The description of thedynamic behavior of multiple time series represents an important point of departure to obtain accurate forecasts both in economic and financial analysis. The aim of this work isthe comparison of methods whichexploit serial dependence in stock returns to improve out-of-sample portfolio performance.For multivariate time series, the popular and easy-to-use Vector AutoRegressive (VAR) model is compared to some copula models which allow for a non-linear and/or asymmetric dependencestructure among the variables.After deriving theVAR-based and copula-based conditional expected returns, we construct different portfolios and compare them in terms of Sharpe ratio.

A Copula-VAR approach for the analysis of serial dependence in stock returns

DE LUCA, GIOVANNI;RIVIECCIO, GIORGIA
2014-01-01

Abstract

The description of thedynamic behavior of multiple time series represents an important point of departure to obtain accurate forecasts both in economic and financial analysis. The aim of this work isthe comparison of methods whichexploit serial dependence in stock returns to improve out-of-sample portfolio performance.For multivariate time series, the popular and easy-to-use Vector AutoRegressive (VAR) model is compared to some copula models which allow for a non-linear and/or asymmetric dependencestructure among the variables.After deriving theVAR-based and copula-based conditional expected returns, we construct different portfolios and compare them in terms of Sharpe ratio.
2014
978-84-937822-4-5
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/30703
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact