Rama Cont Professor of Mathematics Professor of Mathematics, Chair of Mathematical Finance, University of Oxford

Rama Cont is Professor of Mathematics at the University of Oxford, where he holds the Chair of Mathematical Finance. He is an internationally recognized expert in mathematical modeling in finance and quantitative risk management, in particular the quantitative modeling of extreme market risks: discontinuities in market behavior, liquidity risk, endogenous risk, stress testing and systemic risk. He has more than 100 publications on a broad range of topics including valuation and risk management of derivatives, stress testing, liquidity risk, bank regulation, market microstructure and applications of machine learning and generative AI in finance.

Prof. Cont has extensive consulting experience with regulators, financial institutions and exchanges, in particular in the area of stress testing and risk management of central counterparties (CCPs). He has participated as an expert in many stress testing exercises of banks and market infrastructures across Europe, Asia, the US and Latin America. He has served as a consultant to the Basel Committee on Banking reform, the European Central Bank, the New York Federal Reserve, the US Commodity Futures Commission (CFTC), the US Office of Financial Research, the International Monetary Fund (IMF), DTCC, LCH, B3 (the Brazilian securities clearinghouse), CME, Norges Bank and the Hong Kong Exchange, on matters related to stress testing and the design of margin and risk management systems.

He was awarded the Louis Bachelier Prize by the French Academy of Sciences in 2010 for his research on mathematical modeling in finance and the Royal Society Award for Excellence in Interdisciplinary Research in 2017 for his research on systemic risk.

• Doctor of Philosophy (Mathematical Finance)
  Ecole Normale Superieure

• Professor of Mathematics (University Of Oxford)
  UK
• Head of (Oxford Mathematical and Computational Finance Group)
  UK
• Professor (Columbia University)
  USA
• Senior Consultant (US Exchanges)
  USA
• Senior Consultant (Basel Committee)
  Switzerland
• Senior Consultant (European Central Bank)
  EU
• Senior Consultant (New York Federal Reserve Bank)
  USA
• Senior Consultant in Stress Testing and Risk Management (US Commodity Futures Commission, CFTC)
  USA
• Senior Consultant in Stress Testing and Risk Management (International Monetary Fund, IMF)
  USA
• Senior Consultant in Stress Testing and Risk Management (Depository Trust & Clearing Corporation, DTCC)
  USA
• Senior Consultant in Stress Testing and Risk Management ( London Clearing House, LCH)
  UK
• Senior Consultant in Stress Testing and Risk Management (Brazilian securities clearinghouse, B3)
  Brazil
• Senior Consultant in Stress Testing, Risk Management and Margin design (CME)
  USA
• Senior Consultant in Stress Testing and Risk Management (Hong Kong Exchange)
  Hong Kong

Tail-GAN: Learning to Simulate Tail Risk Scenarios

The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components. We propose a novel data-driven approach for simulating realistic, high-dimensional multi-asset scenarios, focusing on accurately representing tail risk for a class of static and dynamic trading strategies. We exploit the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES) to design a Generative Adversarial Network (GAN) that learns to simulate price scenarios preserving these tail risk features. We demonstrate the performance of our algorithm on synthetic and market data sets through detailed numerical experiments.

Academic Rama Cont Sep 2024 Machine Learning 211

Asymptotic Analysis of Deep Residual Networks

Residual networks, or ResNets, are multilayer neural network architectures in which a skip connection is introduced at every layer. This allows very deep networks to be trained by circumventing vanishing and exploding gradients, mentioned in [3]. The increased depth in ResNets has lead to commensurate performance gains in applications ranging from speech recognition to computer vision.

Academic Rama Cont Jul 2024 Machine Learning 167

Central Clearing and Risk Transformation

The clearing of over-the-counter transactions through central counterparties (CCPs), one of the pillars of financial reform following the crisis of 2007-2008, has promoted CCPs as key elements of the new global financial architecture. Given the cost of implementing central clearing mandates and the associated collateral requirements, it is important to examine how these reforms have affected risks in the financial system and whether central clearing has attained the initial objective of the reform, which is to enhance financial stability and reduce systemic risk. We show that, rather than eliminating counterparty risk, central clearing transforms it into liquidity risk: margin calls transform accounting losses into realised losses which affect the liquidity buffers of clearing members. Accordingly, initial margin and default fund calculations should account for this liquidity risk in a realistic manner, especially for large positions. While recent discussions have centered on the solvency of CCPs, their capital and ‘skin-in-the-game’ and capital requirements for CCP exposures of banks, we argue that these issues are secondary and that the main focus of risk management and financial stability analysis should be on the liquidity of clearing members and the liquidity resources of CCPs. Clearing members should assess their exposure to CCPs in terms of liquidity, rather than counterparty risk. Stress tests involving CCPs should focus on liquidity stress testing and adequacy of liquidity resources.

Academic Rama Cont Jul 2024 Central Clearing 158

Liquidity at Risk: Joint Stress Testing of Solvency and Liquidity

The traditional approach to the stress testing of financial institutions focuses on capital adequacy and solvency. Liquidity stress tests have been applied in parallel to and independently from solvency stress tests, based on scenarios which may not be consistent with those used in solvency stress tests. We propose a structural framework for the joint stress testing of solvency and liquidity: our approach exploits the mechanisms underlying the solvency-liquidity nexus to derive relations between solvency shocks and liquidity shocks. These relations are then used to model liquidity and solvency risk in a coherent framework, involving external shocks to solvency and endogenous liquidity shocks arising from these solvency shocks. We define the concept of “Liquidity at Risk”, which quantifies the liquidity resources required for a financial institution facing a stress scenario. Finally, we show that the interaction of liquidity and solvency may lead to the amplification of equity losses due to funding costs which arise from liquidity needs.

Academic Rama Cont Jul 2024 Stress Testing 212

Fast and Slow Optimal Trading with Exogenous Information

We are able to explicitly compute the equilibrium strategies, in two steps. We first derive the optimal strategy of the high-frequency trader given any strategy adopted by the investor. Then, we solve the problem of the investor given the optimal strategy of the high-frequency trader, in terms of the resolvent of a Fredholm integral equation. Our results show that the high-frequency trader adopts a predatory strategy whenever the value of the trading signal is high, and follows a cooperative strategy otherwise. We also show that there is a net gain in performance for the investor from taking into account the order flow of the high-frequency trader. A U-shaped intraday pattern in trading volume is shown to arise endogenously as a result of the strategic behavior of the agents.

Academic Rama Cont Jun 2024 Trading 194