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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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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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