Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2011
Abstract
We propose a semiparametric conditional covariance (SCC) estimator that combines the first-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 specification of PCC models, and study its asymptotic properties. We evaluate the finite 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, Specification Test.
Discipline
Econometrics | Multivariate Analysis
Research Areas
Econometrics
Publication
Journal of Business and Economic Statistics
Volume
29
Issue
1
First Page
109
Last Page
125
ISSN
0735-0015
Identifier
10.1198/jbes.2009.07057
Publisher
Taylor and Francis
Citation
LONG, Xiangdong; SU, Liangjun; and ULLAH, Aman.
Estimation and forecasting of dynamic conditional covariance: A semiparametric multivariate model. (2011). Journal of Business and Economic Statistics. 29, (1), 109-125.
Available at: https://ink.library.smu.edu.sg/soe_research/1332
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1198/jbes.2009.07057