Enhancing Risk Assessment with Generative Models

Abstract

Conditional Value-at-Risk (CVaR) is one of the most popular risk measures in finance, used in risk management as a complementary measure to Value-at-Risk (VaR). VaR estimates potential losses within a given confidence level, such as 95% or 99%, but does not account for tail risks. CVaR addresses this gap by calculating the expected losses exceeding the VaR threshold, providing a more comprehensive risk assessment for extreme events. This research explores the application of Denoising Diffusion Probabilistic Models (DDPM) to enhance CVaR calculations. Traditional CVaR methods often fail to capture tail events accurately, whereas DDPMs generate a wider range of market scenarios, improving the estimation of extreme risks. However, these models require significant computational resources and may present interpretability challenges.

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