The bridge between
investment banking and
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.
Enjoyable atmosphere, lots of networking, expert speakers, way to learn developments in the industryArtur Sepp
The MathFinance conference provides an excellent environment to learn about recent developments and networking with leading experts from both industry and academiaMartin Simon
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 jobEugen Tiganu
We would like to thank our sponsors:
MathFinance Conference 2019 is supported by:
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
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.
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
Associate Professor Mathematics and Computer Science
University of Antwerp
Numerical Valuation of Bermudan Basket Options via Partial Differential Equations
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
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.
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
Dr. Jacopo Mancin
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
Prof. Dr. Thorsten Schmidt
Professor for Mathematical Stochastics
University of Freiburg
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
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
Humboldt University of Berlin
Prof. Dr. Uwe Wystup
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.