has experienced the divide between theory and practice from both sides. Before joining OMI in 2011 as a senior research fellow after finishing his PhD at Columbia University in New York, both his Master’s and doctoral theses won prestigious industry prizes, including the Morgan Stanley Prize for Excellence in Financial Markets, and he has worked as an intern at several major investment banks. Indeed, he doesn’t rule out the possibility of returning to the industry at some point. For the moment, though, the sociable and highly interdisciplinary environment of the OMI has its own attractions.

single action every day, with the chance of a reward. Each gets to see use this information in making their next decision.

Specialists in ‘belief-free learning’ investigate questions like how agents should behave in such situations – how can they get the best rewards and the least risk? For example, one simple approach would be to copy whichever of the last two actions seen got the better results. Which rules lead to the best decisions? Such questions are studied in simplified theoretical terms, but they can help us understand complex problems like how market participants choose their investment strategies based on incomplete knowledge of what others are doing.

“Academia isn’t as fast-moving or as well paid, but there’s an amazing amount of freedom to pursue whatever subjects interest me, and it’s great to be surrounded by experts in so many different disciplines. It’s not exactly less work though – I’ve discovered that academics don’t get weekends!”

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what one other person does, and the results they achieve, and can

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MEMBERS Arnaud Doucet

Georg Gottlob

is a Professor in the Department of Statistics, University of

is a Professor of Computing Science, University of Oxford.

Oxford. He completed a PhD in Information Engineering at

His research interests are database theory, web information

University Paris-Sud Orsay. Before joining Oxford University,

processing and theoretical computer science. At OMI, he

he previously held faculty positions at Cambridge University,

researches data exchange, semantic database and web

the University of British Columbia and the Institute of

querying, and automatic web data extraction for betting

Statistical Mathematics in Tokyo.

and quantitative finance.

He is particularly interested in the development of Monte Carlo

He was a Professor at the University of Technology, Vienna from

methods for statistical inference in complex stochastic systems. He

1988-2005, where he still holds an adjunct appointment. Before

has worked extensively on Sequential Monte Carlo methods, also

that, he was affiliated with the Italian National Research Council

known as Particle Filters, Markov chain Monte Carlo methods and

in Genoa, Italy, and with the Politecnico di Milano, Italy. He has

their applications to non-linear non-Gaussian time series.

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

Mike Giles

of Sciences, the German National Academy of Sciences, and the

is a Professor of Scientific Computing at the Mathematical

European Academy of Sciences Academia Europaea in London.

Institute, University of Oxford where he is a member of the Mathematical and Computational Finance Group. He read mathematics at Cambridge before completing a PhD

Greg Gyurko

in Aeronautical Engineering at Massachusetts Institute of

joined OMI in 2007 as one of the first student members of

Technology (MIT).

the Institute. He obtained a DPhil at the University of Oxford

He was an Associate Professor at MIT before moving to Oxford in 1992 to join the Computing Laboratory at the University. After working closely with Rolls-Royce for many years developing

and is currently a Departmental Lecturer in the Mathematical Institute, where he is a member of the Mathematical and Computational Finance Group.

computational fluid dynamics techniques, he moved into the

Greg is the course director of the MSc in Mathematical and

development of Monte Carlo and finite difference methods

Computational Finance, and is actively involved in organising the

in computational finance, which led to his transfer to the

Practitioner Lecture series and the Mathematical Finance Internal

Mathematical Institute in 2008. In 2007 he was named ‘Quant

Seminar series. Greg’s research interests relate to the theory and

of the Year’ by Risk magazine, together with Paul Glasserman

applications of Rough Paths Theory, as well as the development

of Columbia Business School, for their joint work on the use of

and software implementation of probabilistic numerical methods

adjoints for the efficient calculation of Monte Carlo sensitivities.

for approximating solutions to stochastic differential equations and

More recently, he has developed the multilevel Monte Carlo

certain types of partial differential equations.

method for the pricing of financial options, and is active in the exploitation of GPUs (graphical processing units) for high

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mike

performance computing in a variety of application areas.

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MEMBERS Ben Hambly

Chris Holmes

is a University Lecturer in Mathematics at the Mathematical

is a Professor of Biostatistics in the Statistics Department at

Institute where he is a member of the Mathematical and

the University of Oxford. He holds a ‘Programme Leaders’

Computational Finance Group and the Stochastic Analysis

award in Statistical Genomics from the Medical Research

Group. He has a PhD from the University of Cambridge and

Council and was awarded the Royal Statistical Society’s Guy

previously had lectureships in Edinburgh and Bristol. He is

Medal in Bronze in 2009.

Co-editor in Chief of Applied Mathematical Finance.

Chris’ research is focussed on Bayesian methods and computation

His research interests in mathematical finance are in the modelling

for high-dimensional inference problems, in particular, analysis

and pricing of financial derivatives. In particular he has worked on

techniques for sequential data structures arising in bioinformatics,

electricity spot price models and the pricing of complex derivative

statistical genetics and genetic epidemiology. Within OMI he has

contracts in energy markets. He is also interested in credit markets

ongoing projects with Mike Giles on graphical processing unit (GPU)

and the pricing of large portfolio credit baskets contracts. His other

implementation of Monte Carlo methods for dynamic inference

research interests include random walks and diffusion in random

problems, and Stephen Roberts on Bayesian Nonlinear Models.

and fractal environments, rough paths, branching processes,

Chris studied for his PhD in Bayesian Nonlinear Methods within the

random matrices and particle systems.

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

Vicky Henderson

Society’s biennial ‘Research Prize’ for his work in Bayesian statistics.

is a Senior Research Fellow at OMI and is affiliated with the Mathematical Institute, University of Oxford. Previously a Reader in the Finance Group at Warwick Business School, Vicky

Sam Howison

held positions at Princeton University, ETH Zurich, and spent six

is an applied mathematician working in the Mathematical

months at the Isaac Newton Institute, University of Cambridge.

Institute, University of Oxford, of which he is currently

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

Chairman. 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.

which have been applied to problems in real and executive stock options. Recently, Vicky has studied optimal stopping problems

chris

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

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ben

2010 Quantitative Finance program at the Fields Institute, Toronto.

10 01 11 09 10 03

MEMBERS Hanqing Jin

Gechun Liang

completed his PhD in Financial Engineering in 2004 at the

joined the Oxford-Man Institute as a Postdoctoral Research

Chinese University of Hong Kong. He is a university lecturer

Fellow in the Michaelmas Term of 2010. Prior to that, he

at the Mathematical Institute, is on the editorial board of

was a student member of OMI whilst completing a DPhil

Mathematical Methods of Operations Research and is also

in Mathematics at the Mathematical Institute under the

a member of the Mathematical and Computational Finance

supervision of Professor Terry Lyons and Dr Zhongmin Qian. He

Group at the University of Oxford.

has a Master’s Degree in Mathematics from Tongji University, and studied finance as an undergraduate in Jilin University.

His research interests include portfolio selection, behavioural finance, applied stochastic analysis and optimisation. He has

His research interests are mainly focused on mathematical finance

previously worked on stochastic control, portfolio selection with

and applied probability. He is especially interested in backward

transaction costs and behavioural portfolio selection. He is currently

stochastic differential equations and credit risk modelling.

working on time consistency of dynamic decisions.

Terry Lyons

Shin Kanaya

is the Director of the Oxford-Man Institute. He is the Wallis

is a Postdoctoral Research Fellow at the Department of

Professor of Mathematics at the University of Oxford, a

Economics, University of Oxford. He earned a Bachelor’s and

Fellow of the Royal Society and one of the UK’s leading

Master’s degree from the University of Tokyo, majoring in

mathematicians, having made a number of contributions to

economics, and a PhD in Economics from the University of

stochastic analysis.

Wisconsin-Madison in 2008.

His interest in stochastic analysis relates particularly to the control

His primary field is financial and time-series econometrics, with an

of non-linear systems driven by rough paths. Prime examples of

emphasis on nonparametric testing and estimation problems of

such systems are provided by stochastic differential equations and

continuous-time economic and financial models. He is currently

stochastic systems.

working on the following projects: nonparametric testing of

His research on ‘rough paths’ has founded a new field, stimulating

the stationarity for continuous-time Markov processes, and

an enormous amount of work, allowing breakthroughs in many

nonparametric estimation for mixed frequency time series data.

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

terry

gechun

hanqing

shin

methods as new tools allowing more efficient numerical modelling.

10 01 11 10 04 11

MEMBERS MB José Martinez

Diaa Noureldin

is a Lecturer in Finance at the Saïd Business School. He obtained

is a postdoctoral researcher in Economics. He is interested in

his PhD from Columbia Business School. Before joining the

financial econometrics, particularly modelling and forecasting

University of Oxford he was a Visiting Researcher at the Institute

volatility and dependence in financial time series.

for Financial Research in Stockholm, Sweden. José specialises in capital markets, investments and investor behaviour.

He is interested in developing methods suitable for large dimensional systems and high-frequency data. Diaa previously studied for an

His research explores the role of information sellers in financial

MPhil in Economics at the University of Oxford, and holds a BA

markets and the use investors make of their financial advice. He is

and MA in Economics from the American University in Cairo. In

also interested in the differences exhibited by pension and mutual

Michaelmas 2011, he joined the Department of Economics at the

fund investors and is currently working on understanding how

University of Oxford as a Postdoctoral Research Fellow.

capable individuals are of managing their retirement accounts.

Jan Obłój

Sergey Nadtochiy

is a University Lecturer at the Mathematical Institute, University

is a Senior Postdoctoral Research Fellow at OMI. His research

of Oxford where he is a member of the Mathematical and

interests lie in the field of financial mathematics, specifically

Computational Finance Group. Before coming to Oxford he was

the applications of stochastic and functional analysis for the

a Marie Curie Postdoctoral Fellow at Imperial College London.

pricing and hedging of financial derivatives.

He holds a PhD in Mathematics from the University Paris IV and

His current research is concerned with the construction of

Warsaw University. His general interest is in mathematical finance

so-called ‘market models’ – the financial models that are designed

and its interplay with probability theory, and he looks at a number

to be permanently consistent with the prices of the liquidly traded

of different problems where tools from martingale theory and

derivatives. In addition, he has done work on static hedging;

stochastic analysis can be applied.

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.

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, hedge-funds managers’ incentive schemes and inverse problems for utility maximisation.

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jan

sé

10 01 11 09 12 03

MEMBERS Han Ozsoylev

Tarun Ramadorai

is a Lecturer in Financial Economics at the Saïd Business

is Professor of Financial Economics at the Saïd Business

School. Before joining the University of Oxford, he earned his

School. Tarun has a BA in Mathematics and Economics from

PhD in economics from the University of Minnesota and BSc

Williams College, an MPhil in Economics from Emmanuel

in Mathematics from Bilkent University. He has held visiting

College, Cambridge, and a PhD in Business Economics from

appointments at the University of California, Berkeley and

Harvard University.

Johns Hopkins University.

He is also a Research Affiliate of the Centre for Economic Policy

Han’s research primarily focuses on financial market imperfections,

Research, London. He has published papers in journals such as

such as those generated by asymmetric information, imperfect

the Journal of Finance, The Journal of Financial Economics and

competition, behavioural biases, and bounded memory. He has

The Review of Financial Studies. His main areas of interest are

studied information sharing amongst stock market investors and, in

capital markets, international finance and hedge funds. His current

particular, how social and information networks affect asset prices

research deals with two main topics: the impact of international

and investor welfare. He is also interested in questions related to

investment flows on equities and foreign currencies in a range of

financial fragility, liquidity and market manipulation.

countries; and the performance, riskiness and capital formation processes of hedge funds. He has taught courses on international

Ludovic Phalippou has been a Lecturer in Finance at the Saïd Business School

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.

since January 2011. He holds degrees from INSEAD (PhD in Finance), the University of Southern California (Master’s in both Mathematical Finance and Economics), and Toulouse University (BSc in Economics).

Ludovic’s research is mainly on private equity funds and has

received considerable attention from the professional and academic investment community alike. Several major newspapers echoed his findings such as the Financial Times and The Economist. He has received several best paper awards and research grants. His research has been presented at the best academic conferences and at seminars in prestigious universities. His research has been published in top academic and practitioner journals: Journal of Finance, Review of Financial Studies, Journal of Economic Perspectives, Harvard Business Review, Review of Finance and Financial Analyst Journal. Ludovic is ranked in the top 100 worldwide by SSRN.com out of all business school researchers for the number of downloads of his articles,

taru

Norway sovereign wealth fund - one of the largest investor worldwide.

Ludovic has also consulted for several companies, of which the GPGP the

ludo

vic

and has been selected in the Speaker Retainer Program of the CFA institute.

10 01 11 04 10 13

MEMBERS Iead Rezek

Steve Roberts

is a senior research fellow with the Pattern Analysis Research

is Professor of Information Engineering at the University

Group at the Department of Engineering Science, University

of Oxford. He studied physics, completed a PhD in Signal

of Oxford.

Processing and was appointed to the faculty at Imperial College

His research interest is in statistical modelling of complex and mostly physiological and biological systems. His particular focus is

London, before taking up his post in Oxford in 1999. He heads the Pattern Analysis and Machine Learning Research Group.

on creating feasible, scalable and interactive statistical methods, i.e.

His main area of research lies in machine learning approaches to data

methods that scale well with data, inference methods that scale with

analysis. He has particular interests in the development of machine

model complexity and decision processes that interact with the user.

learning theory for problems in time series analysis and decision theory. Current research applies Bayesian statistics, graphical models and

Johannes Ruf is a Senior Research Fellow at OMI. Before joining, he completed his PhD under the supervision of Ioannis Karatzas at Columbia University in New York.

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.

One of his main research interests is Stochastic Portfolio Theory. He has studied the hedging of derivatives and completeness of financial markets in models that allow for arbitrage, and is currently working on modelling forward utility, the hedging of foreign exchange options in situations of exploding exchange rates, and financial models under uncertainty. Besides working on a variety of topics in Quantitative Finance, Johannes has done research in Economic Learning Theory and the analysis of sparse social network data. Both his master and PhD thesisâ&#x20AC;&#x2122; won prestigious industry awards (DZ-Bank Karrierepreis and Morgan Stanley Prize for Excellence in Financial Markets) and lead to scientific publications.

steve

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Commerzbank, d-fine, JPMorgan and Morgan Stanley.

Iead

valuable experience in the financial industry through internships at

johan

Association, and a Teaching Award at Columbia University. He gained

Johannes won various scholarships including one by the Fulbright

10 01 11 09 14 03

MEMBERS Neil Shephard

Mungo Wilson

is Head of Financial Econometrics and Statistics at OMI and

is a Lecturer in Financial Economics at the SaĂŻd Business School.

a Professor of Economics at the University of Oxford. He is a

His research interests include determinants of expected

Council Member of the Society of Financial Econometrics and

returns, credit risk, mutual funds and portfolio allocation.

an Associate Editor of Econometrica. Neil is a member of the advisory boards of Research Centres at Aarhus University and Singapore Management University. His research interests

Thaleia Zariphopoulou

are mainly focused on econometrics â&#x20AC;&#x201C; particularly working with high

is the first holder of the Man Professorship of Quantitative

frequency data to try and understand financial volatility and time

Finance and is a member of the Mathematical Institute, the

varying dependence, market microstructure and the role of jumps in

University of oxford. Her area of expertise is in financial

financial markets. He is also interested in the use of simulation to carry

mathematics, quantitative finance and stochastic optimisation.

out econometric inference. He was an undergraduate at York studying

Her research interests are in portfolio management, investment

economics and statistics. He has carried out graduate work and taught

performance measurement and valuation in incomplete markets.

at LSE. He was elected a Fellow of the Econometric Society in 2004 and a Fellow of the British Academy in 2006.

Xunyu Zhou

Kevin Sheppard

is the Nomura Chair of Mathematical Finance and Director

is a University Lecturer in the Department of Economics.

University of Oxford. He obtained his PhD at Fudan

His research interests focus on financial econometrics.

University in 1989. He currently focuses on the mathematics

He has carried out work on estimating large dimensional

of behavioural finance.

of the Nomura Centre for Mathematical Finance at the

time-varying covariance matrices and has recently focused

Prior to joining the University of Oxford he was Chair of Systems

on the use of high frequency data to more precisely

Engineering and Engineering Management at the Chinese University of

estimate dependence amongst asset returns. Kevin was an

Hong Kong. His general research interests are in quantitative finance,

undergraduate at the University of Texas at Austin and did

stochastic control and applied probability, while he has recently engaged

his PhD at the University of California, San Diego.

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.

tha

xuny

kevi

neil ia

ngo

10 01 11 10 15 04

STUDENTS Bahman Angoshtari

Youness Boutaib

is a third year DPhil student in the Mathematical Institute,

is a DPhil student in the Stochastic Analysis Group, working

University of Oxford. He holds an MSc in Applied Mathematics

with Professor Terry Lyons has drawn his attention to the

from the University of Twente and a BSc in Industrial

power of the theory of rough paths.

Engineering from Sharif University of Technology, Iran.

The theory, along with giving the appropriate frame of solving

His research interests lie in the application of stochastic analysis

equations driven by very irregular signals (like the fractional

and control theories in finance, especially in portfolio choice. He

Brownian motion), encompasses the previous theories of

is currently focused on identifying the optimal investment strategy

integration (Stieltjes, Young and Stratonovitch). He aims to develop

in a market with co-integrated assets. The results are directly

a control theory based on it that would help solve optimisation

applicable to pairs-trading, and possible extensions to statistical

problems of systems that are ruled by differential equations driven

arbitrage are under investigation.

by rough paths. Applications naturally include finance and quantum physics and other older classic problems.

Tigran Atoyan is a DPhil student at the Mathematical Institute’s

Karolina Bujok

Mathematical and Computational Finance Group and a

is a fourth year DPhil student in the Mathematical Institute at

member of OMI. He received his undergraduate and Master’s

the University of Oxford. She has a Master’s degree in Financial

degrees from McGill University in Montreal, Canada. His

Mathematics from King’s College London (2008) and a Master’s

Master’s thesis topic touched upon parametric inference of

degree in Quantitative Methods in Economics and Information

Lévy processes using Fourier transform methods, which was

Systems from Warsaw School of Economics (2005).

applied to asset pricing. Other topics covered during the Master’s research were stochastic volatility modelling and implementations of EM algorithms in finance.

Her general interest is in building mathematical finance models, which have convincing economic interpretation, and developing efficient and robust numerical methods to make the models useful

Tigran is currently looking into applications of Brownian motions

in practice. She is currently working on basket credit derivatives

evaluated at random times in asset pricing and in option pricing.

and building a multidimensional structural model that captures

He is particularly interested in the incorporation of historical asset

behaviour of a portfolio of companies underlying the derivative.

prices into the pricing and hedging of options with the goal of

Each company asset value is driven by a jump diffusion and the

merging the statistical and probabilistic approaches to option

firms are correlated via common factors. The joint distribution of

valuation into one coherent framework.

a portfolio is found as a limit of an empirical distribution, which is given as a stochastic partial differential equation. Karolina’s supervisor is Christoph Reisinger.

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k a roli

youness

bahman

tigran a

10 01 11 09 16 03

STUDENTS Sylvestre Burgos

Martin Gould

is studying for a DPhil in Mathematics within the

is a third year DPhil student in Mathematics. He holds an MASt

Mathematical and Computational Finance Group at the

(Part III) in Mathematics from the University of Cambridge and

University of Oxford. He holds a BSc in Mathematics from

a BSc in Mathematics from the University of Warwick.

the University Paris VI, an MSc in Engineering from the École Centrale Paris and an MSc in Mathematical and Computational Finance from the University of Oxford.

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

Sylvestre’s research interests lie broadly in the field of numerical

exchange markets. He hopes to be able to extend his model to gain

methods for computational finance. His research under the

insight into how prices are affected by the release of macroeconomic

supervision of Mike Giles focuses on the computation of Greeks

news by central governments and to examine how changes in limit order

with Multilevel Monte Carlo simulations.

arrival flows propagate through the network of different currency pairs.

Vladimir Cherny

Ni Hao

is a third year DPhil student at the Mathematical Institute,

is a third year student in the Stochastic Analysis Group at the

University of Oxford. His research interests lie broadly

University of Oxford. Ni previously completed a Bachelor’s

in stochastic analysis and optimisation theory with their

Degree in Mathematics at Southeast University, China and a

applications to mathematical finance.

Master’s Degree in mathematical and computational finance

He is working under the supervision of Jan Obłój on implementing

at the University of Oxford.

methodology of Azema-Yor processes for different optimisation

She is currently working on rough paths theory with her supervisor

problems in mathematical finance, such as long-term expected

Professor Terry Lyons, and her research interest is the expected

utility growth rate maximisation subject to drawdown constraint.

signature of stochastic processes.

Xi Geng

Jan Hendrik Witte

is a first year DPhil student in the Stochastic Analysis Group at the

is a DPhil Student at the Mathematical Institute, University

Mathematical Institute, University of Oxford. His supervisor is Dr.

of Oxford. His research interests are Numerical Mathematical

Qian Zhongmin. He studied mathematics as an undergraduate at

Finance. Right now, Jan is looking at the numerical solution

Sun Yat-sen University in China. In 2009, he organised a research

of various non-linear equations arising in finance.

group for a one year research project on the topic “Micro and Macro Analysis for Chinese Welfare Lotteries”, in which the group

martin

In 2010, he wrote a research paper on “Helly’s Theorem for Vector-

dimir

rational game strategies and the prediction of sale amount.

vla

developed original models and techniques for the construction of

valued Measures” using the techniques in functional analysis and topology. After graduation with first class honours from Sun Yatobtained the studentship from OMI. His DPhil research area is multi-dimensional calculus of variations, nonlinear PDE and Riemannian geometry via methods in stochastic analysis,

especially in Malliavin Calculus.

jan

s y l v e s t re

sen University, he was admitted into the University of Oxford and

10 01 11 10 17 04

STUDENTS Richard Hills

Mathias Kruettli

is a Dphil student in Financial Economics at the Saïd Business School.

is a DPhil student in economics. He holds an MPhil in

He has an MPhil in Finance from the University of Cambridge,

Economics from the University of Oxford. Before coming to

and a Masters in Engineering, Economics and Management

Oxford, he has studied at the University of Zurich and NYU.

from the University of Oxford. He worked for two years in the

Mathias is interested in the prediction of the equity premium.

CDS risk management technology team at Morgan Stanley.

He uses a Bayesian procedure from the macroeconometrics literature,

Richard is conducting theoretical research into areas related to market

which takes priors from DSGE models, and tries to adapt it to asset pricing

liquidity, specifically price impact risk and its effects on asset pricing

models and the equity premium prediction. Further, Mathias analyses the

and risk sharing. He is especially interested in models of agents with

cross-border transmission mechanisms of crises initiated by sovereign debt

ambiguous beliefs, and models of asymmetric information.

markets through a gaussian dynamic term structure model.

Thomas Hosking

Arnaud Lionnet

is a third year DPhil student at the Mathematical Institute

is a DPhil student at the University of Oxford. His interests

and a member of the Mathematical and Computational

in mathematics include functional analysis and probability

Finance Group, University of Oxford. He studied Actuarial

theory and he is very interested in complex and dynamical

Sciences in Australia before completing an MSc in

systems, especially when they involve randomness (markets,

Mathematical and Computational Finance at Oxford.

population evolution, meteorology, etc).

His research is primarily focused on dynamic games of exhaustible

More specifically he is interested in stochastic differential equations,

resources, as well as numerical techniques used in solving

Malliavin’s calculus and rough paths. He specialises in backward

differential games.

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

Sigrid Källblad

mathematical finance on the other (option pricing, risk measures).

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

Kasper Lund-Jensen is a DPhil student in Economics at Nuffield College, University of Oxford. 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. Kasper’s research interests lie in the areas of financial econometrics

r i c h a rd

sigr

and economic forecasting. Currently, his research is focused on outof-sample equity premium predictability and combination forecasts.

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10 01 11 09 18 03

STUDENTS Cavit Pakel

Michael Streatfield

is interested in the field of financial econometrics and, specifically,

is interested in hedge funds and investment management.

in volatility modelling. He is also interested in the nuisance

He is a third year doctorate student supervised by Tarun

parameter issue and bias reduction in the likelihood framework.

Ramadorai. In his research work he has been analysing the

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,

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.

this structure makes it possible to model volatility using a smaller than

His future research involves analysing the impact of the recent crisis

usual number of observations in the time-series dimension.

on hedge fund reporting. Prior to his DPhil, Michael worked in the investment industry for 15 years in London and South Africa.

Daniel Schwarz is a DPhil student at the Mathematical Institute and a member

Maria Tchernychova

of the Mathematical and Computational Finance Group,

is a DPhil Student in Mathematics at the Mathematical

University of Oxford. Previous he obtained a Master of

Institute, University of Oxford. Her research interests are

Mathematics (MMath) degree from the University of Oxford.

Gaussian Cubature for Tail Estimation and Rare Event

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

Simulation of Multidimensional T-Distributions; Application of Rough Path Theory to Controlled Differential Equations in Earthquake Modelling.

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,

Pedro Vitória

which provides intuition for the underlying dynamics and leads to

is a DPhil Student in Mathematics, Mathematical Institute,

approximations that are useful for the calibration to market data.

University of Oxford. His research Interests are in Stochastic Analysis, Optimal Control and Mathematical Finance.

Peter Spoida is a DPhil student at the Mathematical Institute where he is a member of the Mathematical and Computational Finance Group. He read the BSc in Mathematics at Technische Universität München before completing his MSc in Mathematics and Finance at Imperial College London in 2011.

Kaiwei Wang is a second year DPhil student in the Mathematical and Computational Finance Group at the Mathematical Institute, University of Oxford. His research is focused on behavioural finance and time inconsistent problems.

Peter is working with Jan Obłój on a robust approach in Mathematical

market. His research interests lie in probability theory and its

mic

vanilla options in the market and, possibly, other beliefs about the

cavit

hedging of exotic derivatives which is consistent with the prices of

peter

synthesise a coherent framework for model-independent pricing and

hael

Finance and the incorporation of market information. He aims to

interplay with Mathematical Finance, in particular the understanding of

danie

maria

optimality properties of solutions to the Skorokhod embedding problem.

Welcome to our data laboratory

10 01 11 01 03

THE OMI DATA LAB Learning how to extract needles from haystacks The availability of data has always provided an opportunity to challenge and inform our understanding of the world we live in. In the financial markets, the quantity of information that researchers can access has exploded, but this has not yet been matched by an increase in understanding on a similar scale. An interdisciplinary approach is essential to making progress.

The Lab also incorporates a staff member to provide specialist

Developing new and effective ways to interrogate data – for

quickly and painlessly, and the common approach here should

example to quickly identify anomalous trading patterns or the early

further improve the prospects for collaboration, with OMI

stages of a ‘flash crash’ – can require novel mathematics and

academics working more closely with AHL industry practitioners

statistical algorithms, as well as high-quality tick-by-tick market

as both learn to speak each others’ languages.

data to test them on. Machines can execute trades far faster than

The hope is that the Lab will let OMI academics do research in a new way, and perhaps make breakthroughs of a new kind. “Having a theory and then using data to validate or disprove that theory is the traditional approach,” says Dr Anthony Ledford, Chief Scientist at AHL. “But these days data can lead to theory. I’m firmly convinced that some of the most important insights and theories of the future will come from explaining effects first seen in data.”

humans can intervene, and there is an urgent need to develop a conceptual understanding of the data streams that is robust enough to allow regulators to automatically detect and respond to events such as the publicised software fault that led, over a 30 minute period, to a flood of unintended orders and losses of $440m for a major market maker. Understanding the impact of government guarantees on the valuation of banks seen as too big to fail requires the analysis of a completely different sort of data.

All this means the availability of, and novel conceptual and systemic approaches to, financial data are critical issues for research at the OMI, and this importance will only increase.

support to researchers. Regular seminars are planned to introduce new members to the wealth of information available, as well as to tools and techniques for analysing it. In setting up the Lab, the OMI benefited from the presence in the same building of AHL, the Man Group subsidiary specialising in systematic trading strategies. AHL’s industry relationships and expertise in financial data gave the initiative’s early stages a valuable head-start. It has contributed extensively to the design of the tools that will help researchers access the data they need

The Lab is already enabling projects that would once have been difficult or impossible. One pilot initiative with the Bank of England aims to build a small agent-based model of banks’ interactions with their financial environment, calibrated to

In response, the OMI has created an innovative Data Lab to

market data. Researchers on the project have skills in computer

address the logistical issues its researchers face and allow them

science, maths, econometrics and financial economics, and the

to focus their efforts on fundamental data driven conceptual

data officer will help them find and access the data streams

questions. Commercial organisations such as Man Group plc,

they need to build and calibrate the initial models.

the alternative investment manager that provides financial support for the OMI, spend substantial sums to make data easy to access. Such budgets are never available in universities, but the Data Lab initiative should nevertheless be invaluable in

They will also form relationships with the financial industry to secure access to non-public data. For example, a key priority in modern finance is to gain a better understanding of the consequences of

helping OMI academics carry out badly-needed research.

automated trading, which has already contributed to events like

The Lab draws together all the data available to OMI academics

trading algorithms – still poorly-understood – caused the market to

and makes them readily accessible. OneMarketData has

plunge and then quickly rebound for no obvious reason.

the 2010 flash crash, mentioned above, in which the activities of

donated a licence for its OneTick software for managing large volumes of tick-by-tick financial data, and the OMI has invested in Bloomberg terminals, giving access to information on everything from bond prices to global weather conditions.

1 In May 2010 the US stock market suffered a crash where the Dow Jones Industrial Average plunged about 1000 points, or about 9%, only to recover its losses in minutes.

“The fact the OMI is investing in hedge fund data is enormously beneficial for me and my group in terms of papers and other research output,” Ramadorai continues. “But formalising this in the Data Lab will take things to the next level. When other researchers come to us to ask how they can start working in this area, we can now point them to this wonderful new resource.”

10 01 11 04

THE OMI DATA LAB The OMI has research plans aimed at gaining a deeper

and my group in terms of papers and other research output,”

understanding of trading platforms and processes in order

Ramadorai continues. “But formalising this in the Data Lab will

to shed light on such episodes. Building a meaningful

take things to the next level. When other researchers come to

experimental trading platform, calibrating it to reality and

us to ask how they can start working in this area, we can now

gaining a reasonable understanding of its behaviour requires

point them to this wonderful new resource.”

substantial amounts of trading data. Through its data officer, the OMI plans to work with exchanges and others to build up a dataset that is comprehensive enough to do this.

Likewise, the Institute was recently funded by the ESRC to research ‘Credit Default Swap data, Contagion and Financial Resilience’, which will be most effective if it can analyse proprietary data to understand banks’ CDS positions on each other during the financial crisis, and their wider effects. “The Data Lab gives OMI the central structure needed to acquire and manage this confidential data,” says Professor Terry Lyons, the Institute’s Director. Tarun Ramadorai is an OMI member and a professor at the Saïd Business School. A financial economist, his research investigates large-scale empirical questions like the aggregate behaviour of the hedge fund industry, or the economic activities of millions of Indian households. He’s an old hand at tangling with large datasets, but he still anticipates significant gains from using the new facility, with whose development he has been closely involved.

Bringing high-level expertise to bear on rich datasets is exactly the kind of task the OMI was set up to tackle, and could yield important breakthroughs bringing economists, statisticians, econometricians, engineers, and other scientists together. “There’s been an artificial separation between economists and mathematicians,” Ramadorai says. “But we want to take rigorous maths and combine it with real price data. Many OMI researchers have great models; this will make it easier for them to test and improve them. Hopefully they’ll get to grips with some deep and interesting questions as a result.”

Ledford at AHL agrees, adding that the Data Lab won’t just encourage innovative new research; it will also help ensure that exciting new theories are rigorously tested. “Without the regular reality-check of empirically validating against market data, it’s very easy for quantitative research to leave reality behind,” he says. “Data help you keep your feet on the ground. The vast majority of good ideas benefit from being put to the practical test.”

“The Data Lab will make financial information available to a much wider group of users – it’s going to reduce the cost of innovation,” he says. “Finding the right datasets and working out how to use them can be Ledford at AHL agrees, adding very time-consuming, that the Data Lab won’t just encourage and this can deter innovative new research; it will also help people from moving ensure that exciting new theories are into new areas. This rigorously tested. “Without the regular realitycheck of empirically validating against market new facility will data, it’s very easy for quantitative research lower these search to leave reality behind,” he says. “Data help costs, reducing you keep your feet on the ground. The vast barriers to entry.” “The fact the OMI is investing in hedge fund data is enormously beneficial for me

majority of good ideas benefit from being put to the practical test.”

10 01 11 10 19 04

STUDENTS Sumudu Watugala

Weijun Xu

is a DPhil Student at the Saïd Business School, University of

is a DPhil student in the Stochastic Analysis Group under

Oxford. She is interested in the areas of international finance,

the supervision of Terry Lyons at the University of Oxford.

financial markets, contagion, and volatility. Her current work

Before joining Oxford, he completed a Bachelor’s Degree in

focuses on how interlinks between countries such as trade

Economics and Mathematics at Shanghai Jiaotong University

and capital flows affect markets and economies, especially

and a Master’s Degree in Statistics at Harvard.

during periods of financial crisis.

His research interests lie in the area of probability. He is currently

Her undergraduate and previous postgraduate study was in

working on the problem of inversion of signature for paths of

computer science, engineering, and finance at MIT. Sumudu worked

bounded variation. Together with Professor Terry Lyons, he has

in the finance industry, specialising in volatility and derivatives, prior

developed methods to invert the signature for axis paths, which

to joining Oxford for her doctoral studies.

can only move parallel to the axes. Now he is trying to solve the inversion problem for general paths of bounded variation.

Rasmus Wissman

Danyu Yang

is a DPhil student at the Mathematical Institute’s Mathematical and Computational Finance Group, University

Danyu Yang is working with Professor Terry Lyons on rough

of Oxford. He has previously completed a MSc in Computer

path theory and its applications. She is interested in extracting

Science at the University of Oxford and a BSc in Mathematics

nontrivial information of the path from its signature. She is

at the Technical University Munich.

currently working on the potential application of rough path

His current research is in numerical mathematical finance, focusing on PDE Methods for high-dimensional problems.

theory to Harmonic analysis, especially to the convergence problem pioneered by the celebrated theorem of Carleson.

Zichen Zhang

Yuan Xia

is a DPhil student with the stochastic analysis group at the

is a DPhil student at the Mathematical Institute, University

University of Oxford. Before coming to Oxford in 2010, he

of Oxford. 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.

completed his Bachelor degree in mathematics at Tongji University, China. His interest now lies on Malliavin Calculus and its connection with BSDE (Backward Stochastic Differential Equation).

yuan

udu

zichen

su m

danyu

weijun

rasmus

kai wei

10 01 11 09 20 03

ASSOCIATE MEMBERS Ferhana Ahmad

Tim Jenkinson

PHD Student, Mathematical Institute, University of Oxford Research Interests: Mortgage Backed Securities.

Professor of Finance at the Saïd Business School and Director of the Oxford Private Equity Institute, University of Oxford

John Armour

Research Interests: Initial Public Offerings, Private Equity, Securitisation, Regulation and the Cost of Capital.

Hogan Lovells Professor of Law and Finance, Oxford Law, University of Oxford

Robert Kosowski

Research Interests: Law and Finance, Corporate Insolvency Law, Corporate Finance, Comparative and European Corporate Law, Company Law and Principles of Financial Regulation.

Associate Professor in the Finance Group of Imperial College Business School and Director of the Risk Management Lab & Centre for Hedge Fund Research, Imperial College London

Phil Blunsom

Research Interests: Include Asset Pricing and Financial Econometrics with a focus on Hedge Funds, Mutual Funds, Performance Measurement, Business Cycles and Derivative Trading Strategies.

University Lecturer, Computational Linguistics Group; Department of Computer Science, University of Oxford Research Interests: The intersection of machine learning and computational linguistics. The application of machine learning techniques such as graphical models to a range of problems relating to the understanding, learning and manipulation of language, with a focus on structural induction problems such as grammar induction and learning statistical machine translation models.

Dmitry Kramkov Professor of Computational Finance, Carnegie Mellon University and part-time Professor, Mathematical Institute, University of Oxford Research Interests: Computational Finance – Financial Derivatives, Optimal Investment, Numerical and Software Implementations of Financial Algorithms.

Horatio Boedihardjo

Ray Lal

Second year DPhil Student, Mathematical Institute, University of Oxford

DPhil Student, Department of Computer Science, University of Oxford

Research Interests: Schramm-Loewner Evolution in Riemann Surfaces.

Research Interests: Asset Pricing & Hedging, Mathematical Finance.

Andrea Calì

Anthony Ledford

University Lecturer at the Department of Computer Science and Information Systems, University of London, Birkbeck College

Chief Scientist, AHL

Research Interests: Knowledge Representation and Reasoning, Database Theory, Web Information Systems, Information Integration, Logics and Databases.

Samuel Cohen Junior Research Fellow in Mathematics, St. John’s College, University of Oxford

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

Anthony Lee Postdoctoral Researcher, Department of Statistics, University of Warwick

Head of Equities Strategies, AHL

Research Interests: 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.

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

Wonjung Lee

Alan Hammond

Postdoctoral Research Fellow, OCCAM, Mathematical Institute, University of Oxford

Research Interests: Stochastic Analysis and Mathematical Finance.

Matthias Hagmann-van Arx

Fellow of St. Hughes College, Mathematical Institute, University of Oxford Research Interests: Probability Theory, Statistical Mechanics, Partial Differential Equations.

Campbell Harvey J. Paul Sticht Professor of International Business at the Fuqua School of Business, Duke University and a Research Associate of the National Bureau of Economic Research in Cambridge, Massachusetts. He is also Editor of the Journal of Finance Research Interests: Investment Management, Alternative Investments and Corporate Finance.

Research Interests: To find an efficient filtering scheme in high dimension. Filtering is a widely used methodology for the incorporation of observed data into time-evolving systems and it provides an online approach to state estimation inverse problems when data is acquired sequentially. Also rough path theory and its applications to non-linear filtering.

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

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ASSOCIATE MEMBERS Robert May

Ben Ranish

Lord May of Oxford, Professor of Zoology, Department of Zoology, University of Oxford

Graduate Student, Department of Economics, Harvard University Research Interests: Household and Behavioural Finance.

Research Interests: Systemic and Regulatory Issues. Banking, Credit Risk, Financial Distress, Regulation of Markets and Institutions.

Christoph Reisinger

Colin Mayer

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

Peter Moores Professor of Management Studies; Saïd Business School, University of Oxford Research Interests: Corporate Finance, Corporate Governance, Corporate Taxation, Regulation of Financial Institutions.

Michael Monoyios University Lecturer in Financial Mathematics, Mathematical Institute, University of Oxford Research Interests: Optimal Hedging in Incomplete Markets, Transaction Costs and Singular Control, Parameter Uncertainty in Investment and Hedging, Insider Trading and Information Problems.

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

Wolf-Georg Ringe DAAD Lecturer in Law and Deputy Director at the Institute of European and Comparative Law, University of Oxford Research Interests: General area of Law and Finance, (Comparative), Corporate Governance, Securities Law and the Conflict of Laws.

Pavel Savor

Per Mykland

Assistant Professor of Finance, The Wharton School, University of Pennsylvania

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

Research Interests: Empirical Asset Pricing, Empirical Corporate Finance, Behavioural Finance.

Research Interests: High Frequency Financial Econometrics.

Thomas Noe Ernest Button Professor of Management Studies at the Saïd Business School, University of Oxford 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.

Luke Ong Professor of Computer Science, Director of Graduate Studies, Department of Computer Science, University of Oxford Research Interests: The Semantics of Computation, which is concerned with the development and analysis of mathematical structures that model computation using ideas and tools from Mathematical Logic. More recently his research has tended to be motivated by problems of an algorithmic nature.

Andrew Patton

Bernard Silverman Master of Saint Peter’s College and Chief Scientific Advisor to the Home Office; Professor of Statistics in the Department of Statistics, University of Oxford Research Interests: Computational Statistics, Smoothing Methods, Functional Data Analysis, Multiresolution Analysis in Statistics and the Analysis of Very High Dimensional Data.

Ruediger Stucke Postdoctoral Research Fellow in Finance and Economics, Saïd Business School, University of Oxford Research Interests: 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.

Lukasz Szpruch Nomura Research Fellow at the Mathematical and Computational Finance Group, Mathematical Institute, University of Oxford

Associate Professor of Economics, Duke University

Research Interests: Theoretical and Applied Probability Theory, Stochastic Analysis and Numerical Methods for Stochastic Processes.

Research Interests: Financial Econometrics, Forecasting, Volatility and Dependence Models, Hedge Funds.

Lan Zhang

Cornelius Probst DPhil Student, Department of Statistics, University of Oxford

Professor of Finance; University of Illinois at Chicago Research Interests: Market Microstructure, Statistical Arbitrage and High Frequency Financial Econometrics.

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

Research Interests: Rough Path Analysis and Non-linear Partial Differential Equations.

University Lecturer in the Mathematics Institute, University of Oxford

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EVENTS The purpose of the Oxford-Man Institute’s conference and in-house seminar programme is to provide a collaborative environment to engage academics and practitioners in the world leading research that our members undertake. They are also effective in enabling members, distinguished scholars and practitioners from around the globe to share their ideas and explore their relevance to the areas of quantitative finance and alternative investments research.

Hedge Fund Conference 18th November 2011 Organising Committee Andrew Patton, Duke University and the Oxford-Man Institute, University of Oxford and Tarun Ramadorai, Saïd Business School and the Oxford-Man Institute, 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 and Oliver Scaillet, HEC Geneva

5th Annual Conference of the Society for Financial Econometrics 20-22 June 2012 Chairs of Organising Committee Francis X. Diebold, University of Pennsylvania and President of SoFiE; Eric Ghysels, University of North Carolina, Chapel Hill; Eric Renault, Brown University and Neil Shephard, the Oxford-Man Institute and Department of Economics, University of Oxford. The conference committee had 35 additional members from around the world.

Keynote and invited Speakers John Campbell, Harvard University; René Garcia, EDHEC Business School; Peter Hansen, European University Institute, Italy; Lord Robert May, University of Oxford; Alain Monfort, CREST, Banque de France and Maastricht University; M.Hashem Pesaran, University of Cambridge; Eric Renault, Brown University and Lan Zhang, University of Illinois at Chicago. In addition there were 20 plenary session speakers and 16 people gave poster presentations. This event was hosted by the Oxford-Man Institute and held at the Saïd Business School with a funding contribution from the Bank of England and supported by the Numerical Algorithms Group (NAG).

Time Series Econometrics: A Conference in Honour of Andrew Harvey’s 65th Year 29-30 June 2012 Organising Committee Siem Jan Koopman, VU University, Amsterdam and Neil Shephard, the Oxford-Man Institute and Department of Economics, University of Oxford

Speakers Fabio Busetti, Bank of Italy; Francis X.Diebold, University of Pennsylvania; Simon Godsill, University of Cambridge; Piet de Jong, Macquarie University; Charles R.Nelson, University of Washington; Jukka Nyblom, University of Jyväskylä; Siem Jan Koopman, VU University, Amsterdam; Garry Phillips, Cardiff University; Tommaso Proietti, University of Rome; Giuliano De Rossi, UBS; Esther Ruiz, Universidad Carlos III de Madrid; Enrique Sentana, CEMFI, Madrid; Neil Shephard, the Oxford-Man Institute and Department of Economics, University of Oxford and James H.Stock, Harvard University. This event was hosted by the Oxford-Man Institute with a funding contribution from the Bank of England and the Journal of Applied Econometrics.

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WORKSHOPS AND COURSES Workshop for Young Researchers on BSDEs, Numerics and Finance 2-4 July 2012

Society for Financial Econometrics Summer School 30 July – 3 August 2012

Organising Committee

Organised by

Sam Cohen, Mathematical Institute and the Oxford-Man Institute, University of Oxford; Gechun Liang, the OxfordMan Institute, University of Oxford and Arnaud Lionnet, Mathematical Institute and the Oxford-Man Institute, University of Oxford

Neil Shephard, the Oxford-Man Institute and Department of Economics, University of Oxford

Speakers Jean-François Chassagneux, Imperial College, London; Samuel Drapeau, Humboldt Universität, Berlin; Kai Du, ETH, Zürich; Romuald Elie, Ecole Polytechnique; Salvador Ortiz-Latorre, Imperial College, Anthony Réveillac, Université Paris-Dauphine; Adrien Richou, Université de Bordeaux 1 and Yongsheng Song, Chinese Academy of Sciences, Beijing.

Speakers Francis X.Diebold, University of Pennsylvania and current president of SoFiE; Eric Ghysels, University of North Carolina, Chapel Hill and secretary and past president of SoFiE and Eric Renault, Brown University and editor of the Journal of Financial Econometrics. Peter Christoffersen, University of Toronto and Francis X.Diebold were the distinguished lecturers on new directions in financial econometrics of volatility, correlation and option price dynamics – the inaugural, invitation only, research based course for thirty Ph.D. students and new faculty in financial econometrics.

Backward stochastic differential equations are now well recognised as an efficient tool to approach many modern finance problems. As these equations also form a basis for time-consistent nonlinear probability theory, connections with robust statistics and imprecise probability are open for exploration. The use of BSDEs in the theory of nonlinear PDE (Partial Differential Equations), particularly from a numerical perspective, is also an active area of development, with more general applications in applied mathematics.

Over the thirty weeks of the academic year the Oxford-Man Institute organised and hosted 120 seminars which were well attended by Institute Members, Associates and the wider University of Oxford community. These seminars are broadly divided into five separate series: the Institute Series (co-hosting speakers with the Saïd Business School and the Maths Institute for some seminars); Stochastic Analysis (chaired by Terry Lyons and on the subject of Mathematical Finance, Risk Management, Statistics Filtering and Probability Theory); Sandwich (specialist interest seminars); Practitioner (related industry practitioners are invited to share insights and ideas) and the Internal Series (launched in October 2011, with the aim that members of the Institute could understand the research undertaken by colleagues in different disciplines).

This event was co-hosted by the Oxford-Man Institute and St. John’s College, University of Oxford with the cooperation of SMAI

Term Time Seminar Series

The meeting brought together thirty four young researchers from nineteen different institutions and allowed for the development of new ideas and further interaction between the areas.

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VISITORS Faculty

Graduate students

Long-term visitors:

Jake Cohen, Director of INSEAD

Jeffrey Wang, Harvard University

Short-term visitors:

Yiqing Lin, University of Rennes

Peter Christoffersen, Professor of Finance, Rotman School of Management, University of Toronto

Practitioners

Francis X. Diebold, Paul F. and Warren S. Miller Professor of Economics, University of Pennsylvania

Long-term visitors:

Philipp Illeditsch, Assistant Professor of Finance, Wharton, University of Pennsylvania Kumar Muthuraman, Associate Professor, University of Texas at Austin Sergio Pulido, Post-Doctoral Associate, Carnegie Mellon University Sebastian Riedel, Assistant Professor, University College London Ronnie Sircar, Professor, Operations Research & Financial Engineering, Princeton University Michael SĂ¸rensen, Professor and Associate Chair for Research at the Department of Mathematical Sciences, University of Copenhagen Johan Walden, Assistant Professor, Haas Finance Group, UC Berkeley Dacheng Xiu, Assistant Professor of Econometrics and Statistics, The University of Chicago Booth School of Business

Tim Hoggard, Visiting Research Fellow

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visitors “I spent a week at OMI lecturing for 30 international PhD students in the inaugural OMI-SoFiE Financial Econometrics Summer School. OMI has a great reputation throughout North-America and I was honored to be invited to visit for a week. OMI had done a fabulous job selecting the 30 students from around 100 applicants. I was struck by the level of maturity of the students and by the quality of their research which was showcased in their presentations during the course. OMI was a perfect host for this course. The class room environment is superb and OMI handled perfectly the logistics for students and for lecturers alike.” Peter Christoffersen, University of Toronto

“OMI is now firmly established as one of the world’s top places for financial econometrics, and it has been a great honor for me to participate over the years in its vibrant agenda. In just the last two months I have been involved in three events (!), most recently lecturing in the inaugural OMI/SoFiE Summer School in Financial Econometrics, which assembled thirty talented advanced Ph.D. students from all over the world. The OMI atmosphere of intellectual intensity, academicindustry interface, and first-rate facilities makes everything work wonderfully.”

vis

Francis X. Diebold, University of Pennsylvania

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WORKING PAPERS E RS Angoshtari, B.

Gottlob, G.

Angoshtari, B. (2011) Portfolio ortfolio Choice with Cointegrated Assets

Abiteboul, S., Gottlob, G. and Manna, M. (2011) Distributed XML Design. Journal of Computer and System Sciences, 77 (6), 936-964.

Armour, J. Armour, J., Mayer, C. and Polo, A. (2011) Regulatory Sanctions and Reputational Damage in Financial Markets, Working Paper, Saïd Business School, University of Oxford, Oxford.

Bujok, K. Reisinger, C. and Bujok, K. (2011) Valuation of Basket Credit Derivatives in Structural Jump-Diffusion Models, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Cherny, V. Cherny, V. and Obłój, J. (2012) Portfolio optimisation under non-linear drawdown constraints in a semi martingale financial model, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Benedikt, M., Gottlob, G. and Senellart, P. (2011) Determining Relevance of Accesses at Runtime (Extended Version), Working Paper, Department of Computer Science, University of Oxford, Oxford.

Hao, N. Hao, N. and Lyons, T. (2011) Expected signature of Brownian motion up to the first exit time of the domain, Working Paper, Mathematical Institute, University of Oxford, Oxford. Hao, N. and Xu, W. (2012) Exact weak convergence rates for signatures of Brownian motion.

Hambly, B. Gyurko, L.G., Hambly, B.M. and Witte, J.H. (2011) Monte Carlo methods via a dual approach for some discrete time stochastic control problems.

Cohen, S.N.

Henderson, V.

Cohen, S.N. and Hu, Y. (2012) Ergodic BSDEs on Markov chains.

Henderson V. and Liang G. (2012) A Multidimensional Exponential Utility Indifference Pricing Model with Applications to Counterparty Risk, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Cohen, S.N., Elliott, R.J. and Siu, T.K. (2011) Backward stochastic difference equations for dynamic convex risk measures on a binomial tree, Working Paper, Mathematical Institute, University of Oxford, Oxford. Cohen, S.N., Ji, S. and Peng, S. (2011) Sublinear Expectations and Martingales in Discrete Time, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Doucet, A. Lee, A., Caron, F., Doucet, A. and Holmes, C. (2011) Bayesian SparsityPath-Analysis of Genetic Association Signal using Generalized t Priors.

Giles, M.

Henderson, V., Sun, J. and Whalley, E. (2011) Portfolios of American Options under General Preferences: Results and Counterexamples, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Holmes, C. Lee, A., Caron, F., Doucet, A. and Holmes, C. (2011) Bayesian SparsityPath-Analysis of Genetic Association Signal using Generalized t Priors.

Howison, S.

Reisinger, C. and Giles, M. (2011) Stochastic Finite Differences and Multilevel Monte Carlo for a Class of SPDEs in Finance, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Howison, S., Reisinger, C. and Witte, J.H. (2012) The Effect of NonSmooth Payoffs on the Penalty Approximation of American Options, Working Paper, Mathematical Institute and Oxford-Man Institute of Quantitative Finance, University of Oxford.

Szpruch, L. and Giles, M. (2011) A note on Milstein Fundamental Theorem for Non-linear SDEs, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Howison, S. and Schwarz, D. (2012) Risk-Neutral Pricing of Financial Instruments in Emission Markets: A Structural Approach In press, SIAM J Financial Math.

Szpruch, L. and Giles, M. (2011) Efficient Multilevel Monte Carlo simulations of non-linear financial SDEs without a need of simulation of Levy areas, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Gould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. (2011) Limit Order Books. Working Paper, Mathematical Institute, University of Oxford, Oxford.

Gould, M.

Gould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. (2011) Statistical properties of foreign exchange limit order books, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Gould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. (2011) Limit order books. Working Paper, Mathematical Institute, University of Oxford, Oxford. Gould, M., Porter, M., William, S., McDonald, M., Fenn, D. and Howison, S. (2011) Statistical properties of foreign exchange limit order books, Working Paper, Mathematical Institute, University of Oxford, Oxford. Gould, M.D., Porter, M.A., Williams, S., McDonald, M., Fenn, D. J. and Howison, S. (2012) “Price formation in limit order markets with bilateral trade agreements”.

Jenkinson, T. Jenkinson, T. and Stucke, R. (2011) Who Benefits from the Leverage in LBOs? Working Paper, Saïd Business School, University of Oxford, Oxford. Jenkinson, T., Axelson, U., Stromberg, P. and Weisbach, M. (2012) Borrow Cheap, Buy High? The Determinants of Leverage and Pricing in Buyouts, Working Paper, University of Oxford, Oxford.

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WORKING PAPERS Kallblad, S.

Monoyios, M.

Zariphopoulou, T. and Kallblad, S. (2011) Forward Optimal Portfolios. Working Paper, Mathematical Institute, University of Oxford, Oxford.

Monoyios, M. (2012) “Malliavin calculus method for asytmptotic expansion of exponential indifference prices”

Zariphopoulou, T. and Kallblad, S. (2011) On the forward and backward portfolio problem in log-normal markets, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Mykland, P.

Lee, A. Lee, A., Caron, F., Doucet, A. and Holmes, C. (2011) Bayesian SparsityPath-Analysis of Genetic Association Signal using Generalized t Priors. Lee, A., May, B.C., Korda, N. and Leslie, D.N. (2011) Optimistic Bayesian sampling in contextual-bandit problems, Working Paper, Department of Statistics, University of Oxford, Oxford.

Liang, G. Liang, G., Lütkebohmert, E. and Xiao, Y. (2011) A Multi-Period Bank Run Model for Liquidity Risk. Henderson, V. and Liang, G. (2012) A Multidimensional Exponential Utility Indifference Pricing Model with Applications to Counterparty Risk, Working Paper, Mathematical Institute, University of Oxford, Oxford. Liang, G., Lutkebohmert, E. and Wei, W. (2012) A continuous time structural model for insolvency, recovery and rollover risks, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Lionnet, A.

Zhang, L., Li, Y., Mykland, P., Renault, E. and Zheng, X. (2011) Realized volatility when sampling times can be endogenous, Working Paper, Department of Statistics, The University of Chicago.

Nadtochiy, S. Zariphopoulou, T. and Nadtochiy, S. (2011) A class of homothetic forward investment process with non-zero volatility, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Noe, T. Noe, T., Banerjee S. and Bhattacharyya, S. (2011) Pumping Up the SEO: The Rewards of Uninformed Speculation, Working Paper, Saïd Business School, University of Oxford, Oxford. Noe, T. (2012) Blood and money: Kin altruism and the governance of the family firm. Oxford University Working Paper. Yufeng, H., Noe, T. and Rebello, M. (2012) Horses for courses: Fund managers and organizational structures. Oxford University Working Paper. Banerjee, S. and Noe, T. (2012) Legal-system arbitrage and parentsubsidiary capital structures, Oxford University Working Paper.

Lionnet, A. (2011) Quadratic reflected BSDEs under bounded conditions, working paper.

Sudipto, D. and Noe, T. (2012) Does pay activism pay off for shareholders? Shareholder democracy and its discontents, Oxford University Working Paper.

Lyons, T.

Noureldin, D.

Cass, T. and Lyons, T.J. (2011) Integrability estimates for Gaussian rough differential equations.

Noureldin D., Shephard N. and Sheppard K. (2012) Multivariate Rotated ARCH Models, Working Paper, Department of Economics, University of Oxford, Oxford.

Hao, N. and Lyons, T. (2011) Expected signature of Brownian motion up to the first exit time of the domain, Working Paper, Mathematical Institute, University of Oxford, Oxford. Lyons, T. and Xu, W. (submitted) 2012 A uniform estimate for rough paths.

Mayer, C. Klein, M. and Mayer, C. (2011) Mobile Banking and Financial Inclusion: The Regulatory Lessons, Working Paper, Saïd Business School, University of Oxford, Oxford. Mas, I. and Mayer, C. (2011) Savings as Forward Payments: Innovations on Mobile Money, Working Paper, Saïd Business School, University of Oxford, Oxford.

Obłój, J. Cherny, V. and Obłój, J. (2012) Portfolio optimisation under nonlinear drawdown constraints in a semi martingale financial model, Working Paper, Mathematical Institute, University of Oxford, Oxford. Obłój, J. and Spoida, P. (2012) An Iterated Azema-Yor Type Embedding for Finitely Many Marginals, Working Paper, Mathematical Institute, University of Oxford, Oxford. Obłój, J. and Spoida, P. (2012) Robust Pricing and Hedging of European Options Under a Realized Variance Constraint, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Kramkov, D. and Predoiu, S. (2011) Integral representation of martingles and endrogenous completeness of financial models, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Zhang, L. and Mykland, P. (2011) Between data cleaning and inference: Pre-averaging and other robust estimators of the efficient price, Working Paper, Department of Statistics, The University of Chicago.

RES

Kramkov, D. Kramkov, D. and Bank, P. (2011) A model for a large investor trading at market indifference prices, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Mykland P., Shephard, N. and Sheppard K. (2012) Efficient and feasible inference for the components of financial variation using blocked multipower variation, Working Paper, Department of Economics, University of Oxford.

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WORKING PAPERS Ozsoylev, H.

Reisinger, C.

Ozsoylev, H., Walden, J., Yavuz, D. and Bildik, R. (2011) Investor networks in the stock market, Working Paper, Saïd Business School, University of Oxford, Oxford.

Reisinger, C. and Bujok, K. (2011) Valuation of Basket Credit Derivatives in Structural Jump-Diffusion Models, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Patton, A.J

Reisinger, C. and Giles, M. (2011) Stochastic Finite Differences and Multilevel Monte Carlo for a Class of SPDEs in Finance, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Patton, A.J. and Sheppard, K. (2011) Good Volatility, Bad Volatility: Signed Jumps and the Persistence of Volatility. Ramadorai, T. and Patton, A. (2011) Application of Cubature method to TARN option pricing, Working Paper, Saïd Business School, University of Oxford, Oxford. Ramadorai, T., Patton, A. and Streatfield, M. (2012) Change You Can Believe In? Hedge Fund Data Revisions, Working Paper, Saïd Business School, University of Oxford, Oxford.

Witte, J.H.

Reisinger, C. and Witte, J.H (2011) Penalty Methods for the Numerical Solution of HJB Equations -- Continuous Control and Obstacle Problems, Working Paper, Mathematical Institute, University of Oxford, Oxford. Reisinger, C. and Gupta, A. (2011) Robust Calibration of Financial Models Using Bayesian Estimators.

Ruf, J.

Witte, J.H. and Reisinger, C., (2011) Penalty Methods for the Numerical Solution of HJB Equations - Continuous Control and Obstacle Problems.

Ruf, J. (2012) A new proof for the conditions of Novikov and Kazamaki, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Phalippou, L.

Ruf, J. (2012) Negative call prices, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Phalippou, L. (2012) A comment on recent evidence on private equity performance, Working Paper, Saïd Business School, University of Oxford, Oxford. Phalippou, L. (2011) Is Yale a Model? Phalippou, L. (2012) Performance of Buyout Funds Revisited?

Perkowski, N. and Ruf, J. (2012) Conditioned martingales, Working Paper, Mathematical Institute, University of Oxford, Oxford. Carr, P., Fisher, T. and Ruf, J. (2012) On the hedging of options on exploding exchange rates, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Phalippou, L., Xu, F. and Zhao, H. (2012) Hunting the Hunters: New Evidence on the Drivers of Acquirer’s Announcement Returns in M&As.

Carr, P., Fisher, T. and Ruf, J. (2012) Why are quadratic normal volatility models analytically tractable?, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Ramadorai, T.

Blanchet, J. and Ruf, J. (2012) A Weak Convergence Criterion Constructing Changes of Measure, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Ramadorai, T. and Patton, A. (2011) Application of Cubature method to TARN option pricing, Working Paper, Saïd Business School, University of Oxford, Oxford. Ramadorai, T. and Streatfield, M. (2011) Money for nothing? Understanding variation in reported hedge fund fees, Working Paper, Saïd Business School, University of Oxford, Oxford. Ramadorai, T., Acharya, V. and Lochstoer, L. (2011) Limits to arbitrage and hedging: Evidence from commodity markets, Working Paper, Saïd Business School, University of Oxford, Oxford. Ramadorai, T., Jotikasthira, C. and Lundblad C. (2011) Asset fire sales and purchases and the international transmission of financial shocks, Working Paper, Saïd Business School, University of Oxford, Oxford. Ramadorai, T., Patton, A. and Streatfield, M. (2012) Change You Can Believe In? Hedge Fund Data Revisions, Working Paper, Saïd Business School, University of Oxford, Oxford. Albuquerque, R., Ramadorai, T. and Watugala, S. W. (2011) Trade credit and Cross-Country Predictable Firm Returns, Working Paper, Saïd Business School, University of Oxford, Oxford. Campbell, J. Y., Ramadorai, T. and Ranish, B. (2012) How do regulators influence mortgage risk? Evidence from an emerging market. Working Paper, Saïd Business School, University of Oxford, Oxford.

Oyarzun, C. and Ruf, J. (2012) Convergence in Models with Bounded Expected Relative Hazard Rates, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Shephard, N. Barndorff-Nielsen O. and Shephard N. (2012) Integer-valued Lévy processes and law latency financial econometrics Working Paper, Department of Economics, University of Oxford, Oxford. Mykland P., Shephard, N. and Sheppard K. (2012) Efficient and feasible inference for the components of financial variation using blocked multipower variation, Working Paper, Department of Economics, University of Oxford. Noureldin D., Shephard N. and Sheppard K. (2012) Multivariate Rotated ARCH Models, Working Paper, Department of Economics, University of Oxford, Oxford.

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WORKING PAPERS Sheppard, K.

Xu, W.

Collard, F., Mukerji, S., Sheppard, K. and Tallon, J-M. (2011) Ambiguity and the historical equity premium.

Hao, N. and Xu, W. (2012) Exact weak convergence rates for signatures of Brownian motion.

Riedel, S. and Xu, W. (submitted) 2012. A simple proof of distance bounds for Gaussian rough paths.

Noureldin D., Shephard N. and Sheppard K. (2012) Multivariate Rotated ARCH Models, Working Paper, Department of Economics, University of Oxford, Oxford.

Zariphopoulou, T.

Spoida, P.

Zariphopoulou, T. and Kallblad, S. (2011) On the forward and backward portfolio problem in log-normal markets, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Obłój, J. and Spoida, P. (2012) An Iterated Azema-Yor Type Embedding for Finitely Many Marginals, Working Paper, Mathematical Institute, University of Oxford, Oxford. Obłój, J. and Spoida, P. (2012) Robust Pricing and Hedging of European Options Under a Realized Variance Constraint, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Lyons, T. and Xu, W. (submitted) 2012. A uniform estimate for rough paths.

Zariphopoulou, T. and Kallblad, S. (2011) Forward optimal portfolios. Working Paper, Mathematical Institute, University of Oxford, Oxford.

Zariphopoulou, T. and Nadtochiy, S. (2011) A class of homothetic forward investment process with non-zero volatility, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Patton, A.J. and Sheppard, K. (2011) Good Volatility, Bad Volatility: Signed Jumps and the Persistence of Volatility.

Zariphopoulou, T., Kallblad, S. and Malamud, S. (2011) Monotonicity and convexity properties of optimal portfolios in complete lognormal markets, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Streatfield, M.

Zheng, X

Streatfield, M. and Ramadorai, T. (2011) Money for nothing? Understanding variation in reported hedge fund fees, Working Paper, Saïd Business School, University of Oxford, Oxford.

Zhang, L., Li, Y., Mykland, P., Renault, E. and Zheng, X. (2011) Realized volatility when sampling times can be endogenous, Working Paper, Department of Statistics, The University of Chicago.

Streatfield, M., Ramadorai, T. and Patton, A. (2012) Change You Can Believe In? Hedge Fund Data Revisions, Working Paper, Saïd Business School, University of Oxford, Oxford.

Zhang, L.

Szpruch, L.

Szpruch, L. and Giles, M. (2011) A note on Milstein Fundamental Theorem for Non-linear SDEs, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Zhang, L.(2011), What you don’t know cannot hurt you: On the detection of small jumps, Working Paper, Department of Statistics, The University of Chicago.

Zhang, L., Li, Y., Mykland, P., Renault, E. and Zheng, X. (2011) Realized volatility when sampling times can be endogenous, Working Paper, Department of Statistics, The University of Chicago.

Szpruch, L. and Mao, X. (2011) Strong Convergence of Numerical Methods for Nonlinear Stochastic Differential Equations under Monotone Conditions, Working paper, Mathematical Institute, University of Oxford, Oxford.

Watugala, S.W.

Zhou, X. Qian, Z. and Zhou, X. (2012) “Existence of solutions to a class of indefinite stochastic Riccati equations”, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Albuquerque, R., Ramadorai, T. and Watugala, S. W. (2011) Trade Credit and Cross-Country Predictable Firm Returns, Working Paper, Saïd Business School, University of Oxford, Oxford.

Witte, J.H.

RES

Reisinger, C. and Witte, J.H. (2011) Penalty Methods for the Numerical Solution of HJB Equations -- Continuous Control and Obstacle Problems, Working Paper, Mathematical Institute, University of Oxford, Oxford.

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PUBLICATIONS Calì, A.

Gottlob, G.

Calì, A. and Pieris, A. (2011) On Equality-Generating quality-Generating Dependencies in Ontology Querying (extended abstract). Proc. of SEBD.

Gottlob, G. (2011) On Minimal Constraint Networks. Principles and Practice of Computer Programming. 6876, 325-339.

Calì, A. Gottlob, G. and Pieris, A. (2011) New Expressive Languages for Ontological Query Answering. Proceedings of AAAI.

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

Calì, A., Gottlob, G. and Pieris, A. (2011) Querying Conceptual Schemata with Expressive Equality Constraints. Proceedings of ER. Calì, A., Gottlob, G. and Pieris, A. (2012) An Ontological Query Answering under Expressive Entity-Relationship Schemata. Information Systems 37 (4), 320-335.

Cohen, S.N. Cohen, S.N. (2012) Representing filtration consistent nonlinear expectations as g-expectations in general probability spaces. Stochastic processes and their Applications. 122 (4), 1601-1626. Cohen, S.N. and Elliott, R.J. (2011) Backward Stochastic Difference Equations and nearly-time-consistent nonlinear expectations. SIAM Journal of Control and Optimization, 49 (1), 125-139.

Gottlob, G., Pichler, R. and Savenkov, V. (2011) Normalization and optimization of schema mappings. The VLDB Journal. 20 (2), 277-302. Gottlob, G., Sellers, A. J., Furche, T. and Grasso, G. (2011) Taking the OXPath down the deep web. Proceedings of EDBT. 14, 542-545. Gottlob, G., Sellers, A., Furche, T., Grasso, G. and Schallhart, C. (2011) OXPath: little language, little memory, great value. Companion volume of the 20th International WWW Conference, 261-264. Calì, A., Gottlob, G. and Pieris, A. (2011) New Expressive Languages for Ontological Query Answering. Proceedings of AAAI. Calì, A., Gottlob, G. and Pieris, A. (2011) Querying Conceptual Schemata with Expressive Equality Constraints. Proceedings of ER.

Cohen, S.N. and Elliott, R.J. (2011) Existence, Uniqueness and Comparisons for BSDEs in General Spaces. Annals of Probability.

Calì, A., Gottlob, G. and Pieris, A. (2012) An Ontological Query Answering under Expressive Entity-Relationship Schemata. Information Systems 37 (4), 320-335.

Cohen, S.N. and Szpruch, L. (2012) A limit order book model for latency arbitrage, to appear in Mathematics and Financial Economics.

Gyurko, L.G.

Cohen, S.N. (2012) Chaos representations for Marked Point Processes, Communications on Stochastic Analysis 6(2), 263-279, 2012. Cohen, S.N. and Szpruch, L. (2012) On Markovian solutions to Markov chain BSDEs, Numerical Algebra, Control and Optimization 2(2), 257269, 2012.

Lyons, T. and Gyurko, L.G. (2011) Efficient and Practical Implementations of Cubature on Wiener Space. Stochastic Analysis 2010, 73-111.

Hambly, B.M.

Cohen, S.N., (2012) Quasi-sure analysis, aggregation and dual representations of sublinear expectations in general spaces, Electronic Journal of Probability, 17, Article 62.

Hambly, B.M. (2011) Asymptotics for functions associated with heat flow on the Sierpinski carpet, Canadian Journal of Mathematics. 63 (1), 153-180.

Giles, M.

Hambly, B.M., Biggins, J.D. and Jones, O.D. (2011) Multifractal spectra for random self-similar measures via branching processes. Advances in Applied Probability. 43 (1), 1-39.

Bradley, T., du Toit, J., Giles M., Tong R. and Woodhams, P. (2011) Parallelisation techniques for random number generators. In: Hwu W.W. GPU Computing Gems. Volume 1, Burlington: Morgan Kaufman. Giles, M. (2011) Approximating the erfinv function. In: Hwu W.W. GPU Computing Gems. Volume 2, Burlington: Morgan Kaufman. Klingbail, G., Erban, R., Giles, M. and Maini K. (2011) Parallel stochastic simulation for the Systems Biology Toolbox 2 for MATLAB. Bioinformatics, 27 (8), 1170-1171. Klingbeil G., Erban, R., Giles, M. and Maini, P. (2011) Fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactionsm. IEEE Transactions on Parallel and Distributed Systems. 23 (2) 280-287.

Hoa, N. Lyons, T. and Hao, N. (2011) Expected Signature of Two Dimensional Brownian Motion up to the First Exit Time of the Domain, Working Paper, Mathematical Institute, University of Oxford, Oxford.

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PUBLICATIONS Henderson, V.

Lyons, T.

Henderson, V. (2012) Prospect Theory, Liquidation and the Disposition Effect. Management Science. 58 (2), 445-460.

Cass, T., Litterer, C. and Lyons, T. (2011) Rough paths on manifolds. In: New Trends in Stochastic Analysis and Related Topics, World Scientific Publishing.

Holmes, C.

Lyons, T. and Hao, N. (2011) Expected Signature of Two Dimensional Brownian Motion up to the First Exit Time of the Domain, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Holmes, C., Yau, C., Papaspiliopoulos, O. and Roberts, G. (2011) Bayesian non-parametric hidden Markov models with applications in genomics. Journal of the Royal Statistical Society, Series B. 73 (1), 37-57.

Jenkinson, T.

Lyons, T. and Gyurko, L.G. (2011) Efficient and Practical Implementations of Cubature on Wiener Space. Stochastic Analysis 2010, 73-111.

Jenkinson, T., Abrahamson, M. and Jones, H. (2011) Why Don’t U.S. Issuers Demand European Fees for IPOs? The Journal of Finance. 66(6) 2055-2082.

Lyons, T. and Litterer, C. (2011) Introducing Cubature to Filtering. In: Crisan, D. and Rozovskii B., The Oxford Handbook for Non-Linear Filtering. Oxford University Press. 786-798.

Jenkinson, T. and Sousa, M. (2011) Why SPAC Investors Should Listen to the Market, Working Paper, Saïd Business School, University of Oxford, Oxford.

Litterer, C. and Lyons, T. (2011) Introducing Cubature of Filtering. In: Crisan D. and Rozovsky, B., Oxford Handbook of Non-Linear Filtering. Oxford, Oxford University Press.

Jin, H.

Lyons, T., Cass, T. and Litterer, C. (2011) Integrability Estimates for Gaussian Rough Differential Equations, Working Paper, Mathematical Institute, University of Oxford, Oxford.

Jin, H., Dai, M. and Liu, H. (2011) Illiquidity, Position Limits, and Optimal Investment for Mutual Funds. Journal of Economic Theory. 146 (4), 1598-1630. Zhou, X. and Jin, H. (2011) Greed, leverage, and potential losses: A prospect theory perspective. Mathematical Finance. Zhou, X., Jin, H. and Zhang, S. (2011) Behavioral portfolio selection with loss control. Acta Mathematica Sinica, 27 (2), 255-274.

Kanaya, S. Kanaya, S. and Otsu, T. (2012) Large deviations of realized volatility. Stochastic Processes and their Applications. 122, 546-581.

Liang, G. Liang, G. and Jiang, L. (2011) A modified structural model for credit risk. IMA Journal of Management Mathematics. Liang, G., Lyons, T. and Qian, Z. (2011) Backward stochastic dynamics on a filtered probability space. Annals of Probability. 39 (4), 1422-1448. Liang, G., Lutkebohmert, E. and Xiao, Y. (2012) A multi-period bankrun model for liquidity risk, Review of Finance, accepted.

Lyons, T., Cass, T. and Litterer, C. (2011) Rough Paths on Manifolds. In: Zhao, H. and Truman A., New Trends in Stochastic Analysis and Related Topics. World Scientific Publishing. Liang, G., Lyons, T. and Qian, Z. (2011) Backward stochastic dynamics on a filtered probability space. Annals of Probability. 39 (4), 1422-1448. Liang, G., Lyons, T. and Qian, Z. (2011) Backward Stochastic Dynamics on a Filtered Probablity Space. Annals of Probability, 39 (4), 1422-1448.

Martinez, J. Martinez, J. (2011) Information Misweighting and the Cross Section of Stock Recommendations. Journal of Financial Markets. 14 (4), 515-539. Martinez, J. and Sandleris, G. (2011) Is it punishment? Sovereign defaults and the declines in trade, Journal of International Money and Finance.

Monoyios, M. Monoyios, M. and Ng, A. (2011) Optimal exercise of an executive stock option by an insider. International Journal of Theoretical and Applied Finance. 14 (01), 83-106.

RES

Liang, G. and Jiang, L. (2012) A modified structural model for credit risk, IMA Journal of Management Mathematics, Vol.23, No.2, 147–170.

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PUBLICATIONS Mykland, P.A.

Patton, A.

Mykland, P.A. and Zhang, L. (2011) The Double Gaussian Approximation for High Frequency Data. Scandinavian Journal of Statistics, 38 (2), 215-236.

Patton, A. (2011) Data-Based Ranking of Realised Volatility Estimators. Journal of Econometrics, 161 (2), 284-303.

Mykland, P.A., Ait-Sahalia, Y. and Zhang, L. (2011) Ultra high frequency volatility estimation with dependent microstructure noise. Journal of Econometrics. 160 (1), 160-165. Mykland, P.A., Zhang, L. and Ait-Sahalia, Y. (2011) Edgeworth expansions for realized volatility and related estimators. Journal of Econometrics, 160 (1), 190-203. Zhang, L. and Mykland, P.A. (2012) The econometrics of high frequency data. In: Kessler, M., Lindner, A., Sorensen, M., Statistical Methods for Stochastic Differential Equations, Chapman and Hall.

Noe, T. Noe, T. (2011) Where did all the dollars go? The effect of cash flows on capital and asset structure. Journal of Financial and Quantitative Analysis. 46 (5), 1259-1294. Noe, T, Rebello, M. and Wang, J. (2012) Learning to bid: The design of auctions under uncertainty and adaptation, Games and Economic Behavior 72, 620-636. Noe, T. and Rebello, M. (2011) Optimal corporate governance and compensation policy in a dynamic world, Review of Financial Studies 25, 480-521.

Noureldin, D. Noureldin, D., N. Shephard, and K. Sheppard (2011). Multivariate high-frequency-based volatility (HEAVY) models. Journal of Applied Econometrics. Forthcoming.

Obłój, J. Obłój, J. and Cox, A.M.G. (2011) Robust pricing and hedging of double no-touch options, Finance and Stochastics. Carraro, L. El Karoui, N. and Obłój, J. On Azema-Yor Processes, their Optimal Properties and the Bachelier-Drawdown Equation, Ann. Probab. Volume 40, Number 1 (2012), 372-400.

Ozsoylev, H. Ozsoylev, H. and Werner, J. (2011) Liquidity and asset prices in rational expectations equilibrium with ambiguous information. Economic Theory, 48 (2), 269-291. Ozsoylev, H. and Walden, J. (2011) Asset pricing in large information networks. Journal of Economic Theory, 146(6), 2252-2280.

Pakel, C. Pakel, C., Shephard, N. and Sheppard, K. (2011) Nuisance parameters, composite likelihoods and a panel of GARCH models. Statistica Sinica, 21 (1) 307-329.

Patton, A. (2011) Volatility Forecast Comparison using Imperfect Volatility Proxies. Journal of Econometrics, 160 (1), 246-256. Patton, A. Timmermann, A. (2011) Predictability of Output Growth and Innovation: A Multi-Horizon Survey Approach. Journal of Business and Economic Statistics. 29 (3), 397-410.

Phalippou, L. Morris, P. and Phalippou, L. (2012) A new approach to regulating private equity. Journal of corporate law studies. Volume 12, Number 1, April 2012 , pp. 59-84(26).

Qian, Z. Qian, Z. and Tudor, J. (2011) Differential Structure and Flow equations on Rough Path Space. Bulletin des Sciences Mathématiques, 135 (6-7), 695-732. Qian, Z. and Ying, J. (2011) Martingale representations for diffusion processes and backward stochastic differential equations. Séminaire de Probabilités XLIV. Qian, Z., Tudor, J. and Cass, T. (2011) Non-Linear Evolution Equations Driven by Rough Paths, Working Paper, Mathematical Institute, University of Oxford, Oxford. Qian, Z., Zheng, W. and Duan, X.L. (2011) On Local Linear Approximations to Diffusion Processes. International Journal of Mathematics and Mathematical Sciences, 2011.

Ramadorai, T. Ramadorai, T. (2011) Capacity constraints, investor information, and hedge fund returns, Working Paper, Saïd Business School, University of Oxford, Oxford. Patton, A., and Ramadorai, T. (2012) On the high-frequency dynamics of hedge fund risk exposures, Journal of Finance. Jotikasthira, P., Lundblad C.T. and Ramadorai, T. (2012) Asset fire sales and purchases and the international transmission of funding shocks Journal of finance forthcoming.

Roberts, S. Ebden, M. and Roberts, S.J. (2011) Graph Marginalization for Rapid Assignment in Wide-Area Surveillance. Advances in Ad Hoc Networks, 9 (2), 180-188. Roberts, S. and Ebden, M. (2011) Graph marginalization for rapid assignment in widearea surveillance. Ad Hoc Networks, 9 (2), 180-188. Roberts, S. and Fox, C. (2011) A Tutorial on Variational Bayesian Inference. In: Robertson, D., Artificial Intelligence Review. Springer.

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PUBLICATIONS

Roberts, S., Psorakis I. and Ebden, M (2011) Overlapping Community Detection using Bayesian Non-negative Matrix Factorization. Physical Review E, 83 (6). 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, 24 (7), 726-734.

Zariphopoulou, T. and Musiela, M. (2011) Initial investment choice and optimal future allocations under time-monotone performance criteria. International Journal of Theoretical and Applied Finance, 14 (1), 61-81. Zariphopoulou, T., Sircar, R. and Leung, T. (2012) Forward indifference valuation of American options. Stochastics: an International Journal of Probability and Stochastic Processes. 2012, 1-30.

Zhang, L. Mykland, P.A. and Zhang, L. (2011) The Double Gaussian Approximation for High Frequency Data. Scandinavian Journal of Statistics, 38 (2), 215-236.

Ringe, G.

Mykland, P.A., Ait-Sahalia, Y., and Zhang, L. (2011) Ultra high frequency volatility estimation with dependent microstructure noise. Journal of Econometrics. 160 (1), 160-165.

Kettunen, M. and Ringe, W.G. (2012) Disclosure Regulation of CashSettled Equity Derivatives – an Intentions-Based Approach. Lloyd’s Maritime and Commercial Law Quarterly 227-260.

Mykland, P.A., Zhang, L. and Ait-Sahalia, Y. (2011) Edgeworth expansions for realized volatility and related estimators. Journal of Econometrics, 160 (1), 190-203.

Shephard, N.

Zhang, L. and Mykland, P.A. (2012) The econometrics of high frequency data. In: Kessler, M., Lindner, A., Sorensen, M., Statistical Methods for Stochastic Differential Equations, Chapman and Hall.

Shephard, N., Barndorff-Nielsen, O. and Pollard, D. (2012) Integervalued Levy processes and low latency financial econometrics, Quantitative Finance, 12, 587-605.

Zhou, X.

Shephard, N. and Flury, T. (2011) Bayesian inference based only on a simulated likelihood. Econometric Theory, 27 (5), 933-956.

Zhou, X. and He, X. (2011) Portfolio choice under cumulative prospect theory: An analytical treatment. Management Science, 57 (2), 315-331.

Shephard, N., Barndorff-Nielsen, Ole E., Lunde, A. and Hansen, P.R. (2011) Subsampling realised kernels. Journal of Econometrics, 160 (1), 204-219.

Zhou, X. and Chiu, C. (2011) The premium of dynamic trading. Quantitative Finance, 11 (1), 115-123.

Barndorff-Nielsen, O., Hansen, P.R., Lunde, A. and Shephard, N. (2011) Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and nonsynchronous trading. Journal of Econometrics, 162 (2). Pakel, C., Shephard N. and Sheppard K. (2011) Nuisance parameters, composite likelihoods and a panel of GARCH models. Statistica Sinica, 21 (1) 307-329.

Sheppard, K. Sheppard, K. (2012) Forecasting High Dimensional Covariance Matrices, Handbook of Volatility Models and Their Applications, Vol. 3. Pakel, C., Shephard N. and Sheppard K. (2011) Nuisance parameters, composite likelihoods and a panel of GARCH models. Statistica Sinica, 21 (1) 307-329.

Zhou, X. and He, X. (2011) Portfolio choice via quantiles. Mathematical Finance, 21 (2), 203-231. Zhou, X. and Jin, H. (2011) Greed, leverage, and potential losses: A prospect theory perspective. Mathematical Finance. Zhou, X., Jin, H. and Zhang, S. (2011) Behavioral portfolio selection with loss control. Acta Mathematica Sinica, 27 (2), 255-274. Zhou, X., Meyer-Brandis, T. and Øksendal, B. (2012) A mean-field stochastic maximum principle via Malliavin calculus. Stochastics. Xu, Z. and Zhou, X. (2012), “Optimal stopping under probability distortion”, Annals of Applied Probability. Hu, Y. Jin, H., and Zhou, X. (2012), “Time-inconsistent stochastic linearquadratic control”, SIAM Journal on Control and Optimization. Bjork, T., Murgoci, A. and Zhou, X. (2012), “Mean-variance portfolio optimization with state dependent risk aversion”. Mathematical Finance.

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, 415 (1), 251-256.

Zariphopoulou, T.

RES

Roberts, S., Reece, S., Nicholson D. and Lloyd, C. (2011) Determining intent using hard/soft data and Gaussian process classifiers. 2011 Proceedings of the 14th International Conference on Information Fusion. 1-8.

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AN INTRODUCTION Man: Innovating to perform The history of Man is a story of innovation,

GLG is a leading discretionary, multi-strategy global investment

accomplishment and true entrepreneurial spirit.

manager that offers a range of alternative and long-only investment

From our foundations as a barrel maker in 1783, the company has evolved into today being one

strategies. Founded in 1995, GLG has built up one of the world’s most widely respected teams of investment professionals covering equity, macro, emerging markets, credit, convertible bond and

of the world’s largest independent alternative

thematic strategies. The team of 130 investment professionals1 is

investment managers. We offer a comprehensive

recruited and retained through a performance-oriented culture which

range of transparent, dynamic and thematic

continually attracts and further develops leading investment talent.

trading strategies across the liquidity spectrum

FRM is a top 10 global industry allocator to hedge funds by AUM,

to a highly-diversified client base.

and the largest independent European based FoHF managing commingled funds and advising institutional clients2. It has one

Our principal objective is to deliver strong and stable returns for our

of the industry’s largest research and investment teams – located

investors. We achieve this by leveraging our:

in London, New York, Tokyo, Guernsey, Singapore and Pfäffikon

•

Agility – Man has constantly evolved and reinvented itself, setting the agenda as a leader and spotting new opportunities and markets as a company of innovators

•

(Switzerland) – who are supported by institutional quality infrastructure. The business offers clients a wide range of multimanager investment services across the liquidity spectrum, from commingled FoHFs through managed accounts (‘MACs’) to risk

Heritage – With over 225 years of trading experience and

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of experience in building MAC capabilities and USD 7.6 billion

reassurance of longevity that clients are looking for in a rapidly

invested via MACs3, FRM’s industry-leading platform provides the

changing financial world

access and transparency to promote better investment decisions.

Foresight – We employ an enviable team of seasoned market

Man also offers access to individual managers which excel in specialist

professionals who have the expertise, talent and speed of

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systematic trading experience of AHL, GLG and Man and aims to build

Man’s business model comprises a compelling array of investment products and investment management expertise. Whilst the underlying specialists operate independently of one another, they all benefit from Man’s robust infrastructure, financial backing and centralised support functions. This allows the teams to concentrate solely on investment management and research. AHL is Man’s industry-leading quantitative managed futures manager. It aims to identify and profit from market trends and other inefficiencies by employing systematic trading models, which are backed by its unrivalled research capabilities and a unique collaboration with the University of Oxford in the form of the OxfordMan Institute. Founded in 1987, AHL is a pioneer in the application of systematic trading and has established itself as a world leader in its field, with an enviable track record of delivering double-digit annualised returns with low correlation to traditional assets.

www.man.com

profitable strategies driven by cutting-edge technology. Our in-house convertible bonds team operates a range of geographically-focused portfolios consisting of composite securities which combine the attributes of equities and conventional bonds. And finally, Nephila4, a market leader in insurance-linked investments, provides investors with access to a zero-beta return stream that has minimal correlation to the performance of other asset classes. Source: Man database. 1. Figures listed are as at 30 June 2012 unless otherwise stated. 2. Source: Man and Towers Watson Global Alternatives Survey, JUL 2012, based on YE 2011 AUM. 3. As at 1 July 2012. 4. Please note that Nephila is a Man affiliated manager but is not wholly owned by Man Group plc. This material is communicated by Man Investments Limited which is authorised and regulated in the UK by the Financial Services Authority. This material is for information purposes only and it is not intended to be a solicitation or invitation to invest. Any organisations or products described in this material are mentioned for reference purposes only. This material is intended only for investment professionals and professional clients and must not be relied upon by any other person. It is proprietary information of Man Investments Limited and its affiliates and may not be reproduced or otherwise disseminated in whole or in part without prior consent from Man Investments Limited. Alternative investments can involve significant risks and the value of an investment may go down as well as up. There is no guarantee of trading performance and past or projected performance is not a reliable indicator of future performance. We recommend to consult your bank, investment and/or tax adviser.

www.oxford-man.ox.ac.uk

designed and produced by Annabel Percy-Hughes