Copula function approaches for the analysis of serial and cross dependence in stock returns

Giorgia Rivieccio and Giovanni De Luca

Finance Research Letters, 2016, vol. 17, issue C, 55-61

Abstract: The description of the dynamic behavior of multiple time series represents an important point of departure to obtain accurate forecasts both in economic and financial analysis. We provide a method for the comparison of the out-of-sample performance of portfolios, respectively, ignoring and exploiting serial and cross dependence in stock returns. The serial and cross dependence is modeled using both the classical linear and easy-to-use Vector AutoRegressive and more sophisticated models making use of copula functions. After deriving the classical and copula-based VAR conditional expected returns and covariance, we construct different portfolios and compare them in terms of Sharpe ratio in an out-of-sample period.

Keywords: Copula function; Sharpe ratio; Vector AutoRegressive models (search for similar items in EconPapers)
JEL-codes: C13 C22 C53 G11 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:17:y:2016:i:c:p:55-61

DOI: 10.1016/j.frl.2016.01.006

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