The Effect of Correlation in the CVaR Algorithm

Abstract

According to existing literature, the CVaR (conditional value at risk) method outperforms, therefore in this study CVaR is applied as a constraint to transform portfolio optimization problem into a linear one. Linearization of portfolio optimization plays a central role in financial studies, since linear problem allows for performing sensitivity analysis. Sensitivity analysis makes it possible to measure the variation of parameters due to the variation of one parameter in a linear problem, without solving the problem from the scratch. The objective function coefficient of mentioned method for a portfolio includes average of asset returns, which are highly correlated. Therefore, principal component analysis is employed to tackle the correlation of the functional relations. An example of stock market is employed to validate method. Finally, it is shown that the result of the presented method is closer to the ideal result.