Tag: Conference

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.

Video Description

A Talk from Annual Conference 2024

The panel discusses the importance of real options in the energy ecosystem in the context of energy transition with the focus on battery storage and other critical new technologies. We will debate their potential implications on prices and volatility across different energy markets.

Speaker Bios

Dr. Ilia Bouchouev

Dr. Ilia Bouchouev is the former President of Koch Global Partners where he launched and managed global derivatives trading business for over 20 years. Over the years, he introduced several energy derivatives products and was recognized as one of the pioneers in energy options trading. He is currently a managing partner at Pentathlon Investments and an adjunct professor at New York University, where he teaches energy trading at Courant Institute of Mathematical Sciences. He is also a senior research fellow with Oxford Institute for Energy Studies.

Professor Doyne Farmer

J. Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking and Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment, University of Oxford. He is also External Professor at the Santa Fe Institute and Chief Scientist at Macrocosm.

His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to UBS in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology. His book, Making Sense of Chaos: A Better Economics for a Better World, was published in 2024.

During the 1980s he was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student in the 1970s he built the first wearable digital computer, which was successfully used to predict the game of roulette.

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.

Video Description

A Talk from Annual Conference 2024

We develop a model based on time changed Lévy processes and study its ability of reproducing the joint S&P500/VIX implied volatility smiles and the VIX futures prices – a problem known in the literature as the ‘joint calibration problem’. The model admits semi-analytical characteristic functions for the key quantities, and therefore efficient Fourier based pricing schemes can be deployed. We focus on a specification of the proposed general setting which uses purely discontinuous processes. Results from the application to market data show satisfactory performances in solving the joint calibration problem, and therefore demonstrate that the class of affine processes can provide a workable fit. (Joint work with Ernst Eberlein and Grégory Rayée).

Speaker Bio

Professor Laura Ballotta

Professor Ballotta works in the areas of quantitative finance and risk management. She has written on topics including stochastic modelling for financial valuation and risk management, numerical methods aimed at supporting financial applications, and the interplay between finance and insurance. Recent major contributions have appeared in Journal of Financial and Quantitative Analysis, European Journal of Operational Research and Quantitative Finance among others. A former pole-vaulter, she is passionate of track and field.

Video Description

A Talk from Annual Conference 2024

Twenty five years ago, several authors proposed to model the forward interest rate curve as an elastic string along which idiosyncratic shocks propagate, accounting for the peculiar structure of the return correlation across different maturities. In this paper, we revisit the specific “stiff” elastic string field theory of Baaquie-Bouchaud 2004 in a way that makes its micro-foundation more transparent. Our model can be interpreted as capturing the effect of market forces that set the rates of nearby tenors in a self-referential fashion. The model is parsimonious and accurately reproduces the whole correlation structure of the FRC over the time period 1994-2023, with an error below 2 \%$. The dependence of correlation on time resolution (also called the Epps effect) is also faithfully reproduced within the model and leads to a cross-tenor information propagation time of a few tens of minutes. Finally, we confirm that the perceived time in interest rate markets is a strongly sub-linear function of real time. In fact, our results are fully compatible with hyperbolic discounting, in line with the recent behavioural Finance literature.

Speaker Bio

Jean-Philippe Bouchaud

Jean-Philippe Bouchaud is a pioneer in Econophysics. He co-founded the company Science & Finance in 1994, which merged with Capital Fund Management (CFM) in 2000. In 2018 he was appointed as an adjunct Professor at Ecole Normale Supérieure, where he teaches a course on complex systems. He is also a member of the French Académie des Sciences. His work includes the physics of disordered and glassy systems, granular materials, the statistics of price formation, stock market fluctuations, and agent based models for financial markets and for macroeconomics. He was awarded the CNRS Silver Medal in 1995, the Risk Quant of the Year Award in 2017, the Lars Onsager prize in 2024. He is the author of several books in physics and finance, including “Trades, Quotes & Prices” (CUP, 2018).