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Building models for high dimensional portfolios is important in risk management and asset allocation. Here the authors propose a novel and fast way of estimating models of time-varying covariance's that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets. Indeed they 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.
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