Encoded Value-at-Risk: A Predictive Machine for Financial Risk Management
This addresses risk management for financial markets, but it is incremental as it builds on existing VaR methods with a new neural network approach.
The paper tackles financial risk measurement by introducing Encoded Value-at-Risk, a method using Variational Auto-encoders to model market scenarios without distributional assumptions, and shows it is competitive with eleven other VaR algorithms in out-of-sample tests.
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.