Regime Switching Vector Autoregressions: a Bayesian Markov Chain Monte Carl0 Approach

Many financial time series processes appear subject to periodic structural changes in their dynamics. Regression relationships are often not robust to outliers nor stable over time, whilst the existence of changes in variance over time is well documented. This paper considers a vector autoregression subject to pseudocyclical structural changes. The parameters of a vector autoregression are modelled as the outcome of an unobserved discrete Markov process with unknown transition probabilities. The unobserved regimes, one for each time point, together with the regime transition probabilities, are to be determined in addition to the vector autoregression parameters within each regime.