Instability in regime switching models

In this paper, we look at the instability of a self-exciting regime-switching autoregressive model, specifically regime-switching models that are locally stable in each of their regimes. It turns out that the local stability of each regime is insufficient to ensure the overall stability of the model. The instability’s mechanism is described, and a sufficient condition for the instability is provided.

Application of Markov Regime Switching Autoregressive Model to Gold Prices in Pakistan

The goal of this study is to investigate the performance of the Markov regime switching autoregressive (MRS-AR) model to estimate and forecast the gold prices in Pakistan. Initial analysis of the data covering from January 1995 to January 2019 reveals the existence of nonstationarity, heteroscedasticity, and structural changes. The dynamics of the data are studied in two distinct regimes. The empirical analysis provides evidence that the regime shifts are mattered and MRS-AR model is found to be suitable even in the case of nonstationarity. Moreover, it is worthwhile to note that the Markov regime switching successfully captures the nonlinearities and heteroscedasticity underlying the selected data and provides efficient forecasts. Based on empirical evidence it is recommended that the applications of regime switching models should be promoted in other fields of life.