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Fitting vast dimensional time-varying covariance models

  • Neil Shephard
  • Kevin Sheppard
  • Robert F. Engle

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.

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File URL: http://www.economics.ox.ac.uk/materials/working_papers/paper403.pdf
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 403.

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Date of creation: 01 Sep 2008
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Handle: RePEc:oxf:wpaper:403
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