  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.
