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Multivariate Rotated ARCH models

Diaa Noureldin, Neil Shephard () and Kevin Sheppard

No 594, Economics Series Working Papers from University of Oxford, 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 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 mdoels is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on some SJIA stocks.

Keywords: RARCH; RCC; Multivariate volatility; Covariance targeting; Common persistence; Empirical Bayes; Predictive likelihood (search for similar items in EconPapers)
JEL-codes: C32 C52 C58 (search for similar items in EconPapers)
Date: 2012-02-16
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-fmk
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Citations: View citations in EconPapers (5) Track citations by RSS feed

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