Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model Variables with Econometric Applications
Xiangdong Long (),
Liangjun Su () and
Aman Ullah
Additional contact information
Xiangdong Long: Judge Business School, University of Cambridge
No 200908, Working Papers from University of California at Riverside, Department of Economics
Abstract:
We propose a semiparametric conditional covariance (SCC) estimator that combines the ï¬ rst-stage parametric conditional covariance (PCC) estimator with the second-stage nonparametric correction estimator in a multiplicative way. We prove the asymptotic normality of our SCC estimator, propose a nonparametric test for the correct speciï¬ cation of PCC models, and study its asymptotic properties. We evaluate the ï¬ nite sample performance of our test and SCC estimator and compare the latter with that of PCC estimator, purely nonparametric estimator, and Hafner, Dijk, and Franses’s (2006) estimator in terms of mean squared error and Value-at-Risk losses via simulations and real data analyses.
Keywords: Conditional Covariance Matrix; Multivariate GARCH; Portfolio; Semiparametric Estimator; Speciï¬ cation Test. (search for similar items in EconPapers)
JEL-codes: C3 C5 G0 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2009-07, Revised 2009-07
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://economics.ucr.edu/repec/ucr/wpaper/09-08.pdf First version, 2009 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ucr:wpaper:200908
Access Statistics for this paper
More papers in Working Papers from University of California at Riverside, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Kelvin Mac ().