Application Extreme Value Theory and long-Memory to Stock Market in Iran (In Framework Model-GARCH)‎

During last decades, financial markets have witnessed large losses due to their exposure to unexpected market crash. Resulting in these financial disasters, financial institutions, regulators and academics have developed intensive research to provide better measurement techniques and hedging tools. Value-at-Risk (VaR) is the most popular risk measure in the financial industry. In this paper Application Extreme Value Theory and long-memory to Stock Market in Iran (In Framework Model-GARCH) was Checked. We use same long-range memory GARCH-type models (FIAGARCH, HYGARCH and FIAPARCH) and EVT to forecast the financial market risk. Findings Indicated that The FIAPARCH-EVT approach performs better in predicting the one day ahead VaRs for different series studied.