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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 Department of Economics and Business Economics, Aarhus University 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
Contact details of provider: Web page: http://www.econ.au.dk/afn/

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  1. Ingmar Nolte & Valeri Voev, 2008. "Estimating High-Frequency Based (Co-) Variances: A Unified Approach," CREATES Research Papers 2008-31, Department of Economics and Business Economics, Aarhus University.
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  10. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
  11. Valeri Voev & Asger Lunde, 2007. "Integrated Covariance Estimation using High-frequency Data in the Presence of Noise," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 5(1), pages 68-104.
  12. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Forecasting Realized (Co)Variances with a Bloc Structure Wishart Autoregressive Model," Working Papers on Finance 1211, University of St. Gallen, School of Finance.
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  15. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-62, July.
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