Investigating the Impact of Time-varying Volatility of Macroeconomic Indices on the Predictability of Optimal Stock Portfolio Return in Tehran Stock Exchange

In this study, 3 models of Time-Varying Parameters (TVP), Dynamic Model Selecting (DMS) and Dynamic Model Averaging (DMA) and their comparison via the Ordinary Least Squares (OLS) method in MATLAB in the time period 2003-2013 (monthly) are discussed. In the present study the variables of unofficial exchange rate changes, interest rate changes and inflation oil price forecast returns for stocks in Tehran Stock Exchange are used. The study concludes that dynamic models with time-varying parameters are more accurate in predicting returns in the Stock Exchange, in a way that the MAFE and MSFE models, DMA, DMS which have complete dynamics are more efficient than other models. As a consequence, it can be said that the variability of the coefficients of the variables in the TVP model cannot lead to higher accuracy in predicting returns in the Stock Exchange, and it is required that the dynamics of time-varying variables of the model used to predict stock returns be taken into consideration.