Download this Paper Open PDF in Browser

Multivariate Rotated ARCH Models

36 Pages Posted: 19 Feb 2012 Last revised: 19 Nov 2013

Diaa Noureldin

University of Oxford - Department of Economics

Neil Shephard

Harvard University

Kevin Sheppard

University of Oxford - Department of Economics; University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: November 5, 2013

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.

Keywords: RARCH; RBEKK; RDCC; multivariate volatility; covariance targeting; common persistence

JEL Classification: C32, C52, C58

Suggested Citation

Noureldin, Diaa and Shephard, Neil and Sheppard, Kevin, Multivariate Rotated ARCH Models (November 5, 2013). Available at SSRN: https://ssrn.com/abstract=2007484 or http://dx.doi.org/10.2139/ssrn.2007484

Diaa Noureldin

University of Oxford - Department of Economics ( email )

Manor Road Building
Manor Road
Oxford, OX1 3UQ
United Kingdom

Neil Shephard

Harvard University ( email )

1875 Cambridge Street
Cambridge, MA 02138
United States

Kevin Keith Sheppard (Contact Author)

University of Oxford - Department of Economics ( email )

Manor Road Building
Manor Road
Oxford, OX1 3BJ
United Kingdom

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom
+44 1865 616 613 (Phone)

HOME PAGE: http://www.oxford-man.ox.ac.uk

Paper statistics

Downloads
806
Rank
23,962
Abstract Views
3,632