Causal data science for financial stress testing

Gelin Gao, Bud Mishra, Daniele Ramazzotti

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)294-304
Number of pages11
JournalJournal of Computational Science
StatePublished - May 2018


  • Causality
  • Classification
  • Decision trees
  • Graphical models
  • Stress testing
  • Suppes-Bayes Causal Networks

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science
  • Modeling and Simulation


Dive into the research topics of 'Causal data science for financial stress testing'. Together they form a unique fingerprint.

Cite this