Causal and Predictive Market Views and Stress-Testing

Abstract

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 in order 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 of the results of the case study is available in the open-source package fortitudo.tech.

Keywords: Bayesian networks, minimum relative entropy, Entropy Pooling, market views, stress-testing, causality, predictiveness, Monte Carlo simulation, synthetic market generator

JEL Classification: C02, C11, C58, C61, C63, D53, E27, E44, E58, G1, G11, G12, G13,