Tail-GAN: Nonparametric Scenario Generation for Tail Risk Estimation

45 Pages Posted: 16 Mar 2022

See all articles by Rama Cont

Rama Cont

University of Oxford

Mihai Cucuringu

University of Oxford - Department of Statistics

Chao Zhang

University of Oxford - Department of Statistics

Renyuan Xu

University of Southern California - Epstein Department of Industrial & Systems Engineering

Date Written: March 3, 2022

Abstract

The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios. Commonly used parametric models have been successful in applications involving a small number of assets, but may not be scalable to large or heterogeneous portfolios involving multiple asset classes.

We propose a novel data-driven approach for the simulation of realistic multi-asset scenarios with a particular focus on the accurate estimation of tail risk for a given class of static and dynamic portfolios selected by the user. By exploiting the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES), we design a Generative Adversarial Network (GAN) architecture capable of learning to simulate price scenarios that preserve tail risk features for these benchmark trading strategies, leading to consistent estimators for their Value-at-Risk and Expected Shortfall. We demonstrate the accuracy and scalability of our method via extensive simulation experiments using synthetic and market data. Our results show that, in contrast to other data-driven scenario generators, our proposed scenario simulation method correctly captures tail risk for both static and dynamic portfolios.

Keywords: Scenario Simulation, Generative Adversarial Network (GAN), Time Series, Expected Shortfall, Value at Risk, Dynamic Strategies.

Suggested Citation

Cont, Rama and Cucuringu, Mihai and Zhang, Chao and Xu, Renyuan, Tail-GAN: Nonparametric Scenario Generation for Tail Risk Estimation (March 3, 2022). Available at SSRN: https://ssrn.com/abstract=3812973 or http://dx.doi.org/10.2139/ssrn.3812973

Rama Cont (Contact Author)

University of Oxford ( email )

Mathematical Institute
Oxford, OX2 6GG
United Kingdom

HOME PAGE: http://https://www.maths.ox.ac.uk/people/rama.cont

Mihai Cucuringu

University of Oxford - Department of Statistics ( email )

24-29 St Giles
Oxford
United Kingdom

Chao Zhang

University of Oxford - Department of Statistics ( email )

24-29 St Giles
Oxford
United Kingdom

Renyuan Xu

University of Southern California - Epstein Department of Industrial & Systems Engineering ( email )

United States

HOME PAGE: http://renyuanxu.github.io

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