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On the Economic Evaluation of Volatility Forecasts

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  • Valeri Voev

    ()
    (Aarhus University and CREATES)

Abstract

We analyze the applicability of economic criteria for volatility forecast evaluation based on unconditional measures of portfolio performance. The main theoretical finding is that such unconditional measures generally fail to rank conditional forecasts correctly due to the presence of a bias term driven by the variability of the conditional mean and portfolio weights. Simulations and a small empirical study suggest that the bias can be empirically substantial and lead to distortions in forecast evaluation. An important implication is that forecasting superiority of models using high frequency data is likely to be understated if unconditional criteria are used.

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File URL: ftp://ftp.econ.au.dk/creates/rp/09/rp09_56.pdf
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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2009-56.

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Length: 22
Date of creation: 24 Nov 2009
Date of revision:
Handle: RePEc:aah:create:2009-56

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Forecast evaluation; Volatility forecasting; Portfolio optimization; Mean-variance analysis;

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References

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  14. Kim Christensen & Silja Kinnebrock & Mark Podolskij, 2009. "Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data," CREATES Research Papers, School of Economics and Management, University of Aarhus 2009-45, School of Economics and Management, University of Aarhus.
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  19. Ingmar Nolte & Valeri Voev, 2007. "Estimating High-Frequency Based (Co-) Variances: A Unified Approach," CoFE Discussion Paper, Center of Finance and Econometrics, University of Konstanz 07-07, Center of Finance and Econometrics, University of Konstanz.
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  22. Roxana Chiriac & Valeri Voev, 2008. "Modelling and Forecasting Multivariate Realized Volatility," CoFE Discussion Paper, Center of Finance and Econometrics, University of Konstanz 08-06, Center of Finance and Econometrics, University of Konstanz.
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Citations

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Cited by:
  1. Massimiliano Caporin & Michael McAleer, 2011. "Ranking Multivariate GARCH Models by Problem Dimension: An Empirical Evaluation," Working Papers in Economics, University of Canterbury, Department of Economics and Finance 11/23, University of Canterbury, Department of Economics and Finance.
  2. Rombouts, Jeroen & Stentoft, Lars & Violante, Franceso, 2014. "The value of multivariate model sophistication: An application to pricing Dow Jones Industrial Average options," International Journal of Forecasting, Elsevier, Elsevier, vol. 30(1), pages 78-98.
  3. Massimiliano Caporin & Michael McAleer, 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," Working Papers in Economics, University of Canterbury, Department of Economics and Finance 12/06, University of Canterbury, Department of Economics and Finance.
  4. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2013. "Do High-Frequency Data Improve High-Dimensional Portfolio Allocations?," SFB 649 Discussion Papers, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany SFB649DP2013-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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