Testing for persistence in US mutual funds’ performance: a Bayesian dynamic panel model

Abstract

We provide a Bayesian panel model to consider persistence in US funds’ performance while we tackle the important problem of errors in variables. Our modelling departs from prior strong assumptions such as error terms across funds being independent. In fact, we provide a novel, general Bayesian model for (dynamic) panel data that is stable across different priors as reported from the mapping of the prior to the posterior of the Bayesian baseline model with the adoption of different priors. We demonstrate that our model detects previously undocumented striking variability in terms of performance and persistence across funds categories and over time, and in particular through the financial crisis. The reported stochastic volatility exhibits a rising trend as early as 2003–2004 and could act as an early warning of future crisis.