25th MathFinance Conference

Abstract

Commodity Trading Advisors (CTAs) have historically relied on trend-following rules that operate on vastly different horizons—from long-term breakouts that capture major directional moves to short-term momentum signals that thrive in fast-moving markets. Despite a large body of work on trend following, the relative merits and interactions of short- versus long-term trend systems remain controversial. This paper adds to the debate by (i) dynamically decomposing CTA returns into short-term trend, long-term trend and market beta factors using a Bayesian graphical model, and (ii) showing how the blend of horizons shapes the strategy’s risk-adjusted performance.

https://arxiv.org/abs/2507.15876

Biography

William is a Business Analyst and Systems Engineer at BCCG. Before joining BCC Group, he graduated from New York University with a Computer Science degree and then proceeded to work on renewable energy technology for marine applications. William utilizes his technical background to work as a liaison between clients and internal development teams to accomplish the company’s goals.  When not traveling, he lives and works in New York City.

Abstract

We present a dynamic model for forward curves within the Heath-Jarrow-Morton framework, inspired by the local volatility model from equity markets. The forward curves take values in a function space H, and their dynamics are governed by a stochastic partial differential equation with state-dependent coefficients. These coefficients are defined through simple pointwise operations on H, resulting in a locally state-dependent structure. Although the model describes the evolution of the entire forward curve through a single equation, it remains remarkably simple. We propose a calibration routine to fit the model to observed option prices and test it using EEX-listed option data.

Joint work with Silvia Lavagnini (BI Norwegian Business School)

Abstract

The physics of standing waves and resonances imply that two tones sound well together if the ratio
of their frequencies is a quotient of small integers. The smaller the integers, the better (or “juster”)
the sound. Even though this is well understood, humanity has been struggling for millennia to find
well sounding tone systems. They always ran into the following conflict: If a tone system followed the
physics of euphony with “just” frequency ratios between its tones, then the same interval above
different tones would have different frequency-ratios, making playing, composing, and especially
transposing music nightmares of inconsistencies. Or, a tone system would be suitable for consistently
playing music – but then it wouldn’t have just frequency ratios (like our modern Equal Temperament
where all frequency ratios are irrational). The search for tone systems which facilitate consistent
music and adhere to the physics of euphony has been a battleground for centuries. But this battle is
now over. We show how to best fit tone-structures to overtone-spectra by mathematically optimizing
for euphony while always staying fully consistent. For this, many new concepts are needed such as
structural rings, tonal numbers, generator-logarithms, characteristic and natural determinants,
diatonic quanta, comma-factorizations, seed-coefficients, and mathematical measures for sound
quality. All this enables fundamentally new insights into musical intonations and, finally, the
identification of the absolute best among them, called “Cleantone Temperament”. In this talk we
present this new intonation, explain some of the mathematical ideas leading to it, and – most
importantly – practically demonstrate it by playing on a keyboard tuned in Cleantone Temperament.

Biography

Dr Hans-Peter Deutsch is one of the founders of d-fine, a leading consulting firm in Europe. Before
founding this firm he was Partner at Arthur Andersen and head of Andersen’s Financial and
Commodity Risk Consulting (FCRC) in Germany, which he founded in 1997. Hans-Peter holds a
“summa cum laude” Ph.D. in theoretical physics and is author of about 20 international scientific
publications in this field, mainly on Monte Carlo simulations of stochastic processes. He is also author
of many publications in the field of mathematical finance including books on quantitative portfolio
management, derivatives pricing and risk management. His classical textbook “Derivatives and
Internal Models” is now already in its 5th Edition in two languages, English and German. For many
years Dr Deutsch has been a Guest Lecturer and Member of the Advisory Board of the Mathematical
Finance Programme at the University of Oxford, UK, and also Chairman of the Advisory Board of the
MathFinance Institute at Johann Wolfgang Goethe-Universität in Frankfurt, Germany. As of July 2010,
with the consultancy firm d-fine well established and running smoothly without his help, Dr. Deutsch
decided to step out of the business world and devote his life to his two great passions: He became a
yoga teacher and professional travel photographer. In recent years, remembering his upbringing as a
scientist and mathematical physicist, he also engages in several mathematical endeavors, from the
Collatz conjecture to the Riemann Hypothesis, from calculating the efficiency of bitcoin mining to
f
inding the best mathematically consistent intonation for musical instruments.

Abstract

Path dependence is ubiquitous in finance, sometimes explicitly as the payoff of an exotic option may depend on the whole path of the asset price, not only at maturity, other times through the dynamics of the underlying (volatility, dividends…).
The framework to model path dependence is the Functional Itô Calculus and we review its basic concepts before offering a partial panorama of its applications: computation of the Greeks of path dependent options, perturbation analysis, volatility risk decomposition, Taylor expansion with signatures for fast computation of VaR and characterization of attainable claims, amongst other ones.

Biography

Bruno Dupire is heading the Quantitative Research at Bloomberg L.P. after having lead research teams at several banks. He is best known for having pioneered the widely used Local Volatility model (simplest extension of the Black-Scholes-Merton model to fit all option prices) in 1993 and the Functional Itô Calculus (framework for path dependency) in 2009. He is a Fellow and Adjunct Professor at NYU and he is in the Risk magazine “Hall of Fame”. He is the recipient of the 2006 “Cutting edge research” award of Wilmott Magazine and of the Risk Magazine “Lifetime Achievement” award for 2008.

Abstract

I will examine emerging quantitative challenges in Decentralized Finance (DeFi), with a focus on the mechanisms of Automated Market Makers (AMMs), lending and borrowing protocols, price discovery processes, and arbitrage dynamics between centralized and decentralized exchanges (CEXs and DEXs). I will also present associated modeling approaches and discuss the theoretical and practical questions they entail.

Biography

Emmanuel Gobet graduated from Ecole Polytechnique – Paris in 1995, he got a PhD degree in probability at University Paris Diderot. He took different academic positions at University Pierre et Marie Curie, Grenoble Institute of Technology and he is currently Professor at Ecole Polytechnique. He is an expert in Monte Carlo simulations, Machine learning and data science, extremes, risk management, stochastic modelling with applications in Climate change, Energy, Finance. He has written more than 100 papers in international journals and 3 books. He is the scientific leader of the Chair Stress Test, between BNPP Paris and Ecole Polytechnique. He is also a Scientific Advisor to Kaiko, a major digital finance data provider.

Abstract

In today’s fast-paced financial markets, timely and accurate calculations are crucial for informed decision-making. This presentation showcases a cutting-edge solution that enables seamless invocation of complex calculations from a market data platform in the cloud. By leveraging cloud-based infrastructure and advanced computing capabilities, the presentation demonstrates:

– Real-time data processing: Rapidly process vast amounts of market data to generate insights and calculations.

– Scalable computation: Dynamically scale computing resources to handle complex calculations and large datasets.

– Seamless integration: Easily integrate cloud calculation services with existing market data platforms and applications.

The solution empowers financial professionals to make data-driven decisions with confidence, leveraging the power of cloud computing to accelerate complex calculations and unlock new insights. Join us to explore the benefits and technical details of this innovative solution.

Key topics:

– Cloud-based market data processing

– Scalable computation for complex calculations

– Seamless integration with existing platforms

– Real-time data processing and insights

Target audience:

– Financial professionals

– Data scientists

– Cloud computing experts

– Market data platform developers

This presentation will provide a comprehensive overview of the solution, highlighting its benefits, technical architecture, and potential applications in the financial industry.

Biography

*Mauricio González Evans*

*CEO & CTO, BCCG*

Mauricio González Evans is a seasoned technology executive with over 40 years of experience in software development, consulting, and project management. A state-certified software developer, Mauricio has been a pioneer in the market data technology sector since 1993. He founded Workflow GmbH in 1993 and BCCG in 2003, driving innovation and excellence in market data solutions.

As CEO & CTO of BCCG, Mauricio leads the company’s strategic direction, leveraging his expertise in market data architectures and feed software development. With 20 years of experience as a senior consultant for major European clients, Mauricio has established himself as a trusted authority in the industry.

*Key expertise:*

– Market data technology

– Software development

– Consulting

– Project management

– Cloud-based solutions

*Background:*

– Studied Information Technology at the University of Darmstadt

– State-certified software developer (Nixdorf Computer)

– Extensive experience in market data architectures and feed software development

Under Mauricio’s leadership, BCCG continues to deliver cutting-edge market data solutions to clients worldwide.

Abstract

The title of this presentation reflects a key question in modern risk analytics: Can complex AI models outperform simpler approaches without sacrificing transparency and compliance?
Over the past decade, financial institutions have shown increasing interest in the application of machine learning techniques. At the same time, these developments have raised concerns among supervisory authorities.
Two publications are particularly relevant in this context: the EBA’s discussion and follow-up papers on IRB models, and the AI Act proposed by the European Council.
According to the EBA, machine learning models are subject to the same regulatory requirements as traditional approaches. Furthermore, the EBA emphasizes in its follow-up report that many aspects of the planned AI Act are already covered by existing banking regulation. This suggests that both supervisors and European legislation are open to modern technologies.
Nevertheless, AI models embody a fundamental trade-off between innovation and accountability. While they offer improved predictive performance, they also risk being perceived as black boxes—difficult to interpret, explain, or validate in a regulatory context.
This presentation examines the integration of modern machine learning techniques into an enhanced, yet regulation-compliant, validation framework using a credit scoring example. A logistic regression model is used as a benchmark, and an XGBoost model serves as the AI challenger, applied to a retail mortgage dataset. The analysis includes model architecture, discriminatory power and calibration metrics. A systematic SHAP-based decomposition is used to attribute predictions to individual features and to highlight interaction effects not visible to linear models.
The aim of this talk is not only to provide technical insight, but also to contribute to the broader discussion of how risk managers can responsibly integrate advanced analytics into established validation and governance frameworks.

Biography

Dr Andree Heseler is Managing Director of mex consulting GmbH & Co. KG and an expert in data-driven risk modelling.

He advises banks and asset managers on mathematically sound methods of risk management, with a particular focus on modelling, validation, and the application of artificial intelligence. His experience combines financial mathematics, data science, and the development of model-driven prototypes in demanding client projects.

Biography

Karel in ’t Hout is full professor at the Department of Mathematics at the University of Antwerp. He obtained his PhD degree in 1992 in Mathematics at the University of Leiden and subsequently held research positions at the University of Leiden, the University of Auckland, CWI Amsterdam, Boise State University and Arizona State University. In addition he was quant at ABN Amro Bank in Amsterdam. In 2006, he was appointed at the University of Antwerp.

His research area concerns numerical methods for time-dependent partial differential equations (PDEs) arising in finance. He has made ample contributions to the numerical and financial mathematics literature on the development, analysis and application of operator splitting methods for effectively solving multidimensional PDEs, and generalizations thereof, in financial option valuation. He is the author of the book Numerical Partial Differential Equations in Finance Explained that appeared in 2017. At the University of Antwerp, he teaches several courses in the MSc program on Financial and Applied Mathematics.

Abstract

We study the pricing of double barrier knock-out options under stochastic volatility models using the conditional Monte Carlo method based on the local time-space formula of Peskir. Valuation formulas in the form of an early knock-out discount are provided, where the discount depends on the local time of the underlying stochastic processes and the deltas of the option at the barriers. The latter solves systems of coupled Volterra integral equations of the first kind. This characterization leads to an efficient numerical method for general models. Algorithms for numerical implementation are presented.

Biography

Kitapbayev’s research focuses on stochastic calculus and quantitative finance. Topics include optimal stopping problems, real options, energy finance, derivatives pricing, pair trading, mortgage contracts, optimal capital structure, credit risk, and machine learning. His work was published in top journals such as Review of Financial Studies, Mathematical Finance, SIAM Journal of Financial Mathematics, Mathematics of Operations Research, Quantitative Finance, Energy Economics, Applied Energy, Stochastic Processes and Applications, and Stochastics, among others.

Kitapbayev holds a BSc and an MSc in Mathematics from Lomonosov Moscow State University and a Ph.D. in Probability from the University of Manchester. Previously, he was an Assistant Professor in Financial Mathematics at North Carolina State University, a Senior Lecturer in Finance at the MIT Sloan School of Management, and a Postdoctoral Associate at Boston University Questrom School of Business.

Abstract

It is by now well-known that, while the couple (stock price, instantaneous variance) is not Markovian in rough volatility models, the couple (stock price, forward variance curve) is infinitely dimensional. This allowed Viens and Zhang (“A martingale approach for fractional Brownian motions and related path dependent PDEs”) to extend the classical Feynman-Kac approach to this new setting, turning the classical pricing PDE into a path-dependent one. We investigate here some implications of this result for numerical purposes: (i) we propose a deep-learning based numerical scheme to solve it and (ii) we show how to use this PDE to prove weak convergence rates for Monte Carlo methods.

This is based on joint works with Mugad Oumgari (Lloyds), Ofelia Bonesini (London School of Economics) and Alexandre Pannier (University Paris-Cite).

Biography

Jack Jacquier is a Professor of Mathematics at Imperial College London. His research focuses on stochastic analysis and volatility modelling in Mathematical Finance, as well as on quantum computing algorithms. Prof Jacquier also serves as a scientific consultant and advisor for various finance and technology companies.

Abstract

Join us for an in-depth exploration of currently evolving DLT market infrastructures that embed programmable logic to deliver real-time and transparent delivery-versus-payment settlement. We will you take along our learning journey and provide a deep dive and insights into our second DLT-based bond issuance of one of the worldwide largest issuers. We will shed some light on the ECB Exploratory Work and derive the current maturity of the DLT-based capital market.

Biography

Tim has been part of the KfW Treasury Team since October 2022, where he is responsible for designing and implementing digitalization projects related to capital markets. As Senior Manager Capital Market Innovation, he co-led the implementation of KfW’s first two blockchain-based digital bonds in summer 2024. Before joining KfW, Tim worked as a Front Office Manager in the Treasury of Deutsche Leasing Group in Bad Homburg, Germany. He holds a Master’s degree in General Business Administration from Goethe University in Frankfurt and has been a Certified Corporate Treasurer VDT since 2022.

Abstract

A numerical method for approximating the values of European-style financial derivatives is proposed, based on a two-asset exponential Lévy model for the underlying dynamic. The scheme accounts for both finite and infinite activity in the jump component.

According to the Feynman-Kac theorem, the fair derivative value satisfies a two-dimensional partial integro-differential equation (PIDE). Our numerical scheme is structured in two main parts. First, it replaces the integro-differential operator with a finite-difference approximation, reducing the PIDE to a system of ordinary differential equations (ODEs). Second, it solves this system numerically by a suitable time-stepping method.

Among others, two notable challenges are addressed in developing this method. First, the integral term in the PIDE leads to a large dense matrix in the system of ODEs. To efficiently handle this, a second-order implicit-explicit time-stepping method is proposed, enabling application the fast Fourier transform. Second, for jump processes with infinite activity, the jumps below a small threshold are approximated by a diffusive term, which conveniently removes the singularity in the integral term. Numerical experiments are presented illustrating the effectiveness of our numerical approach.

This work is inspired by [I.R. Wang, J.W.L. Wan, and P.A. Forsyth, Robust numerical valuation of European and American options under the CGMY process, J. Comp. Finan. 10 (2007), 31-69].

Biography

Massimiliano Moda is PhD student at the University of Antwerp, where he studies numerical techniques for financial models driven by Lévy processes. His research focuses on the use of finite difference methods for pricing options in the presence of infinite activity jumps and multi-asset dynamics. He is particularly interested in the numerical challenges posed by non-local operators in high-dimensional problems.

Biography

Wojciech Mucha, President and founder of Enterprai has over 15 years of executive experience in fintech. At the age of 23 he joined Bloomberg Professional in London as a Senior Sales and Relationship Manager for emerging markets. Wojciech as a Chief Strategy Officer played an important role in helping to build and scale Conotoxia and Cinkciarz.pl, an award-winning financial services holding company with a portfolio of regulated entities in Europe and the US. Mucha was elected by the nominating and governance committee of the European Payments Council – a SEPA scheme manager – to sit on its General Assembly and lead on innovation in financial services. Wojciech is a current Executive MBA candidate at the University of Chicago Booth School of Business and completed a two-month on campus course in Capital Markets and Investments at Harvard University. He earned his Master of Science in International Financial Management at University of the West of Scotland.

Abstract

We build multi-factor term structure models using neural networks with encoder-decoder structure (i.e. the networks go shallow in the middle). The models can be set up to take market data (swap rates) directly as input, and by imposing no-arbitrage conditions in an affine-inspired way at the decoder level, we can train models that are guaranteed to exactly arbitrage-free.  Joint work with Rastin Matin and Wenjie Wan  https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5040173

Biography

Over the last 25 years Rolf Poulsen has worked (researching and teaching) on a variety of topics in quantitative finance; for instance hedging of exotic options, exchange rate models and markets, and optimal mortgage choice. He will talk about these subjects to all who are interested – and some who aren’t.

Biography

Parviz Rakhmonov is a Quantitative Analyst at Marex, London. Prior to joining Marex, Parviz was overseeing development of quantitative analytics libraries at various investment banks. Parviz has a PhD in Mathematics from Lomonosov Moscow State University. His expertise covers volatility modeling for interest rates and equities with particular focus on stochastic volatility modeling.

Biography

Since 1976, the Abu Dhabi Investment Authority (ADIA) has been prudently investing funds on behalf of the Government of Abu Dhabi, with a focus on long-term value creation. Our portfolio comprises more than two dozen asset classes and subcategories, from equities and fixed income to hedge funds, real estate, private equity and infrastructure. ADIA invests in all of the world’s major markets, in addition to having significant exposure to the developing markets that will drive global growth of the future. Our people are as international as our investments, bringing together an array of skills and experiences to create a collaborative and stimulating workplace. ADIA combines a culturally diverse workforce, made up of more than 65 nationalities, with a global network of relationships with the world’s top investment institutions and thought leaders. ADIA provides a dynamic work environment, where robust debate is balanced by shared values and a willingness to learn and adapt to a changing environment, and where committed people can build rewarding careers while contributing to ADIA’s continued long-term success. We place a particular emphasis on the development and training of UAE Nationals, many of whom currently, or will go on to, hold prominent roles within ADIA and in the UAE. ADIA seeks the best global talent, across disciplines and geographies, and provides them with the resources they need to thrive and build successful careers. Community Guidelines: ADIA reserves its right to remove content that is inappropriate, off-topic, or unprofessional. Important Notice: ADIA does not ask individuals to provide sensitive documents or to register for training courses via LinkedIn or other social media platforms as part of the recruitment process. If you receive any suspicious requests claiming to be from ADIA employees, we strongly advise you to exercise caution and refrain from sharing personal or sensitive information.

He is also the author of:

• Financial Markets for the million
• Financial models in production

Abstract

Swing options are widely traded derivative contracts in energy markets, especially the electricity market. This type of contracts gives the holder the right to, dynamically, buy electricity at a predetermined, fixed price and, hence, reducing exposure to strong price fluctuations. There are constraints on the amount that can be bought by the holder at each exercise date and over the whole lifetime of the contract. We focus on swing options with discrete and fixed-time exercise rights.

The electricity price is modelled by a two-factor model: an Ornstein-Uhlenbeck process and a zero mean-reverting pure jump process. This model takes into account the mean-reverting property of the price, the occurrence of price spikes as well as the possibility of negative prices. We shall consider in this talk finite-activity jumps, notably Merton and Kou jumps.

The prices of the swing options under consideration are given by the solution of systems of parabolic partial integro-differential equations (PIDEs) that are convection-dominated and have non-smooth initial conditions. In this talk, we shall develop an effective finite difference approach to solve these challenging equations numerically and next investigate its convergence behaviour.

Biography

Mustapha Regragui is currently pursuing a joint PhD at Ghent University and the University of Antwerp. His research focuses on numerical methods for partial integro-differential equations (PIDEs), especially for the valuation of swing options.

Prior to his doctoral studies, he worked as a software engineer at Dassault Systèmes in Paris. He studied at ENSEIRB-MATMECA and the University of Bordeaux.

Abstract

In this talk (joint work with Claudio Fontana and Simone Pavarana) we show how affine models can be used to capture the stylized facts of overnight markets (and others): jumps at predictable time. While in the Gaussian setting, this does not pose a big problem, achieving such jumps in a flexible way in the CIR setting is much more challenging. We classify all possible jump types and give a number of example, including such that allow upward and downward jumps.

Biography

Thorsten Schmidt is Professor for Mathematical Stochastics at University Freiburg and Senior Financial Engineer at MathFinance. From 2017-2019 he was fellow of the Freiburg institute of Advanced Studies (FRIAS). 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. Besides many stays at leading universities, he was guest professor at ETH Zurich and at Université d’Evry.

His Ph.D. he obtained from University in Giessen in 2003 on credit risk. He is Editor of Statistics & Risk Modeling,

Associate Editor for Mathematical Finance, International Journal of Theoretical and Applied Finance and was Associate Editor for Journal of Banking and Finance and Statistical and Probability Letters. He is an elected member of the International Statistical Institute and is a member of the Board of the Bachelier Finance Society.

With numerous articles in the areas of Mathematical Finance and Probability in internationally leading journals and frequent presentations on conferences around the world, he is a well-known scientist in the area of affine models, interest rates, credit risk, incomplete information, risk management, filtering, and insurance mathematics. He has a strong background in statistics and information technology and teaches probability, mathematical finance and machine learning at the university of Freiburg.

Abstract

This talk presents an approach that integrates neural network-based surrogate models with conformal prediction techniques to deliver calibrated uncertainty quantification alongside point predictions. The use of deep learning in financial risk applications is subject to strict regulatory requirements. By incorporating the distribution-free guarantees of conformal methods, these emulators enhance both the reliability and interpretability of machine learning models, particularly under distribution shifts or in low-data regimes. We explore applications in financial risk management, including climate risk, portfolio optimization under uncertainty, and option pricing, demonstrating how conformalized neural surrogates improve the transparency and robustness of predictive systems. The resulting framework provides a flexible and scalable toolset for financial institutions seeking to implement AI-driven models with verifiable risk controls.

Biography

Martin Simon is a Professor of Data Science at the Frankfurt University of Applied Sciences and serves as a Docent in Computational Finance at the Lappeenranta-Lahti University of Technology (LUT) in Finland. He holds a PhD in Numerical Mathematics from the University of Mainz and brings many years of industry experience to his academic roles. He is one of the founders of the Sustainable Finance Research Lab at the Frankfurt University of Applied Sciences, where he plays a pivotal role in advancing research on sustainable finance and climate risk. With a strong interdisciplinary background, his research interests span across data science, computational finance, and the application of advanced numerical methods in financial modeling.

Simon is involved in several international collaborations and research projects, particularly focusing on the integration of machine learning techniques into financial risk assessment and quantitative finance. In addition to his academic roles, he actively engages with the financial industry, providing expert insights on the challenges of implementing cutting-edge technologies like AI and machine learning in financial risk management.

His work is recognized both in academia and industry, where he actively bridges the gap between theoretical research and practical application. He is a regular speaker at international conferences, where he discusses topics like climate risk modeling and advances in computational finance.

Abstract

Join us for an in-depth exploration of currently evolving DLT market infrastructures that embed programmable logic to deliver real-time and transparent delivery-versus-payment settlement. We will you take along our learning journey and provide a deep dive and insights into our second DLT-based bond issuance of one of the worldwide largest issuers. We will shed some light on the ECB Exploratory Work and derive the current maturity of the DLT-based capital market.

Biography

Bert is currently working as a Senior Manager for KfW in Frankfurt am Main. His tasks at KfW encompass looking into new technology & new work methods and how they can be implemented into the treasury department. Since 2016 Bert is looking into the topic of Distributed Ledger Technology (DLT)/Blockchain. He has gained insides through various marquee DLT projects. From 2007 to 2010 he worked as a portfolio manager in the Structured Credit unit at Deka Investment GmbH. He started his career at the credit rating agency Fitch Ratings in the field of securitisation. Bert holds a business administration degree from Universität Potsdam as well as a Master of Science in “Risk Management & Regulation” at Frankfurt School of Finance & Management.

Abstract

Traditional portfolio choice often assumes known return parameters. In reality, such information may be unavailable or unreliable. This paper proposes an “uninformative portfolio choice” framework that requires no prior assumptions about return distributions. Each asset’s state price density is treated as a likelihood model validated by observed data. Frequentists select the highest-likelihood asset, while Bayesians form a mixture weighted equally, yielding a simple 1/N-portfolio. Both approaches are model-free, robust, and adaptable, bridging fundamental statistical principles and finance. This unifying perspective allows portfolio choices that remain valid across changing market conditions, enhancing both theoretical soundness and practical implementability.

Biography

Prof. Jan Vecer has worked at the Charles University in Prague since September 2015 where he teaches courses in mathematical finance and stochastic analysis. From 2010 to 2015 he was a Professor of Finance at the Frankfurt School of Finance and Management. Professor Jan Vecer received his PhD in Mathematical Finance from Carnegie Mellon University. He held academic jobs at the University of Michigan and Kyoto University and at Columbia University where he was promoted to the rank of Associate Professor in 2006.

He works in various areas within the fields of Financial Statistics, Financial Engineering and Applied Probability. These areas include Option Pricing, Optimal Trading Strategies, Stochastic Optimal Control, and Stochastic Processes. The method he developed for pricing Asian Options is widely used both in academia and in the finance industry as a benchmark. He is an author of a monograph “Stochastic Finance: A Numeraire Approach” published by CRC Press. He has given about 100 invited talks in the conferences and in the world class universities, such as Harvard, Princeton, Stanford, University of Chicago, Cornell, Oxford, Cambridge, Humboldt, or Tsukuba.

Biography

Uwe Wystup is managing director of MathFinance AG, 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, certified expert for interest rate and currency risk management at Frankfurt’s Chamber of Commerce, honorary judge at Frankfurt Commercial Court and the State Financial Court. 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 He acts internationally as expert witness and mediator in financial disputes. In his consulting work he has advised many leading banks and companies in the world including DBS, OCBC, Deka, GPS, Moneycorp, ICY Software, Murex, SuperDerivatives, Refinitiv, Pictet, La Caixa, DWS, Union Investment, NordLB, LBBW, AXA, Natixis, Reserve Bank of India and many others.

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, followed by a second edition in 2017. Many of his papers appeared in scientific journals. See https://www.mathfinance.com/publications/ for publications.