sharpe ratio analysis: a copula approach

Sharpe ratio [4, 3] is a common tool for comparing the performance of financial assets. multivariate normal distribution is the commonly used model for analyzing return of financial assets. but normal distribution restricts the dependence of returns to be linear as measured by pearson’s correlation. in recent years, copulas are often used to measure the dependence between return of financial assets. copulas separate the dependency between variables from their univariate marginal distributions. in this way, the dependence structure can be linear, nonlinear or tail dependent. despite the use of copula approach to calculate many financial indices [1], little work has been done on the use of copulas for sharp ratio estimation. in this paper, we study the effect of dependency on the sharpe ratio using the copulas and compare it with the empirical method.