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MathFinance Conference 2018

MathFinance hosts the annual Conference in Frankfurt which is tailored to the European finance community. Providing cutting-edge research and brand new practical applications, the conference is intended for practitioners in the areas of trading, quantitative or derivative research, risk and asset management, insurance as well as for academics studying or researching in the field of financial mathematics.

As always, we expect around 100 delegates both from the academia and the industry. This ensures a unique networking opportunity which should not be missed. A blend of world renowned speakers ensure that a variety of topics and issues of immediate importance are covered.

This event is a must for everyone in the quantitative financial industry.


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MathFinance Conference 16/17 April 2018



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Dr. Tomasz R. Bielecki

Dr. Tomasz R. Bielecki

Professor of Applied Mathematics

Illinois Institute of Technology

A Dynamic Model of Central Counterparty Risk

We introduce a dynamic model of the default waterfall of derivatives CCPs and propose a risk sensitive method for sizing the initial margin (IM), and the default fund (DF) and its allocation among clearing members. Using a Markovian structure model of joint credit migrations, our evaluation of DF takes into account the joint credit quality of clearing members as they evolve over time. Another important aspect of the proposed methodology is the use of the time consistent dynamic risk measures for computation of IM and DF. We carry out a comprehensive numerical study, where, in particular, we analyze the advantages of the proposed methodology and its comparison with the currently prevailing methods used in industry.

Tomasz R. Bielecki is Professor of Applied Mathematics at the Illinois Institute of Technology, Chicago, IL. He is an author of numerous research papers in the areas of stochastic analysis, stochastic control, manufacturing systems, operations research and mathematical finance. He is a co-author, with Marek Rutkowski, of the monograph “Credit Risk: Modeling, Valuation and Hedging,” which was published by Springer-Verlag in 2002 and reprinted in 2004.  He also co-authored, jointly with Monique Jeanblanc and Marek Rutkowski the monograph “Credit Risk Modeling” published by the Osaka University Press in 2010. His most recent book, “Counterparty Risk and Funding,” coauthored with Stephane Crepey and Damiano Brigo was published in 2014.  He has been a recipient of five research grants from the National Science Foundation, as well as research grants sponsored by the Moody’s Investors Service.

The areas of his current research interests include valuation and hedging of counterparty risk, modeling of structured dependence between stochastic processes, mathematical theory of dynamic performance assessment indices, Wiener-Hopf decomposition methods for Markov processes, as well as applications of stochastic control to optimal portfolio selection. He has previously held academic positions in the Warsaw School of Economics, University of Kansas, University of Illinois at Chicago, Northeastern Illinois University, and a visiting position in the New York University. He also held several visiting positions at numerous universities and research institutions Worldwide. Tomasz R. Bielecki currently serves as an associate editor of Mathematical Finance, SIAM J. Control and Optimization, International Journal of Applied and Theoretical Finance, European Journal of Pure and Applied Math, Stochastics and International Journal of Portfolio Analysis & Management.  He consulted for Argonne National Laboratory, Bank One, Quantitative Risk Management, Bloomberg, AVM LLC and Merrill Lynch.

Lynn Boen

Lynn Boen

PhD student

Department of Mathematics and Computer Science
University of Antwerp

Symposium on Computational Finance:
Building and Calibrating Multivariate Sato Models with Linear Dependence

The increased trading in multi-name financial products requires the development of state-of-the-art multivariate models. These models should be computationally tractable and at the same time flexible enough to explain the stylized facts of asset log-returns and of their dependence structure. In this talk, we propose a general framework for multivariate models characterized by independent and time-inhomogeneous increments. This is done by space-scaling linear combinations of independent self-decomposable random variables, leading to multivariate models of the Sato type. Dependence between the assets is introduced linearly by considering for each asset both an idiosyncratic and a systemic risk factor. As an example, we elaborate the case of VG and difference of Gamma distributed risk factors and calibrate the resulting multivariate models on a basket of three major stocks included in the DJIA. The calibration is performed on a wide set of quoting days exhibiting different levels of market fear, using two different calibration methodologies.

Lynn Boen is a PhD student at the Department of Mathematics and Computer Science of the University of Antwerp (Belgium), the same university where she obtained her M.Sc. in Financial Mathematics in 2016. During the last semester of her master studies, she combined studying and working as a part-time consultant on data analysis at Atlas Copco in Wilrijk, Belgium. Right after graduation, she started her PhD research on ‘Developing and calibrating tractable cutting-edge multivariate financial models’ under the supervision of Prof. Dr. Ir. Florence Guillaume. After having worked for two years on multivariate financial models based on processes with independent and time-inhomogeneous increments, she very recently started working on efficient and stable numerical solutions of multidimensional PIDEs for the fair value of basket options, under the supervision of Prof. Dr. Karel in ‘t Hout.

Andrea Fontanari

Andrea Fontanari

PhD Student

TU Delft


Symposium on Computational Finance:
Bayesian Non-Parametric Approach to Joint and Last Survivor Annuities Pricing

In this paper we propose a new modeling approach to annuities pricing based on Bayesian non-parametric, focusing mainly on the joint and last survivor products. Differently from the other works in the literature about annuity pricing, our methodology doesn’t require any specifc assumption on the functional form of the marginal and joint distribution. Exploiting the updating mechanisms intrinsic in the Bayes theorem the model can learn autonomously from the data and can adapt to changes in the mortality hence minimizing the misspecifcation risk. Additionally, if prior beliefs about the dependence structure and/or the marginal survivorships are present, we are going to show how these can be easily embedded in the model. Thanks to this features the resulting annuity price will be a combination of the analyst expectations and the information embedded in the data. Empirically, the model can be calibrated via simulation-based techniques, like Markov Chain Monte Carlo. In particular we propose a simple estimation via Gibbs-Sampling. Using the dataset of Frees et al. (2016) as well as customized data, we discuss how the new model can be used in practice, also showing how different priors affect the calibration. Finally we compare our results with copula based pricing models which can be considered the gold Standard of the present methodologies.


Andrea Fontanari is a PhD student at the Delft University of Technology in the Applied Probability group. He is also affiliated with the Centrum Wiskunde and Informatica (CWI) in Amsterdam where he is part of the Scientific Computing group. His research interests include Extreme Value Theory, Inequality and Variably measures for financial data, and Bayesian methods. He is currently working with his supervisors Prof. Dr. C. W. Oosterlee and Dr. P. Cirillo on implementing Bayesian non-parametric techniques for life insurance products pricing. His previous works regard the study of variability measures and their application to risk management.

Andrea obtained a master degree in Economics from Bocconi University in Milan.

As part of his PhD program, Andrea is also a member of the Financial Service Risk team of EY based in Amsterdam where he is involved in model validation and risk assessments.

Silke Glas

Silke Glas

Research Assistant and PhD Student

University of Ulm

Symposium on Computational Finance:
Model Reduction Techniques for Finance

In this work, we consider model reduction techniques for finance motivated problems, e.g., option pricing or optimal trading. In the event that our specific problem does not admit a closed-form expression, one needs numerical methods to gain information about the solution. In many applications, we have a parameter-dependent setting, where the same task needs to be solved multiple times for incoming market data.
For each parameter, we want to achieve a numerical solution with sufficient accuracy. Thus, we need fine discretizations, which can resolve in long computation times. Therefore, we use the reduced basis method (RBM) to derive a reduced model, in which we can evaluate solutions for each parameter with less computational effort.
In particular, we consider the intraday trading of electricity and derive a parametrized Hamilton-Jacobi-Bellman (HJB) equation for this setting. Our aim is to find an optimal strategy within the short trading time using the most recent information of the market. Then, we analyze the reducibility with the RBM of this parametric HJB setting. We comment on the (to our knowledge) only existing approach for this problem and provide numerical investigations for our method.

Silke Glas is a research assistant at Ulm University. Her current research is about model reduction for Hamilton-Jacobi-Bellman (HJB) equations resulting from intraday trading of electricity. Particularly, she is interested in HJB equations, for which the control is not representable by first order conditions.
She is doing her PhD in a joint project with University of Duisburg-Essen and Ulm University under the supervision of Prof. Dr. Karsten Urban and Prof. Dr. Rüdiger Kiesel. The topic of her PhD is model reduction for variational inequalities, motivated by the calibration of American Options.

Peter Hahn

Peter Hahn

Head of eFX Sales Frankfurt


Electronic FX Trading in 2018


Peter Hahn  is the Head of eFX Sales Frankfurt at Commerzbank AG. Peter has had several roles at Commerzbank, from starting in FX Corporate Sales, then Trading Spot, Forwards and Options in Germany, Chicago and Amsterdam he went to our Deutsche Terminbörse Department in the early 90’s doing Derivatives Sales of Stock Options and Bund Futures & Options.
After 4 years Peter went back into FX Corporate Sales, then built the Bank Sales Team and later became responsible for all FX Sales in Germany and London.
In 2006 he also ran the Commodity Sales Project, which is now implemented in the several teams. After the merger with Dresdner Bank Peter moved on to the eFX world.

Dr. Jürgen Hakala

Dr. Jürgen Hakala

Managing Director

Leonteq Securities AG

Machine Learning Applied to SLV Calibration

We calibrate local stochastic volatility using the particle method developed by [Guyon, Henry-Labordere]. A critical step in this method is an estimation of the conditional expectation of the stochastic volatility process, given the realized spot. We reformulate this estimation as a non-linear regression at each time-step of the discretized process, which allows us to apply machine learning (ML) techniques.  We review appropriate ML techniques, compare results and Performance.


Jürgen works for Leonteq Securities AG, where he is involved in modelling and financial engineering for all asset classes. His interests are numerical methods in mathematical finance, in particular multi-asset and hybrid modelling, as well as the impact of regulation onto markets and models. Backed by his PhD was on Neural Network he recently rekindled his interest in machine learning methods, now applied to problems in financial engineering. Initially he worked on foreign exchange, where he is co-editor of a textbook about FX derivatives.


Dr. Karl Friedrich Hofmann

Dr. Karl Friedrich Hofmann

Head of Germany’s Quantitative Modeling Group


Dr. Patrick Büchel

Head of Counterparty ABS Risk & Exposure Management


An HJM-type stochastic spread Model – Theory and Implementation

We present an interest rate model incorporating stochasticity on spreads on the OIS rate, employing a Trolle–Schwartz model for the OIS rate and a Trolle-Schwartz commodity model for the spreads, both facilitating stochastic volatility. We describe the model’s basic properties and derive necessary drift conditions following the works of Grbac and Rungaldier to ensure arbitrage-free-ness under the OIS numéraire. Moreover, we provide a simulation scheme for the hypothetical spread bonds and derive a semi-closed-form solution based on Fourier pricing methods for bond options on spread-bearing zero bonds.





Karl Hofmann leads Deloitte Germany’s quantitative modeling group and is chief developer of Deloitte’s valuation library Deloitte Valuation Analytics, he joined Deloitte in 2013. His main interests are exotic derivatives and XVA pricing and modeling as well as efficient implementations of financial models. Karl holds a PhD in Mathematics of the Technische Universität München and is lecturer at the HTW Berlin.

Prof. Jessica James

Prof. Jessica James

Managing Director Senior Quantitative Researcher


Cross Currency Basis – what drives it?

The cross currency basis is a market dislocation in a fundamental sense, breaking some of the fundamental arbitrage conditions many of us grew up with.  Where does it come from, and how does it persist?  We drill down to show exactly what it is and why it is not traded away, revealing some really opportunities and dispelling some often-repeated myths.

Jessica James is the Senior Quantitative Researcher in the Rates Research team at Commerzbank, where she covers foreign exchange and fixed income. She joined Commerzbank from Citigroup where she was Global Head of the Quantitative Investor Solutions Group. Previously, she lectured in physics at Trinity College, Oxford.
Significant publications include ‘FX Option Performance’, ‘Handbook of Foreign Exchange’ (Wiley), ‘Interest Rate Modelling’ (Wiley), and ‘Currency Management’ (Risk books).
She is on the Board of the Journal of Quantitative Finance, a Fellow of the Institute of Physics, and is a Visiting Professor at UCL and Cass Business School.

Dr. Christian Kappen

Dr. Christian Kappen

Manager Financial Engineering


Approximating MVA along Low-Dimensional State Spaces

The Initial Margin Valuation Adjustment (MVA) is the present value of the opportunity and funding costs generated by future initial margin amounts. Computing MVA requires long-term risk neutral simulations of future initial margin requirements. In this talk, I present a novel method for approximating ISDA SIMM based MVA. It consists in projecting the SIMM measures from high-dimensional risk factor spaces to lower-dimensional subspaces parametrized by model state variables, calibrated model parameters or explanatory variables. ISDA SIMM based MVA can thus be approximated in terms of future sensitivities with respect to this more accessible and lower-dimensional vector of model-specific risk vectors. This projection method uses principal components analysis, and it fits in naturally with American Monte Carlo techniques.

Christian Kappen is Manager in the Financial Engineering unit at d-fine, a leading consultancy company in risk and finance. In this role he manages and delivers client projects on current valuation and risk management topics. He is specialized in the validation and the development of counterparty credit risk models. Christian previously worked as a researcher in pure mathematics at the University of Duisburg-Essen. He holds a Ph.D. and a Diploma from the University of Münster.

Bryan Liang

Bryan Liang

Senior Quant Researcher


Corridor Variance Swap Spread

Two-underlying corridor variance swap spreads have become very popular in recent years. Dealers use them to hedge the vega exposure of their retail structured product flows (autocallable and Uridashi notes); For investors they provide attractive opportunities to capture the relative variance risk premium between different markets. However currently there is very limited literature available. In this talk we examine the use of two-underlying corridor variance swap spreads from both sell-side and buy-side perspectives, and investigate different valuation approaches, especially the impact of stochastic volatility, correlations and quanto effect.

Bryan Liang is a senior quant researcher at Bloomberg LP. He joined the Bloomberg quant research team in 2011 and has been working extensively on various aspects of derivatives modelling, including pricing, hedging, structuring, market making, trading strategies and parallel computing. Prior to joining Bloomberg, he worked for derivatives analysis group at Goldman Sachs, covering interest rate derivatives modelling. Bryan received his Ph.D. in mathematics from University of Michigan. Currently He is also an adjunct professor at the Courant Institute NYU and Columbia University.

Dr. Jacopo Mancin

Dr. Jacopo Mancin


Barclays Capital

Volatility Swaps: PDE pricing improvements for LSV frameworks

The hedging of Volatility Swaps is model-dependent and highly exposed to volatility oscillations and the concavity effect. This, together with the increasing volume of traded Volatility Swaps, demanded for a fast and reliable PDE pricer. We present a development of the 1 Factor PDE pricer that is able to closely replicate LSV Montecarlo prices also in the context of fairly oscillating volatility term structure, while sensibly improving the computational performance. Furthermore, we developed some insights on the hedging of Volatility Swaps, showing a rather different behavior between short and long expiries.

Our analysis is based on FX Volatility Swaps, but can be equivalently applied to other asset classes.

Jacopo is a quant at Barclays, which he joined originally as intern in March 2017. His main focus is FX Volatility products PDE pricing. Prior to that he earned a PhD in Financial Mathematics at the LMU University in Munich, working on Model Uncertainty under the supervision of Prof. Dr. Francesca Biagini.

Dr William A. McGhee

Dr William A. McGhee

Global Head of Quantitative Analytics

NatWest Markets

Machine Learning in Quantitative Finance

  • History of Machine Learning : from MENACE to Alpha Go Zero
  • Machine Learning Algorithms
  • The changing role of Quants
  • Applications within Quantitative Finance
  • Practical considerations : from technology to governance.

William started his quant career in 1994 with J.P. Morgan in the Currency Options business. In 1998 he joined Deutsche Bank where he became Global Head of FX Quantitative Analytics. He worked between 2003 and 2009 at Citi in a number of roles encompassing structuring, exotics trading and heading up the FX Quantitative Strategy Group.
He joined RBS in 2009 to run the multi-asset Hybrid Quantitative Analytics team. In his current position as Global Head of Quantitative Analytics he is responsible for all modelling within the investment bank – from electronic trading to vanilla and complex derivatives.
William holds a PhD in Mathematical Physics, is a Fellow of the Institute of Mathematics and It’s Applications and serves on the UK Parliamentary and Scientific Committee.

Dr. Bereshad Nonas

Dr. Bereshad Nonas

Head of Quantitative Analysis & Modelling

Scope Ratings AG

Simulating Hedge Fund Strategies: Generalising Fund Performance

We apply a multipath search algorithm to map hedge fund performance to a set of market indices. We show that a carefully selected set of market indices is sufficient to capture most features of hedge fund return distributions. In a second step, we demonstrate that this mapping can be generalised to project hedge fund strategies onto the market indices. We calibrated asymmetric GARCH processes to the indices and ran out-of-sample tests for the combined model. The resulting distributions capture the downside risk of almost all hedge fund strategies in our sample set. Finally, we extend the mechanics to simulate the return distribution of portfolios of hedge funds and show that this produces a conservative estimate. The technique is well suited to assess the risk of collateralised fund obligations and other similar products.

Bereshad runs the Quantitative Analysis and Modelling team at Scope Ratings. The team is responsible for quantitative rating model development and provides quantitative support to the other franchises of the Scope Group. Bereshad joined Scope from Barclays in 2015 where he was jointly responsible for the development of the investment bank’s counterparty credit risk model. Before that he worked as a Commodities Quant at Barclays and as a Fixed Income Quant at Commerzbank. He holds a PhD in Physics from RWTH Aachen.

Prof. Luis Ortiz-Gracia

Prof. Luis Ortiz-Gracia

Visiting Professor

Universitat de Barcelona School of Economics

Symposium on Computational Finance:
Quantifying Credit Portfolio Losses under Multi-Factor Models

In this work, we investigate the challenging problem of estimating credit risk measures of portfolios with exposure concentration under the multi-factor Gaussian and multi-factor t-copula models. It is well-known that Monte Carlo (MC) methods are highly demanding from the computational point of view in the aforementioned situations. We present efficient and robust numerical techniques based on the Haar wavelets theory for recovering the cumulative distribution function (CDF) of the loss variable from its characteristic function. To the best of our knowledge, this is the first time that multi-factor t-copula models are considered outside the MC framework. The analysis of the approximation error and the results obtained in the numerical experiments section show a reliable and useful machinery for credit risk capital measurement purposes in line with Pillar II of the Basel Accords.

Luis Ortiz Gracia is visiting professor at the Universitat de Barcelona School of Economics. He obtained a PhD in Mathematics from the Polytechnic University of Catalonia. His fields of research are Computational Finance and Quantitative Risk Management, with particular interests on wavelets-based methods for option pricing and aggregate risk measurement. He teaches Computational Aspects of Risk Management in the Master of Mathematics in Finance at the Autonomous University of Barcelona and Advanced Risk Quantification in the Master of Actuarial and Financial Sciences at the University of Barcelona. He led the Financial Mathematics and Risk Control research group at the Centre de Recerca Matemàtica and carried out research stays at the CWI in the Netherlands as well as in the School of Mathematics and Physics at the University of Queensland in Australia. Before he moved to the academia, he spent some years working on quantitative projects in several private firms within the fields of information technology, business and finance.

Prof. Rolf Poulsen

Prof. Rolf Poulsen

Professor of Mathematical Finance

 University of Copenhagen

How Accurately Did Markets Predict the GBP/USD Exchange Rate Around the Brexit Referendum?

We develop a model for the British pound/US dollar exchange rate around the Brexit referendum in June 2016. Applying the model to a combination of betting market odds and financial option prices, we show that while Leave was the least likely outcome (more unlikely, in fact, than betting odds would immediately suggest), predictions of the exchange rate conditional on the outcome of the referendum were accurate and the market was able to separate its views on the likelihood and the impact of Brexit.

Rolf Poulsen is a professor of Mathematical Finance at the Dept. of Math. Sciences at the University of Copenhagen. His main research interest is quantitative methods for pricing and hedging of derivatives. He will talk about exchange rate markets at length to all who will listen – and some who won’t.

Adil Reghaï

Adil Reghaï

Head of Quantitative Research for Equities and Commodities


The Fair Pricing under Local Stochastic Volatility

Choosing the right mixture between local volatility and stochastic volatility has an important impact on valuation. We propose to link this blending factor with the dynamic of the volatility introducing new formulae linking the mixing weight and the skew stickiness ratio.


Adil Reghaï joined Natixis since 2008 where he is Head of Quantitative Research for Equities and Commodities. Graduated from Ecole Polytechnique (X92) and Ecole des Mines (P94), Paris, Adil was Head of Quantitative Research at Merrill Lynch, BNP Paribas and Calyon. He attended conferences on mathematical finance and has written numerous papers and articles.
He is the author of many scientific publications and a book Quantitative finance: back to basic principles
He is a lecturer at the mathematical master in Nice (SKEMA – France).

Prof. Christoph Reisinger

Prof. Christoph Reisinger

Professor of Applied Mathematics

University of Oxford

Efficient Exposure Computation by Risk Factor Decomposition

The focus of this talk is the efficient computation of counterparty credit risk exposure on portfolio level. Here, the large number of risk factors rules out traditional PDE-based techniques and allows only a relatively small number of paths for nested Monte Carlo simulations, resulting in large variances of estimators in practice. We propose a novel approach based on Kolmogorov forward and backward PDEs, where we counter the high dimensionality by a generalisation of anchored-ANOVA decompositions. By computing only the most significant terms in the decomposition, the dimensionality is reduced effectively,  such that a significant computational speed-up arises from the high accuracy of PDE schemes in low dimensions compared to Monte Carlo estimation. Moreover, we show how this  truncated decomposition can be used as control variate for the full high-dimensional  model, such that any approximation errors can be corrected while a substantial variance reduction is achieved compared to the standard simulation approach. We investigate the accuracy for a realistic portfolio of exchange options, interest rate and cross-currency swaps under a fully calibrated ten-factor model. (Joint work with Kees de Graaf and Drona Kandhai)


Christoph Reisinger is Professor of Applied Mathematics at the University of Oxford, an Associate Member of the Oxford-Man Institute of Quantitative Finance, and a Member of the Oxford-Nie Financial Big Data Lab. His research covers various aspects of the development, analysis and implementation of mathematical models and numerical algorithms in financial engineering.

He is Co-Editor-in-Chief of Applied Mathematical Finance and serves on the editorial board of The Journal of Computational Finance.

At Oxford, Christoph teaches various Masters level courses in the areas of computational finance, numerical analysis, and partial differential equations. He was Course Director of the professional MSc in Mathematical Finance at Oxford for over six years. He is a tutor for Applied Mathematics at St Catherine’s College, Oxford.

Artur Sepp

Artur Sepp

Quantitative Strategist

Julius Bär

Applications of Machine Learning for Volatility Trading and Asset Allocation

Most applications of quantitative trading and investing require the forecast of the future realized volatility as a key input. While there are many models for volatility measurement and forecast, the key decision is how to select the best models with the highest predicative power for a given application. I apply the methods of supervised machine learning and learning to rank for the machine-based selection of volatility models. I demonstrate applications of this framework to trading strategies in implied vs realized volatilities, to designing volatility-targeting products, and to implementing risk-based and risk-parity allocations.


Artur Sepp works as a Quantitative Strategist at the Swiss wealth management company Julius Baer in Zurich. His focus is on quantitative models for systematic trading strategies, risk-based asset allocation, and volatility trading. Prior to that, Artur worked as a front office quant in equity and credit at Bank of America, Merrill Lynch and Bear Stearns in New York and London with emphasis on volatility modelling and multi- and cross-asset derivatives valuation, trading and risk-managing. His research area and expertise are on econometric data analysis, machine learning, and computational methods with their applications for quantitative trading strategies, asset allocation and wealth management. Artur has a PhD in Statistics focused on stopping time problems of jump-diffusion processes, an MSc in Industrial Engineering from Northwestern University in Chicago, and a BA in Mathematical Economics. Artur has published several research articles on quantitative finance in leading journals and he is known for his contributions to stochastic volatility and credit risk modelling. He is a member of the editorial board of the Journal of Computational Finance. Artur keeps a regular blog on quant finance and trading at

Dr. Klaus Spanderen

Dr. Klaus Spanderen

Head of Pricing and Valuation

Uniper SE

Symposium on Computational Finance:
Applications of the Heston Stochastic Local Volatility Model in Commodity Markets

Stochastic Local Volatility (SLV) models have become the industry standard for FX and equity markets. The local volatility extension of the popular Heston stochastic volatility model is a promising candidate within the zoo of SLV models. The Local Volatility component of the Heston SLV model allows for a natural modelling approach of the Samuelson effect, which is prevailing in commodity markets. In addition, the stochastic component assures consistent pricing over the entire volatility skew. The presentation introduces a multi-factor Heston SLV model for the forward curve dynamics in conjunction with a day-ahead process including potential spikes. An efficient calibration algorithm together with a fast and accurate simulation scheme allows for fast pricing of Asian averaging, multi-callable/Bermudan-style options, which are common in gas markets.

Klaus Spanderen runs the Pricing and Valuation team at Uniper SE. He has over 15 years’ experience as an interest rate, equity and commodity quant. His current research interests include partial differential equations with applications in computational finance, hedging performance of trading strategies in incomplete markets and high performance computing. Klaus Spanderen holds a Ph.D. in Theoretical Particle Physics from the University of Muenster.

Prof. Dr. Uwe Wystup

Prof. Dr. Uwe Wystup

Managing Director


FX Volatility 101 Exam


Uwe Wystup is managing director of MathFinance AG. Before, he has actively worked in FX derivatives trading as Financial Engineer, Global Structured Risk Manager and Advisor since 1992, including Citibank, UBS, Sal. Oppenheim and Commerzbank. He is one of the few hybrids in the world working in the intersection of the derivates market and academic research.
Uwe earned his PhD in mathematical finance from Carnegie Mellon University, is currently Professor of Financial Option Price Modeling and Foreign Exchange Derivatives at University of Antwerp and Honorary Professor of Quantitative Finance at Frankfurt School of Finance & Management.
Together with his team at MathFinance he provides independent (re-)structuring, valuation, model validation and expert witness services.
His first book Foreign Exchange Risk was published in 2002, quickly became the market standard and has also been translated into Mandarin. His second book FX and Structured Products appeared in 2006 with a fully updated and expanded second edition in 2017. Many of his papers appeared in scientific journals.