The partition problem: case studies in Bayesian screening for time-varying model structure

TL;DR

This paper explores Bayesian methods for identifying time-varying structures in data, demonstrated through financial market contagion analysis and corporate performance streak detection.

Contribution

It introduces a Bayesian framework for the partition problem applied to real-world data sets with evolving model structures.

Findings

Time-varying graphical structures reveal contagion patterns in European markets.

Identification of firms with significant performance streaks.

Bayesian screening effectively detects structural changes over time.

Abstract

This paper presents two case studies of data sets where the main inferential goal is to characterize time-varying patterns in model structure. Both of these examples are seen to be general cases of the so-called "partition problem," where auxiliary information (in this case, time) defines a partition over sample space, and where different models hold for each element of the partition. In the first case study, we identify time-varying graphical structure in the covariance matrix of asset returns from major European equity indices from 2006--2010. This structure has important implications for quantifying the notion of financial contagion, a term often mentioned in the context of the European sovereign debt crisis of this period. In the second case study, we screen a large database of historical corporate performance in order to identify specific firms with impressively good (or bad) streaks of performance.