26th MathFinance Conference

Abstract

The analysis of volatility smiles is a central component of options pricing theory. An attempt to unify the many models and frameworks into a theory applicable to any underlying is what this paper is. I draw conclusions from the rates, equities, and index markets on the general dynamics of skew, and provide statistical properties of cryptocurrency options skews. This shines light not only on the dynamics unique to crypto options, but also on the implications for exotic option pricing and for the pricing of prediction market bets, like polymarket. A principal component analysis is used in conjunction to the implied volatility regimes to better understand the supply and demand economics that lead to skew.

Biography

Mike Beckhusen is the founder of VolaLabs, a Berlin-based quantitative research firm building implied volatility surfaces. His current work focuses on systematically decomposing vol-surface P&L into skew components and trading them as market-neutral factors. Before VolaLabs, he worked in bank risk and derivatives functions at Deloitte, covering market, credit and interest-rate risk with a focus on VaR, ICAAP and structured-derivatives pricing. Prior to his career in Quantitative Finance he was a professional race car driver, giving him an unique perspective on risk management.

Abstract

We present a generative model for smooth arbitrage free non-parametric option surfaces (“SANOS”) and discuss applications to hedging derivatives.

Biography

Dr. Hans Buehler is a leading quantitative finance executive and researcher known for pioneering work in machine-learning-based derivatives hedging. He led the Equity Derivatives Quant Research at Deutsche Bank before joining J.P. Morgan in 2008, where he became Global Head of Equities Quantitative Research and later co-lead the Markets’ Analytics, Automation & Optimization “AI” effort.

At JPMorgan, he spearheaded the “deep hedging” framework, applying neural networks to trading and risk management—work that earned him Risk.net’s 2022 Quant of the Year award.

In 2022 Hans joined XTX Markets as Deputy CEO and later Co-CEO. He has served as a visiting professor at TU Munich and is now a visiting professor at the Mathematical Institute of the University of Oxford. Hans holds a PhD in Financial Mathematics from TU Berlin.

Abstract

We explore the calculation of the sensitivity with respect to the implied volatility surface for products valued with Monte Carlo simulation in the local volatility model. The theoretical foundation as well as practical implementation details will be discussed.

Biography

Jürgen works as a Quant at Leonteq AG. He started his career working on foreign exchange models. He extended his field of interest to equity, commodity, and interest rate derivatives and hybrids. He has a keen interest in market microstructure and in applying machine learning methods to quantitative finance problems.

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

Perfect for early-career quantitative investors and analysts, traders, and market data professionals, The Quantamental Revolution is an essential read for portfolio managers interested in improving their investment processes. The engaging anecdotal vignettes coupled with academic rigor provide the reader with an authentic front row seat to the evolution of Quantamental investing on Wall Street over the past three decades.

In The Quantamental Revolution: Factor Investing in the Age of Machine Learning, veteran quantitative investor and strategist, Milind Sharma, delivers a comprehensive discussion of factor investing, risk premia, smart betas, multi-factor models and the deployment of ML ensembles towards monetizing alpha in the hedge fund world.

Biography

Milind Sharma’s ~30 years of market experience span running proprietary trading desks at Deutsche Bank (now Saba) & RBC (GAT entity) as well as hedge funds (QuantZ) & mutual funds (MLIM). His funds have won many awards over the years including those from Morningstar, Lipper, WSJ etc. He was a founding member of Quant Strategies at Merrill Lynch AM (now BlackRock) and Manager of the Risk Analytics and Research Group at Ernst & Young. His QuantZ/ QMIT entities have been a pioneer in applying ML to factor investing. QuantZ is the world’s 1^st fully synthetic hedge fund accessible via QIS swaps.

His publications have appeared in the Journal of Investment Management, Risk, Wiley, Elsevier, World Scientific etc in addition to a new Wiley book – “The Quantamental Revolution”. His educational background includes Oxford, Carnegie Mellon, Vassar, UWC & Wharton. He has guest-lectured and (or) run Capstone courses at Columbia, Carnegie Mellon, NYU, UCLA, UCSD etc.

He is founder & President of New York’s premier quant society <QWAFAxNEW>.

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

This talk presents a smart contract-based prototype for trading and settlement of derivatives on Ethereum. Users open collateralized long and short positions on-chain, while a market maker provides liquidity and hedges net exposure off-chain. The prototype is deliberately product-agnostic and supports a range of implementation choices, from direct price feeds and index construction to model-based pricing for derivatives. It also allows for configurable fee structures, exposure limits and access restrictions.

Biography

Jan Szyszko vel Chorazy, CFA is a Senior Consultant at WEPEX GmbH, a consulting firm focused on financial markets, where he works on technical, risk, and blockchain/digital assets topics for capital markets clients — drawing on a background in derivative pricing and risk model validation across a variety of asset classes.

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.