The bridge between
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academic research

MathFinance Conference

8th & 9th April 2019
Frankfurt, Germany

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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(Prime Price of EUR 735 only until 25th Jan 2019)

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Enjoyable atmosphere, lots of networking, expert speakers, way to learn developments in the industry

Artur Sepp

Quantitative Strategist, Julius Bär

The MathFinance conference provides an excellent environment to learn about recent developments and networking with leading experts from both industry and academia

Martin Simon

Risk Controller, Deka

The conference is a great opportunity to meet interesting people and develop new ideas on recent market trends. Special thanks to the organizers, they did a very good job

Eugen Tiganu

FX Options Product Manager, Murex

We would like to thank our sponsors:

MathFinance Conference 2019 is supported by:


Dr. Mark Beinker

Dr. Mark Beinker

Partner, d-fine GmbH


Derivative pricing: A pattern-matching problem?

Before computers became a handy tool available to everyone everywhere, pricing of financial options was mainly a matter of experience, a pattern matching task performed by human neural networks. This changed drastically with the advent of the Black-Scholes equation an the availability of computer power which enables everyone to price options in no time. Today, the pricing of derivatives has become a more complex, sophisticated exercise and enormous computer power is required to calculate all the needed portfolio-based risk figures (e.g. XVA). In this case study, we examine if the application of deep neural networks, which allow pricing of complex derivatives in constant time, could be helpful.

Dr. Mark Beinker is partner at d-fine GmbH and head of d-fine’s derivative valuation consulting business. With more than 20 years of experience in this field, he has been responsible for projects covering all aspects related to derivative pricing, including specification, implementation and testing of new and old pricing models, selection of third party tools, estimation of risk figures and valuation adjustments, and the design and specification of IT infrastructure and business process required for efficient valuation and processing of financial derivatives. He is also responsible for d-fine’s bespoke pricing library MoCo and tools based thereon. Before he joined d-fine he worked as a manager at Arthur Andersen. He holds a PhD  in theoretical particle physics.


Prof. Dr. Griselda Deelstra

University of Brussels

Mutivariate FX models with jumps: Triangles, Quantos and implied correlation

Prof. Dr. Wolfgang Härdle

Prof. Dr. Wolfgang Härdle

Chair Professor of Statistics at the School of Business and Economics

Humboldt University of Berlin

Pricing Cryptocurrency Options: the Case of CRIX and Bitcoin

The CRIX (CRyptocurrency IndeX) has been constructed based on a number of cryptos and provides a high coverage of market liquidity, The crypto currency market is a new asset market and attracts a lot of investors recently. Surprisingly a market
for contingent claims hat not been built up yet. A reason is certainly the lack of pricing tools that are based on solid financial econometric tools. Here a first step towards pricing of derivatives of this new asset class is presented. After a careful econometric pre-analysis we motivate an affine jump diffusion model, i.e., the SVCJ (Stochastic Volatility with Correlated Jumps) model. We calibrate SVCJ by MCMC and obtain interpretable jump processes and then via simulation price options. The jumps present in the cryptocurrency fluctutations are an essential component. Concrete examples are given to establish an OCRIX exchange
platform trading options on CRIX.

Wolfgang Härdle did 1982 his Dr. rer. nat. in Mathematics at Universität Heidelberg and 1988 his Habilitation at Universität Bonn. He is Ladislaus von Bortkieviecz chair professor of statistics at the School of Business and Economics, Humboldt-Universität zu Berlin. He is director of C.A.S.E. – Center for Applied Statistics & Economics. He leads the Collaborative Research Center “Economic Risk” and the International Research Training Group (together with WISE, Xiamen University) „High dimensional, non stationary time series“. His research focuses on dimension reduction techniques, computational statistics and quantitative finance. He has published 34 books and more than 250 papers in top statistical, econometrics and finance journals. He is one of the “Highly cited Scientist” according to the Institute or Scientific Information.

Junjie Hu

Junjie Hu

Ph. D. Candidate

Humboldt University of Berlin

Realized Volatility Forecasting of Cryptocurrencies

Junjie Hu is a Ph.D. candidate at Ladislausvon Bortkiewicz chair of statistics, Humboldt-University in Berlin. He holds a M.Sc. in Finance from Sun Yat-sen University, China. His current research interests are time series modeling and forecasting for financial markets, computational statistics and applied machine learning.

Prof. Dr. Karel in ’t Hout

Prof. Dr. Karel in ’t Hout

Associate Professor Mathematics and Computer Science

University of Antwerp

Numerical Valuation of Bermudan Basket Options via Partial Differential Equations

We study the efficient numerical valuation via partial differential equations of Bermudan basket options with a large number of underlying assets. To deal with the high-dimensionality of the problem, we combine the principal component analysis from Reisinger & Wittum (2007) with modern alternating direction implicit (ADI) schemes, see e.g. in ‚t Hout & Welfert (2009). The convergence of this combined analytical-numerical approach is investigated in detail, and ample numerical experiments are presented illustrating the high performance it attains. This is joint work with Jacob Snoeijer (U Antwerp).

Karel in ’t Hout is Associate Professor in the Department of Mathematics and Computer Science at University of Antwerp, specializing in the analysis and development of numerical methods for time-dependent partial differential equations with applications to finance.  He has previously held positions as Visiting Professor at Arizona State University, Visiting Professor at Boise State University and Researcher at Leiden University and University of Auckland.  Karel has also spent time in the industry, working as quantitative analyst at ABN Amro, Amsterdam.  He holds a PhD in Mathematics from Leiden University.

Dr. Antoine Jacquier

Dr. Antoine Jacquier

Senior Lecturer in Mathematics / Director MSc Mathematics and Finance

Imperial College London

VIX Options in Rough Volatility Models

We discuss the pricing and hedging of volatility options in some rough volatility models. First, we develop efficient Monte Carlo methods and asymptotic approximations for computing option prices and hedge ratios in models where log-volatility follows a Gaussian Volterra process. While providing a good fit for European options, these models are unable to reproduce the VIX option smile observed in the market, and are thus not suitable for VIX products. To accommodate these, we introduce the class of modulated Volterra processes, and show that they successfully capture the VIX smile. Joint work with Blanka Horvath and Peter Tankov.

Dr Jacquier is a Senior Lecturer in the Department of Mathematics at Imperial College London. His research focuses on volatility modelling, with a special emphasis on rough volatility and applications of asymptotic methods in finance. He holds a PhD in Mathematics from Imperial College London, has co-edited a book on Asymptotic Methods in Finance, and has published more than 30 papers in mathematical finance and applied probability.

Vadim Kanofyev

Vadim Kanofyev

Quantitative Researcher

Bloomberg L.P.

Machine Learning for Factor Investing

Style investing helps to construct portfolios that deliver positive long-term returns and have a low correlation with the market. We study the benefits of use of various machine learning techniques on the portfolio construction and „style“ selection stages. The goal of the first stage is to find an optimal combination of the asset characteristics, which has a superior predictive power of the future returns. On the second stage we use macro economic parameters to study the „style“ factor rotation to construct a composite portfolio that is comprised of individual factor-based strategies.

Vadim Kanofyev is a Quantitative Researcher at Bloomberg L.P. His research interests include quantitative asset allocation, algorithmic trading strategies and derivatives pricing. He has an extensive experience in numerical computing, applied machine learning and financial econometrics. Vadim holds a Master’s degree in Economics from the University of Pennsylvania.

Dr. Ingo Mainert

Managing Director CIO Multi Asset Europe

Allianz Global Investors

Maximilian Mair


Dr. Jacopo Mancin

Dr. Jacopo Mancin

Quantitative Analyst

Barclays Capital

Volatility Swaps: PDE Pricing Improvements for LSV frameworks

We study one dimensional PDE pricing techniques for volatility swaps that are able to closely replicate Local Stochastic Volatility Monte Carlo prices also in the context of fairly oscillating volatility term structure, while sensibly improving the computational performance. Our analysis is based on Foreign Exchange (FX) volatility swaps, but can be equivalently applied to other asset classes.

Jacopo is a quant at Barclays, which he joined in March 2017. His main focus is PDE pricing in LSV and Hybrid models for FX. 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. F. Biagini.

Dr. Alla Petukhina

Humboldt University of Berlin

Portfolio Optimization with Modified CoVar in Cryptocurrency Markets

Prof. Dr. Thorsten Schmidt

Prof. Dr. Thorsten Schmidt

Professor for Mathematical Stochastics

University of Freiburg

Statistical Arbitrage

Generalized arbitrage in the sense considered here corresponds to  trading strategies which yield positive gains on average in a class of scenarios rather than almost surely. The relevant scenarios or market states are specified via a  sigma-algebra and so this notion contains classical arbitrages as a special case. It also covers the notion of statistical arbitrage introduced in Bondarenko (2003). Relaxing these notions further we introduce generalized profitable strategies which include also static or semi-static strategies. We show that even under standard no-arbitrage (NA) there may exist generalized gain strategies yielding positive gains on average. In the first part of the paper we characterize these generalized no-arbitrage notions. In the second part of the paper we explicitly construct profitable generalized strategies and study their performance on simulated data and on market data. These strategies, albeit simple in nature, show a surprising performance being profitable on average  with little remaining risk. This is joint work with Christian Rein and Ludger Rüschendorf.

Thorsten Schmidt is Professor for Mathematical Stochastics at University Freiburg (successor of Ernst Eberlein). Prior to this he was professor for Mathematical Finance at Chemnitz University of Technology since 2008, held a replacement Professorship from Technical University Munich in 2008 and was Associate Professor at University of Leipzig from 2004 on. His Ph.D. he obtained from University in Giessen in 2003 on credit risk with infinite dimensional models. Besides his interests in Mathematical Finance, in particular interest rates, credit risk and energy markets, he has a strong background in statistics and probability theory. His research focusses on topics in mathematical finance and the theory and application of stochastic processes. This includes credit risky markets, interest rate markets, dynamic term structure models, insurance mathematics, energy markets and related fields.

Dr. Martin Simon

Dr. Martin Simon

Head of Equity and Equity Derivatives Valuation

Deka Investment GmbH

Stock Price Bubbles – An Option-based Indicator

In this talk we are going to discuss an option-based mathematical indicator for stock price bubbles. The first introductory part recaps the strict local martingale theory for modeling asset price bubbles and its implications for pricing contingent claims. In the second part we present a novel forward-looking indicator based on the information content of bid and ask market quotes for exchange-traded plain vanilla options. Our construction is motivated by a recent theoretical result by A. Jacquier and M. Keller-Ressel proving that bubbles can be identified from the asymptotic behavior of the implied volatility surface. However, in practice, the resulting inverse parameter identification problem is ill-posed and we adopt a statistical perspective in order to cope with this ill-posedness and to quantify the indicator’s inherent uncertainty. Finally, we provide real-market tests of the proposed indicator with focus on tech stocks addressing increasing concerns about a tech bubble 2.0. The talk is based on joint work with Lassi Roininen, Petteri Piiroinen and Tobias Schoden.

Martin Simon works for the German asset management company Deka Investment where he is head of equity and equity derivatives valuation. His research interests focus on numerical analysis, high performance computing, mathematical modeling and uncertainty quantification with applications in pricing and hedging of equity derivatives, asset allocation and risk management. He holds a doctoral degree in applied mathematics from the University of Mainz.

Dr. Niels Wesselhöft

Dr. Niels Wesselhöft

Humboldt University of Berlin

Separating the asset universe from cryptocurrencies

The aim of this paper is to find the proximal genus and the specific difference (genus proximum et differentia specifica) for the daily time series of cryptocurrencies returns, compared to classical asset returns.

In this sense, a daily time series of asset returns (either crypto or classical assets) can be characterized by a multidimensional vector with statistical components like volatility, skewness, kurtosis, tail probability, quantiles, expected shortfall or fractal dimension.

By using dimension reduction (Factor Analysis) and classification models (Support Vector Machines) for a representative sample of cryptocurrencies, stocks, exchange rates and commodities, we are able to classify cryptocurrencies as a new asset class with unique features in the tails of the distribution.


Prof. Dr. Uwe Wystup

Prof. Dr. Uwe Wystup

Managing Director


FX Options Greeks Unlimited

Black-Scholes delta, smile delta, model delta, premium-included delta, inverse delta, forward delta, traders’ gamma, Black-Scholes vega, traders’ vega, vanna, volga, volunga, vanunga, aega, rega, sega, revga, bufga, enoughga.

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.