Video Description
In this talk, Dr. Adam Rej discusses two mechanisms that can load to a decay of a systematic strategy in production: in-sample overfitting and arbitrage. Thanks to the reconstruction of 72 equity strategies / factors published in academic literature, he can test various proxy variables of overfitting and arbitrage in the cross-section and determine their statistical significance. Dr. Rej concludes with practical implications for systematic investors.
Speaker Bio
Dr. Adam Rej
Adam Rej is Head of Macro Alpha, based in New York. He has previously worked for the Portfolio Construction and Alternative Beta teams. Prior to joining CFM, Adam held post-doctoral positions at École Normal Supérieure (Paris), Institute for Advanced Study (Princeton) and at Imperial College London. His PhD research was at the Max Planck Institute for Gravitational Physics – Potsdam in the field of theoretical physics. Adam joined CFM in 2014.
Video Description
For long term Strategic Asset Allocation, model portfolios are defined at the level of indexes. The possible outcomes at a scale of a few decades is obtained by Monte Carlo simulations, resulting in a probability density for the portfolio values. Such studies are critical for long term wealth plannings, for example in the financial component of social insurances. The base model is a constant drift, a constant covariance and normal innovations, as pioneered by Bachelier. Beyond this model, this presentation summarizes a multivariate process that incorporate the most recent advances in the models for financial time series. This includes a dynamic drift estimate, the heteroskedasticity (i.e. the volatility’ dynamics), and the fat tails and asymmetry for the distributions of returns. The quantitative outcomes depend critically on the drift, because this is a non random contribution acting at each time step. The changes introduced by the drift dynamics is the partial decoupling between the volatility along the time direction from the standard deviation of the terminal values. Finally, the main statistics for the wealth at increasing time are presented, showing the key features added by the components beyond the basic normal random walk.
Speaker Bio
Gilles Zumbach
Gilles Zumbach has been doing research on many topics in finance, ranging from tick-by-tick time series to very long term market simulations, from risk evaluations to realistic option pricing, from large scale portfolio optimisation to pricing. He has worked for several institutions, including banks, hedge funds and service providers. Gilles has published over 35 research papers in finance, most of them using careful data analyses combined with mathematical models. He wrote a book published by Springer Verlag, linking fundamental research on time series to applications in finance. Recurring themes in his work are processes and volatility. In a former life, he was as a physicist.
Video Description
In this talk, Dr. Krishnan presents a plausible model for the dollar size of the US equity market that incorporates passive share. The model relies upon a small set of assumptions that are nearly universally accepted, or at a minimum widely used by quant practitioners. While their assumptions are seemingly innocuous and uncontroversial, the implications of our model should be a cause for significant concern. That is the main point they wish to emphasize in their paper. Once the passive share reaches around 65%, index volatility may increase sharply. At 90% share, an increase in volatility at cubic speed is nearly inevitable, leading to exaggerated boom and bust cycles. This research was conducted jointly with Michael Green and Stephan Sturm.
Speaker Bio
Dr. Hari Krishnan
Hari P. Krishnan is Head of Volatility Strategies at SCT Capital in New York. He is a noted author of The Second Leg Down (Wiley 2017) and lead author of Market Tremors (Springer/Palgrave McMillan 2021). He has held various portfolio management roles at Doherty Advisors, CrossBorder Capital and Morgan Stanley over the past 20 years. Hari holds a PhD in Applied Math from Brown University and was a postdoctoral research associate at the Columbia University Earth Institute.
Video Description
In this talk, Jörg Kienitz outlines the potential of mixture models for generating reliable market data – GenAI. Examples like implied volatility surfaces serve as an illustration. This includes generating implied volatility surfaces based on realized ones, checking arbitrage and using liquid markets to obtain surfaces for illiquid ones. The mixture models applied include the well known Gaussian Mixtures but also fat tailed versions can be applied comparatively easy.
Speaker Bio
Jörg Kienitz
Jörg is a Quantitative Finance professional for more than 25 years. After holding positions as Head of Quant with Postbannk/Deutsche Bank, Deloitte or LSEG, he is now Director of Quantitative Methods at mrig, a Frankfurt based consultancy. He also works as an adjunct associate professor at UCT, Cape Town, and as an assistant professor at BUW, Wuppertal. Jörg is frequently speaking on conferences, (co-) authored four books with Wiley and Springer and published many research and practitioners papers in Quantitative Finance, Journal of Computational Finance, RISK or Wilmott.
Video Description
Model calibration under P and under Q are often regarded as two separate branches of finance. P refers to a backward-looking real-world probability measure under which the observed historical price path of a financial asset is viewed as a realization of a stochastic process, while Q denotes a forward-looking risk-neutral measure inferred from the prices of options written on this underlying asset. Accordingly, model estimation based on past prices of the underlying asset is referred to as estimation “under P”, while estimation from option prices is known as calibration “under Q”. One may question whether such a strict separation is justified or whether it rather reflects the lack of models able to capture the joint dynamics of prices and implied volatilities. Path-dependent volatility models are uniquely positioned to reconcile P- and Q-calibration, since they precisely relate past asset returns to volatility, thus to option prices.
In this video, Professor Guyon introduces the discrete-time 4- (or 3-)factor path-dependent volatility model and shows that combining the path-dependency of volatility that is uncovered in the article Volatility Is (Mostly) Path-Dependent (Guyon and Lekeufack, 2023) with fat-tailed random innovations allows him to reconcile model calibration under P and under Q, which further supports the hypothesis of high endogeneity of volatility. He also proposes a new estimation approach that combines P- and Q-information to enhance calibration robustness, and benchmarks its effectiveness against classical methods. This is joint work with Léo Parent.
Speaker Bio
Professor Julien Guyon
Julien Guyon is a Professor of Applied Mathematics at École nationale des ponts et chaussées, Institut Polytechnique de Paris, where he holds the BNP Paribas Chair Futures of Quantitative Finance, a Visiting Associate Professor in the Department of Finance and Risk Engineering at NYU Tandon, and an adjunct professor in the Department of Mathematics at Columbia University. Julien worked in the financial industry for 16 years, first in the Global Markets Quantitative Research team at Societe Generale in Paris, then as a senior quantitative analyst in the Quantitative Research group at Bloomberg L.P., New York. Julien received the 2025 Quant of the Year award by Risk, and is a Louis Bachelier Fellow. He is best known for designing the particle method for smile calibration (with Pierre Henry-Labordère), the Bergomi-Guyon expansion, and his works on path-dependent volatility (in particular the so-called Guyon-Lekeufack model) and on the joint calibration of S&P 500 and VIX smiles. He serves as an Associate Editor for 4 academic journals, co-authored the book Nonlinear Option Pricing, has extensively published in peer-reviewed journals, and is a regular speaker at international conferences, both academic and professional. A big soccer fan, Julien has also published articles on fairness in sports both in academic journals and in top-tier newspapers including The New York Times, The Times, Le Monde, and El País. Some of his suggestions have been adopted by FIFA and UEFA, including a fairer draw method for the FIFA World Cup since 2018 and a fairer format for the 2026 FIFA World Cup.
Catch up with the Volatility and Risk in Quant Finance Conference, featuring an exciting lineup of groundbreaking discussions on the latest industry advancements.
Video Description
Portfolio Management Conference 2026
We extend stationary portfolio optimisation to maximise the probability of achieving target returns – a key objective for sovereign wealth funds (SWF) and pension funds.
Our framework optimizes portfolio weights as functions of market drivers rather than time. We derive analytical solutions for the Gaussian case, revealing a “phase transition” where optimal solutions switch character as targets cross certain thresholds – specifically, the solution switches from maximum to minimum probability regimes when the target barrier crosses the portfolio’s natural drift level. To obtain allocation weights for the general stationary case, we use the obtained Gaussian solution to initialize a highly efficient numerical iterative procedure based on quadratic utility approximations. This approach shows significant improvements over initial Gaussian approximations, with material differences in both optimal weights and achievable probabilities.
Numerical experiments demonstrate advantages over static allocation strategies, offering practical tools for long-horizon institutional investing.
The talk covers theoretical foundations, implementation, and applications to institutional asset management.
Speaker Bio
Dr. Alexandre Antonov
Alexandre Antonov is a leading quantitative finance practitioner currently working at the Abu Dhabi Investment Authority (ADIA). He holds a PhD from the Landau Institute for Theoretical Physics in Russia. Throughout his career, Dr. Antonov has made significant contributions to derivatives pricing methodology and risk modeling at several financial institutions, including Numerix, Standard Chartered Bank and Danske Bank. His experience spans both sell-side institutions, where he developed sophisticated pricing models for trading desks, and buy-side organizations, where he has applied rigorous quantitative methods to investment strategies and risk management frameworks. This dual perspective has informed his practical approach to mathematical finance, particularly in the areas of interest rate modeling and volatility calibration techniques. Dr. Antonov was named Risk Magazine’s Quant of the Year, a prestigious recognition of his contributions to quantitative finance. He has published papers in industry journals and has participated in the development of quantitative methodologies used in derivatives pricing systems.
Video Description
Portfolio Management Conference 2026
In a market environment marked by geopolitical shocks, inflation uncertainty, and rising cross-asset correlations, traditional diversification alone may not provide sufficient protection against extreme drawdowns. Shielding Portfolios from Extremes: Tail Risk Strategies for a Turbulent Era explores how institutional investors can incorporate convex downside protection to enhance portfolio resilience while preserving long-term return objectives. The presentation compares key tail risk approaches, including options overlays, managed futures, quantitative multi-strategy hedges, and structured solutions. The presentation will high their trade-offs in cost, liquidity, governance, and effectiveness. It provides a practical framework for designing sustainable protection programs that reduce vulnerability to severe shocks while maintaining strategic flexibility in volatile markets.
Speaker Bio
Aous Labbane
Aous Labbane is the founder of Jasmin Capital and Consulting, a firm focused on investment products and quantitative investment strategy development and distribution. He previously held senior leadership roles at leading financial institutions, including Managing Director at Nomura, where he headed EMEA Equity Structuring, and Partner & Global Head of Business Development at Ossiam (Natixis Investment Managers), a French asset manager specializing in ETFs and quantitative strategies. Earlier, at Credit Suisse, he was Global Head of Fund Linked and European Head of Equity Derivatives Structuring and a member of the European Equity Derivatives Management Committee. With deep expertise in cross-asset structuring, risk management, portfolio allocation, and product innovation and distribution, Aous has advised and delivered solutions to major institutional clients, from insurers, pension funds to hedge funds and private banks/family offices. He holds advanced degrees in Finance and Applied Mathematics, as well as an engineering degree from École Polytechnique Paris.
Video Description
Portfolio Management Conference 2026
Modern multi-asset portfolio construction must overcome estimation uncertainty and handle illiquid private assets alongside public assets and hedge funds. ROSAA addresses these challenges through the innovative Multi-Asset Tradable Factor (MATF) model, which provides a unified framework for risk-return estimation across diverse asset classes using macro and asset-specific factors. We present Hierarchical Clustering Group Lasso (HCGL) regularization for estimation of asset factor exposures. We apply our framework to Strategic and Tactical Asset Allocation optimization for multi-asset portfolios.
Speaker Bio
Dr. Artur Sepp
Artur Sepp is the Global Head of Investment Solutions Quant Group at LGT Bank in Zurich, where he leads a quant team and builds the quantitative investment platform for systematic asset allocation and portfolio strategies. He is committed to advancing applied finance through research, technology, and team development to deliver data-driven investment solutions that reflect LGT’s long-term perspective and pursuit of excellence for clients. Named Risk Magazine’s Quant of the Year 2024, he brings over 20 years of experience spanning both the buy-side and sell-side. He holds a PhD in Mathematical Statistics from the University of Tartu. His research spans systematic strategies, portfolio optimization, stochastic volatility modeling, machine learning, and blockchain/DeFi, with over 1,200 citations and H-index of 18. He is co-originator of the ROSAA (Robust Optimization of Strategic and Active Asset Allocation) framework and the log-normal beta stochastic volatility model. He actively contributes to the quant community through his editorial board role at The Journal of Computational Finance and by developing open-source Python libraries for quantitative finance. Outside of finance, Artur is a dedicated Brazilian Jiu-Jitsu practitioner, holding a purple belt.
Video Description
Portfolio Management Conference 2026
This talk presents a new investment risk measure designed for fully general Monte Carlo simulation paths and their associated probabilities. The new risk measure is called Conditional Maximum Loss (CML). It draws inspiration from drawdowns but adjusts for their practical shortcomings. The CML risk measure is dynamic because it focuses on the expected loss over the entire horizon between portfolio rebalancing times, not just the losses at the end of the rebalancing horizon. CML portfolio optimization problems can be solved using linear programming, similar to the popular Conditional Value-at-Risk (CVaR). In fact, we can formulate joint portfolio optimization problems where both CML and CVaR are minimized, while the portfolio’s expected return is maximized. This allows investment managers to both optimize the distribution at the end of the investment horizon as well as the portfolio’s path, making the portfolio more robust and minimizing the probability of experiencing stop losses or client withdrawals.
Speaker Bio
Anton Vorobets
Anton Vorobets is the Founder and CEO of Fortitudo Technologies, an investment tech company offering novel software solutions to institutional investors. He has experience from both the sell-side and the buy-side, starting his career working on equity derivatives strategy at Nordea Markets before transitioning to multi-asset portfolio construction at Danske Bank Asset Management. Anton’s work focuses on simulation, stress testing, and optimization methods that operate on fully general Monte Carlo paths. Early versions of this framework have been used successfully by him and his colleagues to manage complex multi-asset portfolios that include derivatives, leverage, and complex risk constraints.
