Sequential Entropy Pooling Heuristics

Abstract

This article introduces two sequential heuristics that are designed to overcome some of the practical limitations of the Entropy Pooling (EP) method. Both of these heuristics repeatedly apply the original EP method to sequentially arrive at the posterior probability and usually lead to significantly better solutions than a simple application of the original approach. In some cases, the sequential heuristics coincide with the original approach, while they help to automatically ensure logical consistency in others. Given the benefits of the sequential heuristics, this article argues that they should become the standard for future EP applications.

Documented Python code that replicates the results of the original approach is available in the open-source package fortitudo.tech. More information about the package can be found on https://os.fortitudo.tech.

Keywords: Entropy Pooling, relative entropy, Kullback-Leibler divergence, change of measure, market views, stress-tests, Monte Carlo simulation, nonlinear convex optimization, heuristic algorithms, Python Programming Language

JEL Classification: C11, C61, G1