Tail-GAN for Simulating Multi-Asset Tail Risk Scenarios

Summary

Tail-GAN is a data-driven scenario generator designed to simulate realistic joint price dynamics for high-dimensional multi-asset portfolios, with emphasis on losses in the tail of the distribution. It uses the joint elicitability of Value-at-Risk and Expected Shortfall to train a generative adversarial network to preserve these risk features. The intended use includes evaluating both static and dynamic trading strategies under simulated market scenarios.

The paper reports numerical experiments on synthetic and market data. It says the approach captures tail risk for a broad class of strategies and generalizes beyond the training examples. Principal component analysis applied to input data is also reported to improve scalability for large-dimensional time series. The supplied description does not state the assets, evaluation metrics, or comparative results in detail, so the claims cannot be independently assessed from this summary. Scenario quality remains tied to the representativeness of the input data and does not ensure that future tail events will match simulated ones.

Key ideas

• Tail-GAN generates joint scenarios for multi-asset price dynamics with a focus on portfolio tail losses.
• The model uses the joint elicitability of Value-at-Risk and Expected Shortfall as training targets.
• The framework is intended to assess static and dynamic trading strategies.
• Experiments on synthetic and market data report tail-risk capture and generalization across strategies.
• Principal component analysis is used to improve scalability to large-dimensional time series.

Tags

# Tail-GAN: Learning to Simulate Tail Risk Scenarios


# 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. In contrast to previously proposed data-driven scenario generators, our proposed method correctly captures tail risk for a broad class of trading strategies and demonstrates strong generalization capabilities. In addition, combining our method with principal component analysis of the input data enhances its scalability to large-dimensional multi-asset time series, setting our framework apart from the univariate settings commonly considered in the literature.

Shown in full with attribution under the source's licence. Licence: abstract CC0

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