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Fitting and testing vast dimensional time-varying covariance models
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When
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2.00pm on Friday 29th February
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Where
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B617, Leverhulme Library, Columbia House
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Presentations
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Speaker
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Neil Shephard
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From
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Oxford-Man Institute, Oxford University
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Abstract
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Building models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel way of estimating models of time-varying covariances that overcome some of the computational problems which have troubled existing methods when applied to hundreds or even thousands of assets. 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.
Joint with Robert F. Engle and Kevin Sheppard.
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For further information
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Postgraduate Administrator Ext. 6879
Department of Statistics, Columbia House
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