Performing Bayesian Risk Aggregation using Discrete Approximation Algorithms with Graph Factorization
This work addresses risk aggregation problems in finance and other domains, but it appears incremental as it builds on existing Bayesian methods.
The paper tackled Bayesian risk aggregation for hybrid and high-dimensional dependency models by introducing two algorithms: one for convolution problems with continuous and discrete variables, and another for general-purpose inference over Bayesian networks.
Risk aggregation is a popular method used to estimate the sum of a collection of financial assets or events, where each asset or event is modelled as a random variable. Applications, in the financial services industry, include insurance, operational risk, stress testing, and sensitivity analysis, but the problem is widely encountered in many other application domains. This thesis has contributed two algorithms to perform Bayesian risk aggregation when model exhibit hybrid dependency and high dimensional inter-dependency. The first algorithm operates on a subset of the general problem, with an emphasis on convolution problems, in the presence of continuous and discrete variables (so called hybrid models) and the second algorithm offer a universal method for general purpose inference over much wider classes of Bayesian Network models.