Estimating Conditional Value at Risk (CVaR) with consideration the robust of the measure based on robust Cipra method

Since the atmosphere of financial markets is uncertain and ambiguous, Conditional Value at Risk measurement has been of great importance in recent years for financial companies and micro and macro investors. In this paper, we estimate the CVaR of the Tehran Stock Exchange Index for distribution of the Trial Student at confidence levels of 95%  and 99%  based on the Cipra method, which is proposed as a new approach for the estimation of the CVaR. In order to evaluate the performance of this approach, the comparison between the said approach and the conventional methods of GARCH, EGARCH and TGARCH was performed using four backtesting of unconditional coverage  test, conditional coverage  test, joint test and Lopez loss function test. The results show that robust Cipra method has a better and more reliable performance than the other methods in estimating the CVaR.