Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model
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 the 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
Conditional covariance matrix; Multivariate GARCH; Portfolio; Semiparametric estimator; Specification test
Applied Statistics | Econometrics
Journal of Business and Economic Statistics
Taylor and Francis
Long, X.; SU, Liangjun; and Ullah, A..
Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model. (2010). Journal of Business and Economic Statistics. 29, (1), 109-125. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1277
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