Video Description
Quantum Machine Learning (QML) is an exciting area of quantum computing research that promises to be the first to deliver tangible quantum advantage and quantum utility since many of its algorithms are resistant to some types of noise and do not require large fault-tolerant quantum computers. QML is ideally suited to conducting experiments on the Noisy Intermediate-Scale Quantum (NISQ) computers. We are investigating what is behind the power of QML models and present the latest generation of QML algorithms with applications in quantitative finance.
Speaker Bio
Dr. Oleksiy Kondratyev
Oleksiy Kondratyev is ADIA Lab Research Fellow and Visiting Professor at the Department of Mathematics, Imperial College London. Prior to joining ADIA in 2021 as Quantitative Research & Development Lead, Oleksiy was Managing Director and Head of Data Science & Innovation at Standard Chartered Bank in London.
Oleksiy has over 25 years of quantitative finance experience in both risk management and front office roles and has been recognized as Quant of the Year (2019) by Risk magazine for his research on the application of machine learning techniques to risk factor analysis and portfolio optimisation.
Oleksiy holds MSc in Theoretical Physics from Taras Shevchenko National University of Kyiv and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine. His research interests are in machine learning and quantum computing.
Watch the recordings from the AI and Machine Learning in Quant Finance Conference 2025.
Video Description
A Talk from the Volatility and Risk Conference 2025
Liar’s Poker was a popular game among Wall Street traders from the mid-1980s to the mid-1990s, made famous by Michael Lewis’ book of the same name. Although the game was played for very high stakes among highly quantitative people, no exact game theory solution has been found. Recent AI approaches seem to dominate human players. Given the close resemblance between the game and financial trading, this could be a bellwether for the impact of AI on trading and risk management.
Speaker Bio
Aaron Brown
Aaron Brown is a columnist for Bloomberg and Wilmott Magazine, and teaches at New York University, University of California at San Diego and New Mexico State University. He worked for 35 years on Wall Street as a trader, portfolio manager, head of mortgage securities and risk manager for firms including Morgan Stanley and AQR Capital Management. He is the author of The Poker Face of Wall Street, Red-Blooded Risk, Financial Risk Management for Dummies and A World of Chance (with Reuven and Gabrielle Brenner). He won the 2011 Risk Manager of the Year award from the Global Association of Risk Professionals. He was a top professional poker player in the 1970s and 80s, and continues active poker, sports betting and other gambling activities. He splits his time between Manhattan, New Mexico and Coronado Island. He has degrees from Harvard (Applied Math) and the University of Chicago (Finance and Statistics). He has been actively involved in crypto trading, venture capital and start-ups since 2011.
Video Description
A Talk from Volatility and Risk Conference 2025
Dr. Mark Rosenberg has a PhD in political science from UC Berkeley and is speaking about using data science and political science to systematically measure geopolitical risk and how it impacts financial markets. – The presentation focuses on “Trump 2.0” and analyzing the risks and market impacts emerging from Trump’s second administration, including political volatility, geopolitical tensions, regime stability, etc. – Mark explains GeoQuant’s methodology for quantifying political risk across 146 countries using machine learning and natural language processing on news reports, social media, and expert analysis. – He discusses the “EMFication” of the US – how US political risk is increasingly resembling that of volatile emerging markets. – The talk examines rising protectionism, nationalism/populism in the US and abroad and its market consequences like impacts on the US dollar, Treasuries, equities, etc. – It shares models GeoQuant has built around assets like gold and US equities that incorporate political risk factors to predict prices and risk-adjusted returns. – Rosenberg also discusses forecasts for increasing geopolitical tensions and bilateral risks in regions like South Asia.
Speaker Bio
Dr. Mark Rosenberg
This video of Dr .Rick Bookstaber giving a quant insights conference presentation related to risk management and how it can be transformed using artificial intelligence covers the following: – Innovations in risk management enabled by AI, specifically large language models (LLMs) and embedding/similarity scoring – Rick argues risk management has not changed much since the 1990s and still relies on historical data and models. AI can enable qualitative improvements. – He outlines 4 key areas of improvement: moving from numbers to narratives; from financial to physical risk; from static to dynamic methods; and from historical to forward-looking risk – LLMs can now process vast amounts of text data and generate narratives and explanations around risk, instead of just generating reports – Embedding company data can enable better analysis of physical/supply chain risk and correlations – Agent-based modeling can capture market dynamics better than static models – LLMs may ultimately be able to serve as “chief risk officers” that can converse with executives, relate risks to past crises, and provide rich explanations – Dr .Rick Bookstaber is actively developing new AI-enabled risk applications and encourages collaboration
Video Description
A Talk from Volatility and Risk Conference 2025
Professor Emanuel Derman, Professor Emeritus of Financial Engineering, Columbia University presents the history of quantitative finance and financial models over the past several decades. Professor Derman outlines key innovations and developments in finance, starting with derivatives and the ideas of Spinoza in the 17th century. He then discusses later advancements like the concepts of risk, volatility, diversification, hedging, and no-arbitrage pricing. Much of the talk focuses on the evolution of models like CAPM, APT, and Black-Scholes for valuing securities and options. Emanuel Derman describes how these models quantify relationships between risk and return. Towards the end of the talk, Derman touches on more recent innovations like machine learning in finance as well as continued challenges with modeling volatility and risk. He speculates briefly on potential future directions like AI and quantum finance. Throughout the talk, the Derman aims to show how modern finance has built upon major conceptual breakthroughs over the past 70+ years. He uses stock examples and diagrams to illustrate key points.
Speaker Bio
Dr. Rick Bookstaber
This video of Dr .Rick Bookstaber giving a quant insights conference presentation related to risk management and how it can be transformed using artificial intelligence covers the following: – Innovations in risk management enabled by AI, specifically large language models (LLMs) and embedding/similarity scoring – Rick argues risk management has not changed much since the 1990s and still relies on historical data and models. AI can enable qualitative improvements. – He outlines 4 key areas of improvement: moving from numbers to narratives; from financial to physical risk; from static to dynamic methods; and from historical to forward-looking risk – LLMs can now process vast amounts of text data and generate narratives and explanations around risk, instead of just generating reports – Embedding company data can enable better analysis of physical/supply chain risk and correlations – Agent-based modeling can capture market dynamics better than static models – LLMs may ultimately be able to serve as “chief risk officers” that can converse with executives, relate risks to past crises, and provide rich explanations – Dr .Rick Bookstaber is actively developing new AI-enabled risk applications and encourages collaboration
Video Description
A Talk from Volatility and Risk Conference 2025
Professor Emanuel Derman, Professor Emeritus of Financial Engineering, Columbia University presents the history of quantitative finance and financial models over the past several decades. Professor Derman outlines key innovations and developments in finance, starting with derivatives and the ideas of Spinoza in the 17th century. He then discusses later advancements like the concepts of risk, volatility, diversification, hedging, and no-arbitrage pricing. Much of the talk focuses on the evolution of models like CAPM, APT, and Black-Scholes for valuing securities and options. Emanuel Derman describes how these models quantify relationships between risk and return. Towards the end of the talk, Derman touches on more recent innovations like machine learning in finance as well as continued challenges with modeling volatility and risk. He speculates briefly on potential future directions like AI and quantum finance. Throughout the talk, the Derman aims to show how modern finance has built upon major conceptual breakthroughs over the past 70+ years. He uses stock examples and diagrams to illustrate key points.
Speaker Bio
Professor Emanuel Derman
Emanuel Derman is professor emeritus at Columbia University, where he directed their program in financial engineering from 2003-2023. He was born in South Africa but has lived most of his professional life in Manhattan. He started out as a theoretical physicist, doing research on unified theories of elementary particle interactions. At AT&T Bell Laboratories in the 1980s he developed programming languages for business modeling. From 1985 to 2002 he worked on Wall Street where he co-developed the Black-Derman-Toy interest rate model and the local volatility model. He is the author of ‘The Volatility Smile’ (Wiley, 2017), ‘Models.Behaving.Badly’ (Free Press 2011) one of Business Week’s top ten books of 2011. He is also the author of ‘My Life As A Quant’ (Wiley 2004), also one of Business Week’s top ten of 2004, in which he introduced the quant world to a wide audience. His latest book, a memoir of youth in an immigrant community in South Africa, is ‘Brief Hours and Weeks: My Life as a Capetonian,’ (LML Press 2025).
Video Description
A Talk from Volatility and Risk Conference 2025
The presenter, Professor Laura Ballotta, Professor of Mathematical Finance, Bayes Business School introduces this talk about their joint research paper with Giovanni Amici and Patrizia Semeraro, which is part of Giovanni’s PhD thesis. – The goal of the research is to develop a multivariate model that can reproduce market implied volatility surfaces and give information about the implied correlation between assets. – Professor Laura Ballotta introduces the concept of additive and multivariate processes, specifically self-similar additive processes with stationary increments (SATO) processes. – They use these SATO processes to construct a multivariate model called the CSB model, where each asset has an idiosyncratic component driven by a time-changed Brownian motion, and a systematic component driven by a common time-changed Brownian motion. – Ballotta analyzes the CSB model’s ability to capture implied volatility surfaces, time-varying correlation, and other properties. They compare it to benchmark Lévy and other additive models. – As an application, they show calibration of the CSB model to FX (foreign exchange) market data, jointly calibrating multiple currency triangles while satisfying no-arbitrage constraints. – Professor Laura Ballotta concludes that the CSB model outperforms benchmarks in calibrating to market prices and provides references to the published paper.
Speaker Bio
Professor Laura Ballotta
Laura Ballotta is a Professor of Mathematical Finance at Bayes Business School (formerly Cass). Prof. 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. She serves as associate editor and referee for several international journals in the field.
Prof. Ballotta holds a BSc in Economics from Universita’ Cattolica del Sacro Cuore, a MSc in Financial Mathematics from the University of Edinburgh, and a PhD in Mathematical and computational methods for economics and finance, from Universita’ degli Studi Bergamo. She is the course director of the Quants cluster of MSc programmes at Bayes Business School.
Video Description
A Talk from Volatility and Risk Conference 2025
This video is from the quant insights conference where Dr. Gueorgui S. Konstantinov, Senior Portfolio Managerv gives a talk on the topic of “Causal Inference and Factor Network Interference and the Treatment Effect.” – The presentation is focused on causal inference and network interference when there are treatment effects applied to assets/factors in a financial network. This contrasts with traditional methods that assume no interference between treatment and control groups. – Constructing financial networks between assets and factors using the Wasserstein distance metric. The networks have properties like sparsity and power law degree distribution. – Modeling potential outcomes under different exposure conditions – direct, indirect, combined, null – for assets when certain nodes receive “treatment” (e.g. a policy change). This allows them to estimate causal effects. – Konstantinov selects treatment nodes either randomly or based on an economic reason and measure the resulting impact on a “dilation” effect such as overall portfolio returns. – Key findings are that more treatment nodes means less portfolio impact, and that factors become more diversified. Also, portfolios of less connected assets are more robust. The video also shows simulations and visualizations of the networks and treatment effects and concludes with a discussion of using this methodology to stress test portfolios.
Speaker Bio
Dr. Gueorgui S. Konstantinov
Gueorgui S. Konstantinov, Ph.D, CAIA, FDP is a senior portfolio manager and has over 18+ years of experience. He managed global bond portfolios and currencies for institutional investors and pension funds. He is an editorial board member of The Journal of Portfolio Management, The Journal of Alternative Investments, and The Journal of Financial Data Science. He earned the designations of Chartered Alternative Investments Analyst (CAIA) and Financial Data Professional (FDP). He received his MA in economics in 2005 and received a doctoral degree in 2008 from Vienna University of Economics and Business Administration (WU).
Video Description
A Talk from Volatility and Risk Conference 2025
We derive an exact relationship between the profit-and-loss (P&L) of a trend-following system with exponentially moving average (EWMA) filter and the auto-correlation function of returns generating stochastic process. This generic result appears to be new. Using this formula, we analyse the impact of the lookback span of EWMA filter on the P&L of the trend-following system when returns dynamics are driven by auto-regressive fractal processes with long memory. We show that the trend-following system is expected to perform well when autocorrelation of returns is positive over longer time scales, even if returns may exhibit mean-reversion over shorter time scales.
In the empirical part, we examine the autocorrelation observed in major futures markets and the performance of different trend-following systems applied to futures markets. We discuss and contrast the so-called American and European specifications of trend-following systems. Finally, we demonstrate the defensive profile of trend-following systems which provides diversification benefits to long-only portfolios.
Speaker Bio
Dr. Artur Sepp
Artur Sepp is the Global Head of Investment Services Quant Group at LGT bank in Zurich focusing on quantitative asset allocation and systematic investment strategies. Artur has almost 20 years of experience in financial markets, including heading quant research and portfolio management at a systematic hedge fund and a family office, as well as leading development of front-office quant strategies and derivatives at private (Julius Baer) and investment banks (Merrill Lynch/BofA). Artur has a PhD in Mathematical Statistics from the University of Tartu, an MSc in Industrial Engineering and Management Sciences from Northwestern University, and a BA cum laude in Mathematical Economics from Tallinn University of Technology. His expertise covers quantitative investing and asset allocation, quantitative modelling of derivative securities, machine learning and data science, and blockchain applications within decentralised finance. He is the author and coauthor of several research articles on quantitative finance published in key journals. Artur won the Quant of the Year Award from Risk Magazine (2024). He is an active martial arts practitioner in his free time.
Video Description
A Talk from Volatility and Risk Conference 2025
This Quant Insights talk is a discussion between Dr. Paul Wilmott, Founder, Certificate in Quantitative Finance (CQF) and Dan Tudball, Editor, Wilmott Magazine, They discuss various topics related to volatility and risk modeling in finance, including: – Uncertainty in markets and economy, factors affecting volatility – Evolution of volatility modeling over time, from simplicity to complexity – Whether complex volatility models provide false sense of security – Role of machine learning and AI in finance now – Regime switching models – Making money from volatility trading – David Orrell’s quantum variance (Q variance) model and unusual empirical relationship he found – Possibilities of non-classical, quantum based approaches in finance – Speculation on what topics will replace volatility modeling as a focus area in future – Replacement of human jobs by AI, including in finance
Speaker Bio
Dr. Paul Wilmott
Dr. Paul Wilmott 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.
Paul has extensive consulting experience in quantitative finance with leading US and European financial institutions. He has founded a volatility arbitrage hedge fund and a university degree course. Paul has lectured at all levels, to students and to practitioners.
Dan Tudball
Dan Tudball is the Editor of Wilmott Magazine, the respected publication serving quantitative finance practitioners in finance, industry, and academia. The magazine publishes new work from leading authors in the field alongside columns from industry greats, and editorial reflecting the lifestyles and interests of a qualitatively demanding readership.
