Forecasting Tehran stock exchange volatility; Markov switching GARCH approach

Esmaiel Abounoori, Elmi, Zahra (Mila) and Younes Nademi

Physica A: Statistical Mechanics and its Applications, 2016, vol. 445, issue C, 264-282

Abstract: This paper evaluates several GARCH models regarding their ability to forecast volatility in Tehran Stock Exchange (TSE). These include GARCH models with both Gaussian and fat-tailed residual conditional distribution, concerning their ability to describe and forecast volatility from 1-day to 22-day horizon. Results indicate that AR(2)-MRSGARCH-GED model outperforms other models at one-day horizon. Also, the AR(2)-MRSGARCH-GED as well as AR(2)-MRSGARCH-t models outperform other models at 5-day horizon. In 10 day horizon, three models of AR(2)-MRSGARCH outperform other models. Concerning 22 day forecast horizon, results indicate no differences between MRSGARCH models with that of standard GARCH models. Regarding Risk management out-of-sample evaluation (95% VaR), a few models seem to provide reasonable and accurate VaR estimates at 1-day horizon, with a coverage rate close to the nominal level. According to the risk management loss functions, there is not a uniformly most accurate model.

Keywords: Markov switching GARCH; Volatility; Forecast; Tehran stock exchange (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:445:y:2016:i:c:p:264-282

DOI: 10.1016/j.physa.2015.10.024

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