Multivariate rotated ARCH models

Diaa Noureldin, Neil Shephard and Kevin Sheppard

Scholarly Articles from Harvard University Department of Economics

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 Öt 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.

Date: 2014
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Citations: View citations in EconPapers (32)

Published in Journal of Econometrics

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Related works:
Journal Article: Multivariate rotated ARCH models (2014)
Working Paper: Multivariate Rotated ARCH Models (2012)
Working Paper: Multivariate Rotated ARCH models (2012)
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