کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7378411 1480128 2016 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting Tehran stock exchange volatility; Markov switching GARCH approach
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات فیزیک ریاضی
پیش نمایش صفحه اول مقاله
Forecasting Tehran stock exchange volatility; Markov switching GARCH approach
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 445, 1 March 2016, Pages 264-282
نویسندگان
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