Application of Extreme Value Theory in Value at Risk forecasting

Quantifying the uncertainty is one of the most important subject in financial issues, so nowadays in each financial and investment activity risk assessment and management is required. Value-at-risk (VaR) has become a popular risk measure since it was adopted by the International agencies in 1988. Precise prediction of VaR provides proper evaluation criteria in areas such as investment decision-making and risk management. Due to the fat-tailed distribution in most real financial time-series, extreme value theory (EVT) is a powerful tool in determining the VaR by concentrating on the shape of the fat-tailed probability distribution. In This study, Peak Over Threshold (POT) approach used for value at risk forecasting by Tehran Stock Exchange (TSE) data. The results show this approach is better than traditional approaches such as historical simulation and variance-covariance methods.