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The Role of Realized Ex-post Covariance Measures and Dynamic Model Choice on the Quality of Covariance Forecasts

  • Rasmus Tangsgaard Varneskov


    (School of Economics and Management, Aarhus University and CREATES)

  • Valeri Voev


    (School of Economics and Management, Aarhus University and CREATES)

Recently, consistent measures of the ex-post covariation of financial assets based on noisy high-frequency data have been proposed. A related strand of literature focuses on dynamic models and covariance forecasting for high-frequency data based covariance measures. The aim of this paper is to investigate whether more sophisticated estimation approaches lead to more precise covariance forecasts, both in a statistical precision sense and in terms of economic value. A further issue we address, is the relative importance of the quality of the realized measure as an input in a given forecasting model vs. the model’s dynamic specification. The main finding is that the largest gains result from switching from daily to high-frequency data. Further gains are achieved if a simple sparsesampling covariance measure is replaced with a more efficient and noise-robust estimator.

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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2010-45.

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Length: 27
Date of creation: 26 Aug 2010
Date of revision:
Handle: RePEc:aah:create:2010-45
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