Nonparametric correlation models for portfolio allocation
Nektarios Aslanidis and
Isabel Casas ()
Journal of Banking & Finance, 2013, vol. 37, issue 7, 2268-2283
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.
Keywords: Semiparametric conditional; Correlation model; Nonparametric correlations; DCC; Local linear estimator; Portfolio evaluation; Carry trade (search for similar items in EconPapers)
JEL-codes: C14 C53 G10 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:37:y:2013:i:7:p:2268-2283
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