Model Performance Between Linear Vector Autoregressive and Markov Switching Vector Autoregressive Models on Modelling Structural Change in Time Series Data

Real financial time series data always exhibit structural change, jumps or breaks. Thus, in this paper, the performance of the linear vector autoregressive model (VAR), mean adjusted Markov switching vector autoregressive model (MSM-VAR) and mean adjusted heteroskedasticity Markov switching vector autoregressive model (MSMH-VAR) are applied in order to examine the oil price return and the gold price return effect on stock market returns. The two break point tests indicate the existence of break dates in the data. In addition, a comparison among the three model's performance show that the two Markov switching vector autoregressive models with first autoregressive order able to provide the most significance, reliable and valid results as compared to linear vector autoregressive.