Causal and Predictive Market Views and Stress-Testing

This article introduces a very flexible framework for causal and predictive market views and stress-testing. The framework elegantly combines Bayesian networks (BNs) and Entropy Pooling (EP). In the new framework, BNs are used to generate a finite set of joint causal views / stress-tests for the relevant factors of a market, while EP is used to project each of these views / stress-tests over market simulations. To tie it all together, the joint view / stress-test probabilities from BNs are naturally used as weights for the associated EP probability vectors to compute a single posterior probability distribution. The new framework allows us to implement market views and perform stress-tests conditional on realizations of relevant market variables in a truly causal and predictive way.Documented Python code that replicates some results of the case study is available in the open-source package fortitudo.tech. More information about this package can be found on https://os.fortitudo.tech