Volume 3, Issue 9 (12-2012)                   jemr 2012, 3(9): 117-141 | Back to browse issues page

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nazifi naeini M, fatahi S, samadi S. Estimating and forecasting the volatility of Tehran stock market, using Markov regime switching GARCH models. jemr. 2012; 3 (9) :117-141
URL: http://jemr.khu.ac.ir/article-1-344-en.html
Abstract:   (10825 Views)

  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.

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Type of Study: بنیادی | Subject: پولی و مالی
Received: 2011/11/25 | Accepted: 2013/02/24 | Published: 2013/02/24

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