  Measuring risk is at the center of modern financial risk management. As the
world economy is becoming more complex and standard modeling assumptions are
violated, the advanced artificial intelligence solutions may provide the right
tools to analyze the global market. In this paper, we provide a novel approach
for measuring market risk called Encoded Value-at-Risk (Encoded VaR), which is
based on a type of artificial neural network, called Variational Auto-encoders
(VAEs). Encoded VaR is a generative model which can be used to reproduce market
scenarios from a range of historical cross-sectional stock returns, while
increasing the signal-to-noise ratio present in the financial data, and
learning the dependency structure of the market without any assumptions about
the joint distribution of stock returns. We compare Encoded VaR out-of-sample
results with eleven other methods and show that it is competitive to many other
well-known VaR algorithms presented in the literature.
