In this study we compare a set of Markov Regime-Switching GARCH models in terms of their ability to forecast the Tehran stock market volatility at different time intervals. SW-GARCH models have been used to avoid the excessive persistence that usually found in GARCH models. In SW-GARCH models all parameters are allowed to switch between a low or high volatility regimes. Both Gaussian and fat-tailed conditional distributions are assumed for the residuals, and the degrees of freedom can also be state-dependent to capture possible time-varying kurtosis. Using stationary bootstrap and re-sampling, the forecasting performances of the competing models are evaluated by statistical loss functions. The empirical analysis demonstrates that SW-GARCH models outperform all standard GARCH models in forecasting volatility. Also, the SW-GARCH model with the t distribution for errors has the best performance in fitting a model and estimation.
nazifi naeini, M., fatahi, S., & samadi, S. (2012). Estimating and forecasting the volatility of Tehran stock market, using Markov regime switching GARCH models. Economic Modeling Research, 3(9), 117-141.
MLA
nazifi naeini, M., fatahi, S., & samadi, S. "Estimating and forecasting the volatility of Tehran stock market, using Markov regime switching GARCH models", Economic Modeling Research, 3, 9, 2012, 117-141.
HARVARD
nazifi naeini M., fatahi S., samadi S. (2012). 'Estimating and forecasting the volatility of Tehran stock market, using Markov regime switching GARCH models', Economic Modeling Research, 3(9), pp. 117-141.
CHICAGO
M. nazifi naeini, S. fatahi & S. samadi, "Estimating and forecasting the volatility of Tehran stock market, using Markov regime switching GARCH models," Economic Modeling Research, 3 9 (2012): 117-141,
VANCOUVER
nazifi naeini M., fatahi S., samadi S. Estimating and forecasting the volatility of Tehran stock market, using Markov regime switching GARCH models. Economic Modeling Research. 2012;3(9):117-141 (In Persian).
Economic Modeling Research
