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Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model Variables with Econometric Applications

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Author Info
Xiangdong Long () (Judge Business School, University of Cambridge)
Liangjun Su () (School of Economics, Singapore Management University)
Aman Ullah () (Department of Economics, University of California Riverside)

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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.

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Publisher Info
Paper provided by University of California at Riverside, Department of Economics in its series Working Papers with number 200908.

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Length: 30 pages
Date of creation: Jul 2009
Date of revision: Jul 2009
Handle: RePEc:ucr:wpaper:200908

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Related research
Keywords: Conditional Covariance Matrix; Multivariate GARCH; Portfolio; Semiparametric Estimator; Speciï¬cation Test.;

Find related papers by JEL classification:
C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
C5 - Mathematical and Quantitative Methods - - Econometric Modeling
G0 - Financial Economics - - General

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This page was last updated on 2009-11-19.


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