Multivariate rotated ARCH models |
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| Authors: | Diaa Noureldin Neil Shephard Kevin Sheppard |
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| Affiliation: | 1. Department of Economics, American University in Cairo, AUC Avenue, New Cairo 11835, Egypt;2. Department of Economics, Harvard University, Littauer Center, Cambridge, MA 02138, USA;3. Department of Statistics, Harvard University, Science Center, Cambridge, MA 02138, USA;4. Department of Economics, University of Oxford, Manor Road, Oxford OX1 3UQ, UK;5. Oxford-Man Institute, Eagle House, Walton Well Road, Oxford OX2 6ED, UK |
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| 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. This yields the rotated BEKK (RBEKK) model. The extension to DCC-type parameterizations is given, introducing the rotated DCC (RDCC) model. Inference for these models is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on the DJIA stocks. |
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| Keywords: | C32 C52 C58 |
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