Rama Cont (Oxford): Dynamic hedging under model uncertainty

Please join us for a seminar of the Advanced Financial Technologies Laboratory (AFTLab):

Time: Thursday, December 4, 2025, 5:00pm-6:00pm

Location: Huang 305

Speaker: Rama Cont (Oxford)

Title: Dynamic hedging under model uncertainty

Abstract: It is commonly assumed that a detailed and accurate description of the joint dynamics of risk factors and asset prices is a pre-requisite for the design of hedging and risk management strategies for derivatives portfolios. This has motivated the development of increasingly complex stochastic models with many risk factors and parameters, which are challenging to estimate and implement, but whose benefit in terms of risk management is not clear. We revisit the dynamic hedging problem as a feedback control problem under model uncertainty. We argue that the widely used approach of continuously recalibrating an auxiliary pricing model is an effective way of treating model uncertainty and link this approach with Model Predictive Control (MPC) and reinforcement learning (RL). We show that any auxiliary pricing model capable of calibrating the cross section of market prices of hedging instruments and satisfying an identifiability condition may be used in this way to compute adequate robust dynamic hedging strategies without explicit knowledge of market dynamics. This procedure captures exposures to latent risk factors through parameter recalibration and leads to a self-financing strategy with predictable loss. We provide explicit formulas for hedge ratios in dynamically recalibrated models, and provide a detailed decomposition of the gain of the dynamically hedged position. We argue that this approach is an effective recipe for dynamic hedging under model uncertainty and pleads in favor of computationally flexible models with good inversion properties. Our results provide insights for model design and clarify  the role of parameter calibration  in risk management.

Bio: Rama Cont is the Statutory Professor of Mathematical Finance at the University of Oxford. He is known for contributions to probability theory, stochastic analysis and mathematical modelling in finance, in particular for his work on pathwise methods in stochastic analysis. In quantitative finance he is known in particular for his work on models based on jump processes, the stochastic modelling of limit order books as queueing systems machine learning methods in finance and the mathematical modelling of systemic risk. He was awarded the Louis Bachelier Prize by the French Academy of Sciences for his work on mathematical modelling of financial markets. He was elected Fellow of the Society for Industrial and Applied Mathematics (SIAM) for "contributions to stochastic analysis and mathematical finance". He received the Award for Excellence in Interdisciplinary Research (APEX) from the Royal Society for his research on mathematical modelling of systemic risk. He was editor in chief of the Encyclopedia of Quantitative Finance. Cont has served as advisor to central banks and international organizations such as the International Monetary Fund and the Bank for International Settlements on stress testing and systemic risk monitoring. His work on network models, financial stability and central clearing has influenced central banks and regulators. Cont obtained his doctoral degree from Ecole Polytechnique. He held academic positions at Ecole Polytechnique, and Columbia University. He was chair of mathematical finance at Imperial College London before he was elected Statutory Professor in Mathematical Finance at the Oxford Mathematical Institute and professorial fellow of St Hugh's College, Oxford.