Frankfurt MathFinance Conference

26 - 27 March 2012

Saeed Amen, Nomura & Thalesians Ltd Saeed started his career at Lehman Brothers. He worked on the FX desk developing systematic trading models for both G10 and EM and was part of the team who developed the MarQCuS suite of models. He was also responsible for a systematic FX prop trading book and conducted research around high frequency FX including economic events. He currently works at Nomura as a Vice President in Quantitative Strategy, also in FX, developing their model infrastructure. He also covers gold for Nomura and his work on gold has been quoted by ZeroHedge, WSJ and La Republica. He graduated from Imperial College with a first class honours master's degree in Mathematics and Computer Science. Saeed Amen is a Managing Director at Thalesians Ltd also.

What drives gold? We discuss the various historical drivers for gold, looking at factors such as real rates, relationship with USD and also risk sentiment. We shall discuss the decomposition of end user demand for gold and the impact of factors on gold such as increases in margin requirements, central bank buying and QE. We examine the various possible scenarios for the gold price over the coming years, with a focus on our own view.

Peter Austing, Quantitative Analytics, Barclays Capital Peter moved from mathematical physics to finance in 2004. He has been in his current role in the quantitative analytics team at Barclays Capital for five years, and is particularly interested in correlation and volatility modeling for foreign exchange derivatives.

Valuing Basket Options with Asset and Correlation Smiles We show how to value basket options with both asset smiles and correlation smiles. The instrument best used to mark the implied correlation smile depends on the asset class. For equity baskets, two-asset sub-baskets can be used, and we provide an analytic on-smile valuation for N-asset baskets. For FX-baskets, cross-vanillas determine the correlation smiles, and we provide a semi-analytic valuation. Effectiveness of the method is demonstrated by comparing against an alternative fully dynamic (but heavy) model.

Dr. Eric Benhamou, Pricing Partners Eric Benhamou is the CEO of Pricing Partners, an international software developer of derivatives pricing analytics and a service provider of independent valuation for all OTC derivatives. Current coverage includes interest rates, credit, equity, inflation, foreign exchange, commodities, insurance derivatives and hybrids.
Eric Benhamou is also known for its endeavor to gather financial institutions, start-ups and public research centers on collaborative innovation in financial mathematics. Previously, he headed the fixed income quantitative research at Ixis CIB, joining from Goldman Sachs. He is a regular speaker at professional conferences and has published various articles on subjects like advanced Monte Carlo simulation, inflation derivatives and other option pricing results. A former alumnus of the Ecole Polytechnique, the ENSAE, he holds a Ph.D. in financial mathematics from the London School of Economics.


Prof. Jin-Chuan Duan, National University of Singapore Duan is the Director of Risk Management Institute at the National University of Singapore (NUS) and concurrently holds the Cycle & Carriage Professorship in Finance at the NUS Business School. He is an Academician of Academia Sinica and also holds a visiting distinguished research chair at National Taiwan University. Duan completed his undergraduate education at the National Taiwan University, an MBA from the State University of New York at Albany and a PhD in Finance from the University of Wisconsin-Madison. He specializes in financial engineering and risk management, and is known for his work on the GARCH option pricing model. He has authored numerous scholarly publications on derivative securities and risk management, and written a book and occasional media commentaries on current financial/economic events. Before joining the NUS, Duan held the Manulife Chair Professorship at the Rotman School of Management, University of Toronto, and also once taught at the Hong Kong University Science and Technology and McGill University. Duan is spearheading a non-profit credit research initiative launched in 2009, which pioneers a "public good" approach to credit rating reform via a Wikipedia-style model development undertaking. The initiative currently provides daily updated default forecasts for over 28,000 exchange-listed firms in 30 economies in Asia, North America and Europe, and the usage is free ( ).

Dynamic Default Predictions and a Bottom-Up Approach to Credit Portfolio Management This talk comprises two parts. First, the forward intensity method for corporate default predictions proposed by Duan, Sun and Wang (2011) will be introduced with discussions on its conceptual foundation, econometric formulation, implementation issues and empirical findings on the US data. The talk will also touch upon the role of momentum in default prediction and a useful distance-to-default treatment for financial firms if one wants to include them in the sample. The forward intensity method powers the default prediction system of the non-profit Credit Research Initiative (CRI) by the Risk Management Institute of National University Singapore. The CRI currently produces daily updated default predictions, from one month to two years ahead, for about 30,000 exchange-listed firms in 30 economies in Asia, North America and Europe. In the second part of the talk, I will show how one can utilize the freely accessible CRI infrastructure for credit portfolio management. Since the forward intensity approach considers all obligors jointly and dynamically, it naturally forms a bottom-up approach to modelling credit portfolios. An example will be used to demonstrate this application.

Prof. Matthias Fengler, University St. Gallen (HSG) Matthias Fengler is Assistant Professor of Financial Econometrics at the School of Economics and Political Science of the University St. Gallen (HSG). Before joining St. Gallen University, he spent six years as a senior quantitative analyst in the investment banking branch of Sal. Oppenheim jr. & Cie, Frankfurt. His primary fields of expertise are modeling equity derivatives and financial statistics and econometrics. Matthias Fengler earned his PhD in Quantitative Finance at the Humboldt-Universität, Berlin. He is author of the textbook Semiparametric Modelling of Implied Volatility edited by the Springer-Verlag.

Semi-Nonparametric Estimation of the Call Price Surface Under No-Arbitrage Constraints When studying the economic content of cross sections of option price data, researchers either explicitly or implicitly view the discrete ensemble of observed option prices as a realization from a smooth surface defined across exercise prices and expiry dates. Yet despite adopting a surface perspective for estimation, it is common practice to infer the option pricing function, for each expiry date separately, slice by slice. In this paper, we suggest a semi-nonparametric estimator for the entire call price surface based on a tensor-product B-spline. To enforce no-arbitrage constraints in strike and calendar dimension we establish sufficient no-arbitrage conditions on the control net of the tensor product (TP) B-spline. Since these conditions are independent of the degrees of the underlying polynomials, the estimator can be parametrized with TP B-splines of arbitrary order. As example we estimate a smooth call price surface from S&P500; option quotes. From this estimate we obtain families of state price densities and empirical pricing kernels and a local volatility surface.

Dr. Jörg Kienitz, Deutsche PostBank Joerg Kienitz is the head of Quantitative Analysis at Deutsche Postbank AG. He is primarily involved in the development and implementation of models for pricing structured products, derivatives and asset allocation. He authored a number of quantitative finance papers and his book on Monte Carlo frameworks has been published in 2009 with Wiley. He is member of the editorial board of International Review of Applied Financial Issues and Economics. Joerg holds a Ph.D. in stochastic analysis and probability theory.

Adjoint Methods

Dr. Alexander Langnau, Allianz Alex Langnau is Global Head of Quantitative Analytics at Allianz Investment Management. He is also Visiting Scientist at the Ludwig- Maximillian University Munich. Prior to this he held various roles across the industry including Global Head of Quants across asset classes at Dresdner Bank, Global Head of Equity Derivatives Modelling at Merrill Lynch and Global Head of Exotic Equity Derivatives Modelling at Goldman Sachs. He started his career as a member of the Global Analytics team at Bakers Trust/Deutsche Bank. He holds a PhD in Theoretical Physics from the Stanford Linear Accelerator Center and completed his post-doc in the area of Theoretical Particle Physics at Cornell University. His current research interests include dynamic modelling of correlations and high frequency trading strategies.

Marking systemic RISK in the Merton model The downside risk of a portfolio of (equity)assets is generally substantially higher than the downside risk of its components. In particular in times of crises when assets tend to have high correlation, the understanding of this difference can be crucial in managing systemic risk of a portfolio. In this paper we generalize Merton's option formula in the presence jumps to the multi-asset case. It is shown how common jumps across assets provide an intuitive and powerful tool to describe systemic risk that is consistent with data. The methodology provides a new way to mark and risk-manage systemic risk of portfolios in a systematic way.

Roger Lee, University of Chicago Roger Lee is Associate Professor of Mathematics at the University of Chicago. Previously he held postdoctoral positions at Stanford University and NYU, and worked in Global Equity-Linked Products at Merrill Lynch in New York. His recent publications address robust approaches to pricing/hedging, asymptotics of implied volatility, and trading of realized volatility. He has a PhD from Stanford University and a BA from Harvard University.

Asymptotics of Implied Volatility to Arbitrary Order In a unified model-free framework that includes long-expiry, short-expiry, extreme-strike, and jointly-varying strike-expiry regimes, we find asymptotic implied volatility and implied variance formulas in terms of L, with rigorous error estimates of order 1/L to any given power, where L denotes the absolute log of an option price that approaches zero. Our results therefore sharpen, to arbitrarily high order of accuracy, the model-free asymptotics of implied volatility in extreme regimes. We then apply these general formulas to particular examples: Levy and Heston.

Joint work with Kun Gao.

Dr. Owen Matthews, Fintegral Owen Matthews studied physics at Leicester, in the UK, and performed research in theoretical astrophysics in Switzerland, Germany and India. He joined Fintegral in 2010 and is now a senior consultant specializing in stress testing and model validation for banks in Europe and the Middle East. He has implemented the stress-testing software for large and complex banking books, including the development of special treatments for sectors such as sovereigns.

From theory to practice – Project experience on designing and implementing a Universal Bank comprehensive Stress Testing framework We will present an example of a complete end-to-end Stress Testing implementation project and discuss the various challenges that had to be met for a large universal bank. The discussion will first focus on the design and integration of the various Risk & Finance elements that have to be included in a comprehensive and compliant Stress Testing framework. We will then move on to discuss the challenges and implications involved in embedding Stress and Scenario Testing in core bank processes and linking it to Risk Appetite in a practical example.

Dr. Attilio Meucci, Kepos Capital, LP & Attilio Meucci is a pioneer in advanced risk and portfolio management. His innovations include Entropy Pooling (technique for fully flexible portfolio construction), Factors on Demand (on-the-fly factor model for optimal hedging), Effective Number of Bets (entropy-eigenvalue statistic for diversification management), Fully Flexible Probabilities (technique for on-the-fly stress-test and estimation without re-pricing), and Copula-Marginal Algorithm (algorithm to generate panic copulas). Attilio is the founder of SYMMYS, under whose umbrella he designed and teaches the six-day ARPM Bootcamp, and manages the charity One More Reason. Attilio Meucci serves as the chief risk officer at Kepos Capital LP.

Attilio is the author of Risk and Asset Allocation - Springer and numerous other publications in practitioner and academic journals. He holds a BA summa cum laude in Physics from the University of Milan, an MA in Economics from Bocconi University, a PhD in Mathematics from the University of Milan and is a CFA chartholder.


Prof. Dr. Cornelis Oosterlee, Technical University of Delft Prof. Cornelis Oosterlee is a full professor in Applied Mathematics at the Delft University of Technology, the Netherlands, and he works as a group leader at the CWI, Centre for Mathematics and Computer Science in Amsterdam. His main field of research is Computational Finance, where he has cooperations with the Dutch financial industry. He is an associate editor for the Journal of Computational Finance.
He obtained his PhD from Delft University of Technology in 1993; spent 8 years in Germany at the National Research Center for Mathematics and Computer Science, in Sankt Augustin after that. He is a full professor since 2007. Oosterlee is teaching computational finance courses in Delft; he teaches Fourier methods in the MSc courses on numerical methods in finance at Oxford University, and taught a summer school in Tokyo in 2009, and in Cape Town in 2012.

On the applicability of the COS method for exotic option pricing In this presentation we will present our follow-up research, after the introduction of the COS method. The COS method is an efficient pricing method for financial derivatives, based on Fourier cosine expansions and the availability of the characteristic function. The method is being used by the financial industry within the calibration process for the pricing of European options.

Next to this kind of options, we recently succeeded to price Bermudans, arithmetic Asian options, multi-asset options (all with L'evy processes for the underlying) but also inflation options, the latter based on hybrid stochastic dynamics. In this presentation we will report on the extension of the COS method to solving these kinds of exotic products.

Professor Dr. Ekkehard Sachs, University of Trier Ekkehard Sachs is a Professor at the University of Trier and previously has held positions at Virginia Tech and North Carolina State University. He is an expert in numerical methods for optimization problems, in particular with partial differential equations and serves on various editorial boards of international journals in optimization. He has published three books and more than 100 research papers. His interest in finance is in calibration and hedging of options and, in particular, the numerical aspects of these tasks.


Erik Vynckier, Scottish Widows Investment Partnership Investment Director at the Financial Solutions Group of SWIP (Scottish Widows Investment Partnership), designing and implementing asset management and risk management for life insurance and pension fund clients, normally involving derivatives and innovative asset strategies.
Prior to SWIP, Erik worked at Credit Suisse First Boston in equity program trading, quantitative modeling of derivatives pricing and hedging, and asset-liability management for European life & pension clients; at HSBC in asset-liability structuring for the European life & pensions clients; and at Standard Life (Group) supporting the UK, German, Canadian and Hong Kong subsidiaries covering with-profits, fixed and variable annuities and guaranteed products. Erik commenced his career with positions in research & development and process engineering in the oil and chemical industry in Germany, the United States and the United Kingdom and has an MSc Chemical Engineering of the Rijksuniversiteit Gent (Belgium), Post-Doctoral research at the Institut Français du Pétrole (Lyon, France) and an MBA from the London Business School.

The Power of Dataflow Computing in Financial Engineering
Case Studies in Acceleration of Credit, Equity and Interest Rates Models.
The paper reviews algorithms to achieve ultra-high speed computation in financial engineering through dataflow computing at a considerable cost and time savings to the computer grids of today's datacentres. In fact, beyond getting computations done faster, the driving case is to move from overnight computations to real-time live updates with the market.
Dataflow computation streams the data through a computational pipeline, with results flowing out with every clocktick: a 200 Mhz pipeline enables up to 200 million results per second, further enhanced by potentially fitting hundreds of pipelines on a single node. The software model for dataflow computing centers around describing dataflow graphs spatially rather than the temporal programming in a multi-threaded environment on CPUs, The case studies presented here rely on the Maxeler suite of acceleration tools, in particular the MaxSpot profiler for statically and dynamically analysing code during the design phase of the algorithm and MaxCompiler creating binary dataflow pipelines from a Java representation.
Earlier successes in credit tranching (integrating the Gaussian copula with stochastic recovery, using pipelined fast Fourier transforms) and in equity derivatives (with pipelined random number generation for Heston's volatility process and for the Poisson jumps of the spot equity process) are reported, quoting achieved field-performance compared to single- or multi-threaded algorithms on multi-core Intel CPU. Original extensions to Cox-Ingersoll-Ross and to Stochastic Alpha Beta Rho (SABR) interest rate models are now presented to the MathFinance conference.
The ease of extending these models to stochastic local volatility using pipelined function evaluation - without loss of performance - is explained. The paper sketches the implementation of Forward Libor market models in a computational pipeline, a crucial breakthrough for rates trading desks, for whom real-time computation and calibration has so far hindered the widespread application of the Libor market model.

See presentation by JP Morgan at Stanford!

Joint paper with Oskar Mencer (Maxeler, Imperial College London) & Robin Bruce (Maxeler).

Produced by MathFinance AG - Last modified: November 2011