Modeling volatility and conditional VaR measure using GARCH models and theoretical EVT in Tehran Stock Exchange
Subject Areas : Journal of Investment KnowledgeSaeed Fallahpoor 1 , Reza Raee 2 , Saeed Mirzamohammadi 3 , seyed mohammad hasheminejad 4
1 - professor of Tehran University, financial management Ph.D
2 - professor of Tehran University, financial management Ph.D
3 - Assistant professor of Iran University of Science and Technology, Economic Ph.D
4 - Ph.D student of financial management at University of Tehran kish, International Campus,
Keywords: Extreme Value Theory, function Lopez losses, long-term memory, FIGARCH,
Abstract :
Trying to identify an appropriate model to enhance measurement accuracy by using value at risk measures is of particular importance. Conditional Value at Risk (CVaR) with having some of the shortcomings of VaR, is a more reliable measure. In this study, the characteristics of the Tehran Stock Exchange index data usage FIGARCH-EVT model to calculate value at risk if states have been more accurate. GARCH-EVT hybrid implementation model and its development, FIGARCH-EVT model, we found that the effect of clustering, dynamic and long-term memory has been included in the modeling. FIGARCH model for log data output index, which will be modeled in terms of the above properties. In addition, the wide trail property index return data using extreme value theory (EVT) is used for residual FIGARCH model. To compare the results, NORMAL-GARCH models and t-Student-GARCH, historical simulation and GARCH-EVT indicator is used for data output. The results of the model using retrospective tests were evaluated. The results of this study indicate that the data distribution is skewed and asymmetrical index returns do not follow a normal distribution. The tests Standardized Exceedance Residuals and The Cumulative Violation Process and Expected shortfall backtesting and loss function Lopez FIGARCH-EVT model over other models is more accurate.
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