Using GANs to Estimate Value-at-Risk for Market Risk Management

We will explore the use of Generative Adversarial Networks (GANs) for market risk management: Estimation of portfolio risk measures such as Value-at-Risk (VaR). Generative Adversarial Networks (GAN) allow us to implicitly maximize the likelihood of complex distributions thereby allowing us to generate samples from such distributions — the key point here is the implicit maximum likelihood estimation principle whereby we do not specify what this complex distribution is parameterized as. Dealing with high dimensional data potentially coming from a complex distribution is a key aspect to market risk management among many other financial services use cases. GANs will allow us to deal with potentially complex financial services data such that we do not have to explicitly specify a distribution such as a multidimensional Gaussian distribution.

Hamaad Shah , Vice President - Lead Data Scientist at Deutsche Bank

Hamaad is a principal data scientist with expertise and experience in machine learning and quantitative analytics applied to banking and insurance. He has extensive expertise in deep learning and Bayesian inference, among other areas of machine learning, applied to various financial services use cases such as Asset Liability Management (ALM) and actuarial pricing, among other financial services use cases. Hamaad holds an MSc in Applicable Mathematics from the London School of Economics and Political Science (LSE) and a BSc in Economics from the University of Manchester.

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