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Estimation and Forecasting of Dynamic Conditional Covariance: A Semiparametric Multivariate Model

Listed author(s):
  • Xiangdong Long
  • Liangjun Su
  • Aman Ullah

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.

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File URL: http://hdl.handle.net/10.1198/jbes.2009.07057
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Article provided by Taylor & Francis Journals in its journal Journal of Business & Economic Statistics.

Volume (Year): 29 (2011)
Issue (Month): 1 (January)
Pages: 109-125

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Handle: RePEc:taf:jnlbes:v:29:y:2011:i:1:p:109-125
DOI: 10.1198/jbes.2009.07057
Contact details of provider: Web page: http://www.tandfonline.com/UBES20

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