24th MathFinance
Conference

Abstract

Perfect calibration of stochastic local volatility models can be achieved by the particle method due to Guyon and Henry-Labordère. Starting from a back-bone stochastic volatility model, a local volatility factor is computed on the fly to perfectly fit market prices. Mathematically, the local volatility factor is given as a conditional expectation, which is approximated by a local regression procedure. While this procedure is quite popular among practitioners, there are substantial gaps in the theoretical understanding. Indeed, even well-posedness of the resulting singular McKean-Vlasov system is not known.

We develop a novel regularization approach based on the reproducing kernel Hilbert space technique (kernel ridge regression) and show that the regularized model is, in fact, well-posed. Furthermore, we prove propagation of chaos and provide error estimates for the numerical scheme. We demonstrate numerically that a thus regularized model is able to perfectly replicate option prices due to typical local volatility models, and demonstrate excellent performance. Our results are also applicable to more general McKean–Vlasov equations. (Joint work with Denis Belomestny, Oleg Butkovsky, and John Schoenmakers.)

Biography

Christian Bayer obtained his PhD at the TU Vienna on numerical methods for stochastic differential equations. He is working as a Senior Researcher at the Weierstrass Institute of Applied Analysis and Stochastics in Berlin. His research interests are in rough volatility, computational finance, stochastic numerics, and stochastic optimal control, and he is active as principal investigator in several research grants related to these topics.

Abstract

Capital markets shall finance political priorities like green infrastructure, defense, start-ups in digital, nano-, or bio-technology, diversify risks,  improve financial stability, and solve the looming pension crisis. But such optimistic expectations are not justified. Real existing capital markets collapse frequently, conflicts of interest are common, and the track record of pension funds is worrying. Even worse, the concentration of power in financial markets negatively affects free competition in non-financial markets already today. How can we reform capital markets in a realistic and feasible way to support both individual freedom and the common good? And how should we even think about stabilizing and regulating international capital markets? I will discuss both questions in this talk and argue that a strategic localization of financial structures yields vastly superior results for society, specifically in the form of regional investment or pension funds.

Biography

Christoph Becker studied mathematics at the University of Trier and did his PhD in financial mathematics at Frankfurt School of Finance and Management. He worked at Frankfurt School as a lecturer and then as a risk manager at Commerzbank AG in Frankfurt. Since 2015 Christoph Becker is Professor for Financial Mathematics and Stochastics at Darmstadt University of Applied Sciences. His research interests are the transformation of the financial system from a bank-based to a market-based system, the stability of the shadow banking system, and financial econometrics.

Abstract

We investigate the dynamic properties of various stochastic and notably rough volatility models, focusing on the dynamics of implied volatilities. While recent literature has extensively analyzed static properties, such as the model’s calibration power or the term structure of ATM skews, dynamic features have received less attention.

We focus on the Skew-Stickiness Ratio (SSR), an industry-standard indicator of joint spot price and implied volatility dynamics, pursuing the analysis of [Bergomi, Smile dynamics IV, Risk 2009] and extending it to rough volatility models. Using different numerical estimators, we compare the behavior of the model SSR for several models to the empirical market SSR for the SPX Index; this comparison sheds light on the suitability of certain modeling choices. Notably, we observe that Bergomi’s original intuition that a forward variance model with a power-law kernel should generate an SSR with a constant term structure turns out to be accurate, but only for small volatilities of volatilities. On the contrary, the typical parameter sets required for the calibration of fractional models to the SPX options surface (with high levels of volatilities of volatilities) generate a term structure of the SSR that displays important deviations with respect to the market, leading to preliminary conclusions not in favor of models such as rough Bergomi or rough Heston.

Joint work with Florian Bourgey (Bloomberg LP) and Jules Delemotte (Ecole Polytechnique).

Biography

Stefano De Marco is Professor in Probability and Mathematical Finance at Ecole polytechnique, Paris. He owns a PhD in applied mathematics from Scuola Normale Superiore di Pisa and Université Paris-Est. He is the academic director of the 1st year of the MScT Data & Finance jointly organised by Ecole Polytechnique and HEC Paris. He is in charge of the Chaire Deep finance and statistics, a joint research program with Qube R&T, and he is a member of the steering committee of the Chaire Risques Financiers, a joint research program with Société Générale. His research activity focuses on risk management problems for options, volatility modeling, optimal transport for quantitative finance, numerical probability and Monte Carlo methods.

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

The new cutting-edge approach to FX Risk management
Assessing FX exposures in the dynamic currency markets requires a timely and responsive approach—something that conventional, historically-based methods often lack. Our approach leverages implied volatilities, providing a risk measurement that is as current and accurate as the market itself. Implied volatilities, utilized by market makers to price options, effectively represent the market’s real-time assessment of risk—akin to pricing an insurance contract.
Through this method, corporate clients can precisely evaluate their risk exposures, refine their hedging policies, and identify the most effective hedging strategies that align with their specific objectives. This ensures that their risk management is as agile and informed as the ever-changing FX markets.
Enrico Ferrante is a dedicated and dynamic FX Specialist committed to empowering clients in achieving their risk mitigation goals. With deep expertise in FX hedging, investments, and liability management, he crafts and delivers innovative, customized solutions for corporate and institutional clients across Europe. Driven by a passion for tackling complex challenges, Enrico excels at creating value for stakeholders and thrives in collaborative environments, working alongside diverse and talented teams.
Among his notable achievements, Enrico launched:
• Kristall (Commerzbank): An innovative FX platform designed to simplify and expand the structured product business for corporate clients and financial institutions.
• UCHedge (UniCredit): A cutting-edge platform that enhances FX risk assessment, supporting corporate clients in their hedging solutions from planning to implementation and reporting.
These accomplishments underscore his ability to develop and implement pioneering solutions that drive client success and advance the industry.

Biography

CO – Head FXIR Structuring & Analytics | UniCredit Spa, Milan
Global Head FX Structuring | UniCredit AG, London
Global Head FX Structuring | Commerzbank AG, London
FX Senior Structurer | Bank of America, London
Head FX Structuring Europe | Calyon Paris
Head FX Options Trading Europe & Global Head FX Structuring | Credit Lyonnais
Global Head of FX Options | Istituto Bancario San Paolo di Torino

Abstract

We propose a neural networks method to estimate extreme Expected Shortfall and extreme conditional tail moments as functions of confidence levels, in heavy-tailed settings. The convergence rate of the uniform error between the log-conditional tail moment and its neural network approximation is established leveraging extreme-value theory (in particular the high-order condition on the distribution tails) and using critically two activation functions (eLU and ReLU) for neural networks. The finite sample performance of the neural network estimator is compared to bias-reduced extreme-value competitors; tests are conducted on various crypto-assets. The experiments reveal that our method largely outperforms others. In addition, the selection of the anchor point appears to be much easier and stabler than for other methods.

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

On classical crypto exchanges, derivatives on crypto assets, initially Bitcoin, were introduced as early as 2011 in the form of futures.      Due to a lack of regulated offerings, the “crypto community” built solutions that differ from instruments commonly offered on regulated CCPs.We give several examples of the most popular instruments and compare offerings to regulated CCPs that recently started offering derivatives on crypto currencies.

Biography

Steffen Härting is Senior Manager at Deloitte. He focusses on risk management and market structure within the crypto currency space. He entered non-directional, fully automated crypto currency derivatives trading in 2016. Before joining Deloitte, he built the crypto currency trading unit for a larger family office. He holds a PhD in Applied Mathematics from University of Heidelberg.

Biography

Stefan Hamberger is a seasoned managing director and consultant with over 30 years of experience in the FX and electronic trading sectors. Currently leading FX ALPHA eBusiness Consulting, he specializes in optimizing trading systems and sales organizations for banks and buyside clients, with a focus on FX and crypto. Stefan has held senior positions at Crédit Agricole CIB and Commerzbank AG, where he drove significant growth and innovation in electronic trading and sales. He is recognized for his expertise in electronic trading, risk management, and business strategy. Stefan has been a member of the ACI FX Committee since its foundation in 2010.

Managing DirectorManaging Director
FX ALPHA eBusiness Consulting
Global Head of eFX, Managing Director
Credit Agricole CIB
Global Head of eFIC Sales, Managing Director
Commerzbank AG
Co-Head FX Institutional Sales / Head of FX and Derivatives Bank Sales
Commerzbank AG

Abstract

Most estimates of term premia, including our own, have not stood the test of time. Many models still suggest that US term premia are still quite depressed, and even the most sophisticated estimates can be replicated by a simple linear combination of a few liquid interest rates. A fresh approach is overdue. The existence of better survey data leads us to a more straightforward way of discovering reasonable values for the term premium, and we present a method for deriving daily data for the US and euro curves.

Biography

Jessica James is a Senior Quantitative Researcher at Commerzbank in London, previously Head of the Quantitative Solutions Group.  She joined Commerzbank from Citigroup where she held a number of FX roles, latterly as Global Head of the Quantitative Investor Solutions Group. Prior to this she was the Head of Risk Advisory and Currency Overlay Team for Bank One. Before her career in finance, James lectured in physics at Trinity College, Oxford.  She holds a BSc in Physics from Manchester University and a D. Phil. in atomic and nuclear physics from Oxford University.

Her significant publications include the ‘Handbook of Foreign Exchange’ (Wiley), ‘Interest Rate Modelling’ (Wiley), and ‘Currency Management’ (Risk books). Her latest book ‘Inflation linked Bonds and Derivatives’ came out in 2023.  She has been closely associated with the development of currency as an asset class, being one of the first to create overlay and currency alpha products.

She is a Trustee Director of Citigroup Pension Board, and a Trustee of Commerzbank AG pension scheme, and a member of the Advisory Committee for the Money Purchase Pension Forum.

Jessica is a Member of the Faculty of Bank Treasury Risk Management, an Industry Advisory Panel member for Edinburgh University Finance programme, and a Visiting Professor both at UCL and at Cass Business School. She is a Managing Editor for the Journal of Quantitative Finance.  Apart from her financial appointments, she is a Fellow of the Institute of Physics and has been a member of their governing body and chaired their Audit and Risk Committee.

Abstract

Market generators are a rapidly evolving class of models that simulate financial market behavior using neural networks. These deep learning models learn the underlying distribution of financial data and generate synthetic market scenarios, offering a powerful alternative to classical stochastic models. The expression “Market Generator” only entered the vocabulary of financial modelling around 2019, but by today it has already grown into an area of its own right, with a rapidly growing number of research contributions appearing on the matter.  This talk aims to unravel the evolution of the rapidly evolving landscape of generative models in Machine Learning in the context of  more classical modelling techniques in finance for the evolution of stock prices but also in the context of the most recent trends of Generative AI that include LLMs.

More concretely, we will present a very powerful generative model for time-series modelling: State-of-the-art performance for irregular time series generation has been previously obtained by training Neural SDEs adversarially as GANs. However, typical GAN architectures suffer from various instabilities in training. Instead, we introduce a novel class of generative models that can be trained non-adversarially, using scoring rules on pathspace based on signature kernels. Most notably this procedure permits to evaluate a corpus of paths against a single observation. But additionally, our procedure permits conditioning on a rich variety of market conditions and significantly outperforms alternative ways of training Neural SDEs on a variety of tasks including the simulation of rough volatility models, the conditional probabilistic forecasts of real-world forex pairs where the conditioning variable is an observed past trajectory. Moreover, our framework can be easily extended to generate spatiotemporal data, including the mesh-free generation of limit order book dynamics.

Biography

Blanka Horvath is an Associate Professor of the University of Oxford, researcher at the Oxford Man Institute and a Member of the DataSig group affiliated with the Alan Turing Institute.

Prior to her current role, she held tenure-track positions at the Technical University of Munich and at King’s College London and postdoctoral positions at Imperial College London and ETH Zurich.

Blanka’s research interests in mathematical finance include stochastic volatility modelling with a particular focus on rough volatility models, and more recently, generative models with a special interest for data representation via rough path signatures. Her research has a pronounced emphasis on practical applicability, which was recognised by the 2020 Rising Star Award of Risk Magazine. Several of her research contributions have been implemented by various entities in the financial industry including solutions to pricing and calibration of rough volatility models and to the development of appropriate market generators in the deep hedging paradigm.

Biography

Dr Przemysław Klupś is a Quantitative Researcher at Enterprai, specialising in generating FX trading signals using SDR data. Dr Klupś holds a PhD in Materials Science from the University of Oxford, where his research focused on solute segregation and clustering in nuclear-grade steels via atom probe tomography. Before his current role, he worked at the UK Atomic Energy Authority, developing characterisation techniques for metals used in fusion reactors. Dr Klupś brings a unique interdisciplinary perspective to financial mathematics, leveraging a strong background in advanced materials science and quantitative analysis.

Abstract

Topic – “Best Execution with Optimal number of counterparties”: FinIQ FXD Multi-dealer module demo followed by a PPT with stats proving that a larger counterparty panel produces “best execution price” better than a smaller counterparty panel?

Biography

Milind founded FinIQ in 2001 in Singapore, after having worked with Reuters and Citibank. When the worldwide interest rates dropped to zero, he led FinIQ’s structured products distribution platform, which was first of its kind. Soon after its launch in 2003, the platform was adopted by leading banks in APAC, especially those in the private and retail wealth space. Building on the yield enhancement success, FinIQ kept adding both complex derivatives linked products as well as simpler investment securities and FX cash products. Same time, FinIQ started linking with various liquidity providers, establishing an unmatched cross-asset liquidity network. Milind operates from Pune, where FinIQ’s development center is located.

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

In the evolving landscape of climate finance, the need to incorporate climate impacts into risk management strategies presents significant challenges and opportunities. This presentation leverages empirical data and real-world experiences to explore the intricacies of managing climate-related financial risks through software applications. We will explore workflows around geo-locating physical assets, hazard identification, financial impact estimation, and the integration of climate risk into operational frameworks. By examining case studies, we will demonstrate the pivotal role of software solutions in transparent, scalable, and reproduceable climate risk management.

Biography

Elre Oldewage is an application engineer at MathWorks specializing in machine learning, climate risk and financial applications. She joined MathWorks in 2023 after completing her PhD which focused on robustness in machine learning and how second order optimization can be used to improve model training. Elre holds an MSc in Computer Science from the University of Pretoria and a PhD in Engineering from the University of Cambridge.

Abstract

The main goal is to value real estate portfolios using publicly available data. Based on publicly registered real estate sales in the Île-de-France region in the period 2014-2022, publicly available geospatial data (OpenStreetMap) and macroeconomic time sieres, we train various machine learning models to forecast real estate sale prices. A particular focus is on the explainability and interpretability of the models. Partial dependence plots and SHAP values enable the identification of key features influencing sales prices.

Biography

Natalie Packham is Professor of Mathematics and Statistics at Berlin School of Economics and Law and Principal Researcher within the International Research Training Group “High Dimensional Nonstationary Time Series” (IRTG 1792) at Humboldt University Berlin. Natalie has several years of industry experience as a front office software engineer at an investment bank, and is frequently involved in industry-related research and consulting projects. Her research expertise includes Mathematical Finance, Financial Risk Management and Computational Finance, and her academic work has been published in Mathematical Finance, Finance & Stochastics, Quantitative Finance, Journal of Applied Probability and many other academic journals. She is associate editor of “Methodology and Computing in Applied Probability” and “Digital Finance” and co-chair of the GARP Research Fellowship Advisory Board. Natalie holds an M.Sc. in Computer Science from the University of Bonn, a Master’s degree in Banking & Finance from Frankfurt School, and a Ph.D. in Quantitative Finance from Frankfurt School.

Abstract

We present a new simulation-based American option pricing method, Delta least squares Monte Carlo (Delta LSM). Whereas the classical LSM method from Longstaff & Schwartz (2001) uses only the discounted payoff to learn the continuation value, Delta LSM uses both the discounted payoff and its derivative (Delta) to estimate regression coefficients. The Delta LSM is straightforward to implement and comes at little extra numerical cost; it is literally an add-on to the LSM method. Our numerical experiments show that irrespective of your speed/safety preference – and robustly across market scenarios – Delta LSM gives a marked improvement over classical LSM.

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.

Abstract

Classical approaches to optimal portfolio selection problems are based on probabilistic models for the asset returns or prices. However, by now it is well observed that the performance of optimal portfolios are highly sensitive to model misspecifications. To account for various type of model risk, robust and model-free approaches have gained more and more importance in portfolio theory.

In this talk, we discuss a pathwise framework covering the most commonly applied trading strategies and mathematical models of financial markets possibly under model uncertainty. Based on this pathwise framework, we develop a model-free approaches to stochastic portfolio theory, Cover’s universal and log-optimal portfolios. In particular, we do provide a pathwise stability analysis for portfolios and can treat significantly more general portfolios in a model-free setting, compared to previous model-free approaches.

The talk is based on joint works with Andrew Allan, Christa Cuchiero, Anna Kwossek and Chong Liu.

Biography

2023- now     Full professor at University of Mannheim
2019–2023    Assistant professor (tenure-track) at University of Mannheim
2017–2019    Lecturer at University of Oxford
2015–2017    Postdoctoral researcher at ETH Zürich
2012–2015    Ph.D. student at Humboldt-Universität zu Berlin

Abstract

We discuss and introduce a robust Deep Hedging framework for pricing and hedging options under model uncertainty, considering market frictions. In particular, based on word embeddings in natural language processing, we propose a neural model for training model embeddings which utilizes the paths of several advanced equity option models with stochastic volatility in order to learn the relationships that exist between hedging strategies under uncertainty. Moreover, we examine the dependence of the observed Profit and Loss distributions on the parameter space of the models used to learn the embeddings. The proposed framework serves as a construction basis for an efficient and robust simulation tool for the systematic development of an entirely model-independent hedging strategy.

Biography

Fabienne Schmid is a Senior Consultant for quantitative finance at RIVACON GmbH. Recently, her work is centered around the development and implementation of quantitative models using AI/Machine Learning methods for applications in the financial industry. She received her PhD in Atmospheric Physics in 2022 from the Goethe University Frankfurt, where her research focused on numerical methods for weather and climate models.

Abstract

Robust modeling approaches often start with a given parameter set. In this paper we study the question how to obtain this parameter set through calibrating stochastic models to historical data.

We focus on affine models under parameter uncertainty, while the approach can be applied similarly to other model classes.

Our approach introduces a dynamic calibration to an observed time series of market prices by determining parameter sets in such a way that bid/ask price intervals are reproduced by the non-linear model.

Through numerical illustrations employing US bond market data and SOFR rates, we show that our dynamic calibration approach outperforms non-robust approaches in typical applications such as hedging of derivatives

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

In this talk, we explore the current state of climate risk implementation within the German financial sector, based on a recent survey conducted among a representative group of financial institutions. The survey reveals a disparity between Significant Institutions (SIs) and Less Significant Institutions (LSIs) in their adoption of climate risk methodologies. While SIs are able to leverage their experience from several European Central Bank (ECB) stress testing exercises, LSIs face considerable challenges, particularly due to significant data gaps and resource constraints.

In the second part of the presentation, we delve into a practical example from the real estate sector, focusing on climate risk in mortgage portfolios. This case study highlights the tangible risks associated with property valuations under climate stress scenarios, emphasizing the need for robust modeling and risk assessment tools.

Finally, we present ongoing research aimed at bridging the data gaps hindering LSIs’ ability to implement climate risk strategies effectively. This research includes efforts to improve data availability, consistency and integration as well as more accessible methodologies tailored to institutions with limited resources.

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

Primary insurers require the reinsurance giants to participate in their underwriting risks of meteorological and other catastrophic impacts to be able to address the market. The reinsurance market is currently heavily in favour of reinsurers both with respect to price and to underwriting terms. The catastrophe bonds market has printed substantial profits since the last serious meteorological event (which occurred September 2022).
Risk management rests on analysis of historic events (such as Miami 1926) and advanced simulation. A number of techniques and critical uncertainties around climate modelling and adverse weather modelling from first physical atmospheric principles as well as mathematical and computational bottlenecks will be investigated.

Biography

Erik was elected to the Board as a Non-Executive Member Director in September 2020. He has formerly served on the Board as a Non-Executive Professional Director between February 2016 to June 2019 and then between June 2019 to December 2019 he held the role of Interim Chief Executive Officer.

Erik is also an advisor on the Industrial Advisory Board at University College London (Chemical Engineering Department), and Maxeler Technologies Limited, following a career in investment banking, insurance, asset management and the petrochemical industry. He co-founded EU initiatives on high performance computing and big data in finance and co-authored “High-Performance Computing in Finance” and “Tercentenary Essays on the Philosophy and Science of Leibniz”. Erik graduated as MBA at London Business School and as chemical engineer at Universiteit Gent (Belgium).

Abstract

There is a common misconception about market data being not fit for the cloud, or to difficult to deploy in the cloud. Using simple shell scripting and the containerization technology “Docker”, Rittik Wystup will attempt to show just how easy the cloud can be by live installing BCC Group’s ONE Platform on an AWS instance.

Biography

Feb 2021 – dato

Software Developer at BCC Group International GmbH

Oct 2018 – Jan 2021

Apprenticeship [Software Development] BCC Group International GmbH

June 2018

BA (Hons) Creative Music Technology [Anglia Ruskin University], Cambridge, UK

May 2015

Abitur from Goethe-Gymnasium, Frankfurt (Main)

Abstract

The options market in EUR-TRY appears attractive to many traders and investors as volatilities are high, the market is relatively liquid and the smile is heavily skewed to the upside, so OTM calls are expensive. The obvious investment strategy is to sell OTM EUR calls and hope for the best. I will explain the strategy, the potential chances and risk, illustrate how a bank amplified the disaster in a stress-situation, and also compare with no-lessons-learned from another large-loss caused in USD-TRY options trading in 2007.

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.