Estimation and forecasting of dynamic conditional covariance: A semiparametric multivariate model

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 nonpa...

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Bibliographic Details
Main Authors: LONG, Xiangdong, SU, Liangjun, ULLAH, Aman
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2011
Subjects:
Conditional Covariance Matrix
Multivariate GARCH
Portfolio
Semiparametric Estimator
Specification Test.
Econometrics
Multivariate Analysis
Online Access:https://ink.library.smu.edu.sg/soe_research/1332
https://ink.library.smu.edu.sg/context/soe_research/article/2331/viewcontent/Estimation_and_Forecasting_of_Dynamic_Conditional_Covariance_A_Semiparametric_Multivariate_Model.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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