.png){kind=icon}
Return
Nonparametric correlation models for portfolio allocation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jbankfin.2013.01.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 MP 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. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Semiparametric conditional
Correlation model
Nonparametric correlations
DCC
Local linear estimator
Portfolio evaluation
Carry trade
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3.8
Papers:
6.4K
Citations:
2.4W
Organization
Cited Papers
ShareSaveShareSaveShareSaveSynthesis of a Surface Photovoltage Sensor Using Self‐Ordered Tin‐Modified MCM‐41 Films: Enhanced NO2 Gas Sensing.png)
ChemPhysChem
ChemPhysChemIF0
PREAI
ShareSave
View more