Causal Data Science for Financial Stress Testing
This addresses the need for more accurate and efficient stress testing in finance, though it appears incremental as it builds on existing causal and machine learning methods.
The paper tackles the problem of inadequate conventional risk management strategies like VaR by proposing a novel approach for financial stress testing using Suppes-Bayes Causal Networks (SBCNs) combined with machine learning, resulting in higher accuracy and lower computational complexity compared to Monte Carlo Simulations.
The most recent financial upheavals have cast doubt on the adequacy of some of the conventional quantitative risk management strategies, such as VaR (Value at Risk), in many common situations. Consequently, there has been an increasing need for verisimilar financial stress testings, namely simulating and analyzing financial portfolios in extreme, albeit rare scenarios. Unlike conventional risk management which exploits statistical correlations among financial instruments, here we focus our analysis on the notion of probabilistic causation, which is embodied by Suppes-Bayes Causal Networks (SBCNs); SBCNs are probabilistic graphical models that have many attractive features in terms of more accurate causal analysis for generating financial stress scenarios. In this paper, we present a novel approach for conducting stress testing of financial portfolios based on SBCNs in combination with classical machine learning classification tools. The resulting method is shown to be capable of correctly discovering the causal relationships among financial factors that affect the portfolios and thus, simulating stress testing scenarios with a higher accuracy and lower computational complexity than conventional Monte Carlo Simulations.