Fitting vast dimensional time-varying covariance models

Robert F. Engle, Neil Shephard () and Kevin Sheppard ()

No 403, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed we can handle the case where the cross-sectional dimension is larger than the time series one. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method. The out of sample hedging performance of various models estimated using this method are compared.

Keywords: ARCH Models; Composite Likelihood; Dynamic Conditional Correlations; Incidental Parameters; Quasi-Likelihood; Time-Varying Covariances (search for similar items in EconPapers)
JEL-codes: C14 C32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-ore and nep-rmg
Date: 2008
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Working Paper: Fitting vast dimensional time-varying covariance models (2008)
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