Presenting of nonlinear hybrid model based on Extreme Value Theory for forecasting the Conditional Value at Risk (CVaR)

The political and economic instability in recent years and followed by rapid changes in the realm of financial markets, has increased the risk of most financial institutions. So that risk managers at these institutions are worried about the decline in their asset value over the coming days. In recent studies, generally the Conditional Value at Risk is used to measure and forecast the risks existing in financial markets. Therefore, in this research, it has been attempted to introduce, calculate and implement a nonlinear hybrid model for forecasting the Conditional Value at Risk. For this purpose, the new hybrid model based on the Extreme Value Theory and the Holt-Winters exponential smoothing (HWES-EVT) that, in addition to dynamics, cluster characteristics and broad data sequence, also takes into account the forecast Conditional Value at Risk of the industry and Tehran Stock Exchange Indices. For evaluating the accuracy the performance of proposed hybrid model, this modek is compared with the GARCH-EVT model. The results of backtesting show that the proposed hybrid approach provides a more accurate answer to the forecasting of Conditional Value at Risk for these indicators Indices.