Tag: Conference

Video Description

A Talk from Portfolio Management Conference 2025

Quantitative strategies rely on stationarity of the underlying processes. Parameter drift eventually kills all strategies, how ever brilliant they may be. This talk will explore some methods for unmixing financial timeseries using intrinsic relaxation timescales, recovering any stationary processes, but also allowing us to separate “slow” from “fast” processes. This has various interesting implications for trading and portfolio allocation, which we will briefly discuss.

Speaker Bio

Dr. Jan Rosenzweig

Jan has been working in the financial markets for close to 20 years, as a quant, structurer, trader and portfolio manager. He worked at Credit Suisse, Rabobank, HSH Nordbank, IV Capital, Brancherose and Pine Tree. He has a PhD in applied maths from Cambridge and BSc in applied maths and computer science from Zagreb. Jan is based in London.

Video Description

A Talk from Portfolio Management Conference 2025

We introduce an option pricing model with clusters of jumps by incorporating a bivariate Hawkes process with exponential decay memory kernel. The Hawkes process characterises the self- and cross-excitement of positive jumps and negative jumps, allowing the model to effectively capture the volatile price dynamics, jump arrival times and implied volatility smiles often observed in cryptocurrencies such as Bitcoin (BTC). We derive jump risk premia for positive and negative jumps, defined as the discrepancies in jump measures between the objective measure and the risk-neutral measure. Our findings reveal that these jump risk premia: (i) provide insights on how the BTC options market reacted to major events, such as the COVID-19 outbreak and the FTX scandal; (ii) possess significant predictive power for delta-hedged option returns; and (iii) are indicators in explaining the volatile cost-of-carry implied from BTC futures prices.

Joint work with Francis Liu and Artur Sepp.

Speaker Bio

Professor Natalie Packham

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 “Quantitative Finance”, “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.

Video Description

A Talk from Annual Conference 2024

Exchanges have a function of facilitating trades buy allowing for buyers and sellers of a security to meet and trade. However many strategies require the trade of not only one security but simultaneous execution of several legs, involving several securities. Examples include pair trading, equity portfolios, multi–maturities Futures strategies and options combinations. We call these multi-legs trades bundles. Not executing the various legs at the same time creates a risk of adverse price movement before the full completion. It can be eliminated by crossing the spreads and posting market orders but it incurs cost. We present a mechanism that allows for market participants to post one-sided orders on arbitrary bundles and then the matching engine computes (case of buy order) the cheapest super-replication of the posted bundle by a portfolio of other already posted bundles. So the features of the algorithm is that 1) it does not match security by security but rather a bundle with a collection of other bundles and 2) it can be a “super-match” in the sense that the super-replication may give the additional benefit of a positive residual. We detail the algorithm and apply this methodology to show how it can in certain cases improve the price of option combinations. This approach has an economic value as it allows for more trades to occur at mutually desirable prices.

We focus in particular on bundles of options of a same maturity on a stock. The dominance in terms of profile is easy to understand in the case of European options and we show how it can be generalized to American options.

Speaker Bio

Bruno Dupire

Paul is the founder of the Certificate in Quantitative Finance and Wilmott.com and he is internationally renowned as a leading expert on quantitative finance. His research work is extensive, with more than 100 articles in leading mathematical and finance journals, as well as several internationally acclaimed books on mathematical modeling and derivatives, including the best-selling Paul Wilmott On Quantitative Finance, published by John Wiley & Sons.

Video Description

A Talk from Annual Conference 2024

In the past decade, machine learning techniques have been widely adopted in the equities space, driving innovation both in academia and among practitioners. Leveraging the rich data environment of single-name equities, these advancements have unlocked new insights in quantitative finance. However, the application of machine learning in commodities futures markets remains underexplored, primarily due to the “data-poor” nature of this asset class compared to equities. This research investigates the use of machine learning in systematic commodities futures trading, expanding beyond the traditional price-based strategies that have dominated the field. We integrate well-established factor-based signals, commonly used in commodities investing, as inputs into our machine learning models. Our findings demonstrate that machine learning enhances the evolution of systematic commodities strategies, offering a more adaptable and comprehensive framework for forecasting market behavior and constructing long-short portfolios. Utilizing a 30-year dataset of 44 commodities futures contracts, we train machine learning models to predict future performance and build long-short portfolios. The results indicate that these models not only rival the performance of traditional systematic approaches but also provide deeper insights into market dynamics and critical factors influencing commodities asset pricing.

Speaker Bio

Tony Guida

Tony Guida is a Quantitative Portfolio Manager and researcher. He began his career at Unigestion in 2006, joining the quantitative equity team as a research analyst. He later became a member of the Research and Investment Committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients. In 2015, Tony moved to Edhec Risk Scientific Beta as a Senior Consultant, specializing in risk allocation and factor strategies. In 2016, he joined a major UK pension fund to build an in-house systematic equity strategy, co-managing £8 billion as a Senior Quantitative Portfolio Manager.

In January 2019, Tony joined RAM Active Investments as a Senior Quantitative Researcher in equities, eventually co-heading the systematic macro hedge fund offering. In 2023, he co-founded a hedge fund, serving as Co-Head of Research and Chief Data Scientist, focusing on systematic cross-asset strategies.

Tony holds Bachelor’s and Master’s degrees in Econometrics, Economics, and Finance from the University of Savoy, France. He has co-authored and edited several books on the application of machine learning in finance, including:
• Big Data and Machine Learning in Quantitative Investment (Wiley, 2018)
• Machine Learning for Factor Investing, R Version (CRC, 2020)
• Machine Learning for Factor Investing, Python Version (CRC, 2023)

He is also an advisory board member for the Financial Data Professional Institute and serves as a reviewer for machine learning papers in several academic journals.

Here’s the Annual Quant Insights Conference 2024 with a brand new line-up of talks from leading quant finance experts.

Video Description

A Talk from Annual Conference 2024

This presentation introduces the application of quantum-inspired Tensor Networks in quantitative finance, focusing on their ability to handle high-dimensional data efficiently. We explore the relationship between quantum computing and Tensor Networks, and how these methods can enhance machine learning models. Specific attention is given to the use of Tensor Network machine learning in derivatives pricing, where they can offer improved accuracy and computational efficiency. By leveraging these techniques, Tensor Networks have the potential to transform both machine learning and financial modeling.

Speaker Bio

Dr. Samuel Palmer

Dr. Samuel Palmer is a seasoned expert in machine learning and quantum computing, with extensive experience leading cutting-edge projects in the financial technology space. As Engineering Director at Multiverse Computing, Samuel spearheads the development of advanced machine learning solutions and quantum computing applications for options pricing and other financial applications. With a PhD in Computational Finance from University College London, Samuel is also deeply involved in research, holding multiple patents and publishing on quantum-inspired methods for derivatives pricing and portfolio optimization.

Video Description

A Talk from Annual Conference 2024

It is argued that in a variety of ways randomness plus path continuity is a strange and almost contradictory request. Attention is then turned to discontinuous continuity approximations with financially relevant results being delivered by finite activity processes of finite variation. The CGMY model is employed to decompose stochastic volatility into stochasticity in speed, scale and the degree of continuity. The bilateral gamma model is used to relate risk premia to the degree of continuity as measured by the speed and scale coefficients. Rescaling mechanisms are introduced to recover return distributions from option prices that are useful for investment analysis.

Speaker Bio

Professor Dilip Madan

Dilip Madan is Professor Emeritus of Mathematical Finance at the Robert H. Smith School of Business. Currently he serves as a consultant to Morgan Stanley, and Norges Bank Investment Management. He is a founding member and past President of the Bachelier Finance Society. He received the 2006 von Humboldt award in applied mathematics, was the 2007 Risk Magazine Quant of the year, received the 2008 Medal for Science from the University of Bologna, held the 2010 Eurandom Chair and is the IAQF Financial Engineer of the Year 2021. He has published over 200 papers and serves on the Advisory Board of Frontiers of Mathematical Finance and as a Director of the Scientific Association of Mathematical Finance.

Video Description

A Talk from Annual Conference 2024

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 for 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.

Speaker Bio

Blanka Horvath

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 and Emmy Noether Fellow of the London Mathematical Society. Prior to her current role, she held faculty 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.

Video Description

A Talk from Annual Conference 2024

Career advice. From me?!; Investors = Bosses; My experience; Behavioural Finance 101; The simplest model for Happiness; A few results.

Speaker Bio

Dr. Paul Wilmott

Paul is the founder of the Certificate in Quantitative Finance and Wilmott.com and he is internationally renowned as a leading expert on quantitative finance. His research work is extensive, with more than 100 articles in leading mathematical and finance journals, as well as several internationally acclaimed books on mathematical modeling and derivatives, including the best-selling Paul Wilmott On Quantitative Finance, published by John Wiley & Sons.

Video Description

A Talk from Annual Conference 2024

This talk will explore how certain aspects of the green shift may potentially lead to “green inflation,” driven by money printing and investment in less effective energy production methods, especially in an environment where populism is prevalent. Additionally, we will briefly discuss our views on quantum computers, including quantum gravity computers, and their potential implications for finance and the society.

Speaker Bios

Professor Espen Gaarder Haug

Espen Gaarder Haug has accumulated over 30 years of experience in derivatives trading and research. He served as a proprietary option trader at J.P. Morgan Chase in New York and as a trader for the hedge fund Paloma Partners. Dr. Haug’s prolific publications span journals such as Quantitative Finance, the International Journal of Theoretical and Applied Finance, and Wilmott Magazine.

He currently holds the position of Professor of finance at the Norwegian University of Life Science. Despite being professor in finance he has over the past decade primarily dedicated his efforts to research in the field of physics, particularly focusing on developing a full quantum gravity theory. Professor Haug has also authored numerous articles at the intersection of finance and physics, highlighting the connection between these two disciplines, his talk will be along some of these lines.