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Multivariate Rotated ARCH ModelsDiaa NoureldinUniversity of Oxford - Department of Economics Neil ShephardUniversity of Oxford - Oxford-Man Institute; University of Oxford - Nuffield College; University of Oxford - Oxford Financial Research Centre Kevin SheppardUniversity of Oxford - Department of Economics; University of Oxford - Oxford-Man Institute of Quantitative Finance February 16, 2012 Abstract: This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. The extension to DCC-type parameterizations is given, introducing the rotated conditional correlation (RCC) model. Inference for these models is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on some DJIA stocks.
Number of Pages in PDF File: 34 Keywords: RARCH, RCC, multivariate volatility, covariance targeting, common persistence, empirical Bayes, predictive likelihood JEL Classification: C32, C52, C58 working papers seriesDate posted: February 19, 2012Suggested CitationContact Information
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