Multivariate Autoregressive Denoising Diffusion Model for Value-at-Risk Evaluation

The Value-at-Risk (VaR) is a common risk measure, often required by financial regulators, typically estimated based on simple closed-form distributions. In this work, we built up on our existing GAN-based model for VaR estimation, by comparing it to newer deep learning approaches, namely an Autoregressive Denoising Diffusion Model based on the Timegrad architecture and a model based on Low-Rank Gaussian Copula Processes.

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