Estimation of the Value of Risky Stocks (Using Conditional Copila-Garch Method)

The traditional approaches for estimating VAR assume that the joint distribution is well-known and the most commonly used normality of the joint distribution of the assets return. In reality, the financial asset return distribution has fatter tails than normal distributions. On the other hand, the use of linear correlation to model the dependence structure shows many disadvantages. Therefore, the problem raised from normality could lead to an inadequate VaR estimate. In order to overcome these problems, this paper resorts to the copula theory which allows the joint distribution of the portfolio to be free from any normality and linear correlation. Combining copula and the forecast function of the GARCH model, this paper proposes a new method, called conditional copula-GARCH, to compute the VaR of portfolios. Examined data in this study includes daily price of selected portfolio, composed of 17 equities for 1082 days in Tehran stock exchange. Presented model compared with traditional methods (including the historical simulation method & variance_covariance method). the results show that conditional copula-GARCH model captures the VaR more successfully at 95% confidence¬.