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Nonparametric correlation models for portfolio allocation

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  • Aslanidis, Nektarios
  • Casas, Isabel

Abstract

This article proposes time-varying nonparametric and semiparametric estimators of the conditional cross-correlation matrix in the context of portfolio allocation. Simulations results show that the nonparametric and semiparametric models are best in DGPs with substantial variability or structural breaks in correlations. Only when correlations are constant does the parametric DCC model deliver the best outcome. The methodologies are illustrated by evaluating two interesting portfolios. The first portfolio consists of the equity sector SPDRs and the S&P 500, while the second one contains major currencies. Results show the nonparametric model generally dominates the others when evaluating in-sample. However, the semiparametric model is best for out-of-sample analysis.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Banking & Finance.

Volume (Year): 37 (2013)
Issue (Month): 7 ()
Pages: 2268-2283

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Handle: RePEc:eee:jbfina:v:37:y:2013:i:7:p:2268-2283

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Web page: http://www.elsevier.com/locate/jbf

Related research

Keywords: Semiparametric conditional; Correlation model; Nonparametric correlations; DCC; Local linear estimator; Portfolio evaluation; Carry trade;

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