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Forecasting the return distribution using high-frequency volatility measures

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  • Hua, Jian
  • Manzan, Sebastiano
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    Abstract

    The aim of this paper is to forecast (out-of-sample) the distribution of financial returns based on realized volatility measures constructed from high-frequency returns. We adopt a semi-parametric model for the distribution by assuming that the return quantiles depend on the realized measures and evaluate the distribution, quantile and interval forecasts of the quantile model in comparison to a benchmark GARCH model. The results suggest that the model outperforms an asymmetric GARCH specification when applied to the S&P 500 futures returns, in particular on the right tail of the distribution. However, the model provides similar accuracy to a GARCH (1,1) model when the 30-year Treasury bond futures return is considered.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Banking & Finance.

    Volume (Year): 37 (2013)
    Issue (Month): 11 ()
    Pages: 4381-4403

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    Handle: RePEc:eee:jbfina:v:37:y:2013:i:11:p:4381-4403

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    Web page: http://www.elsevier.com/locate/jbf

    Related research

    Keywords: Realized volatility; Quantile regression; Density forecast; Value-at-risk;

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    Cited by:
    1. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2014. "Interpreting Financial Market Crashes as Earthquakes: A New early Warning System for Medium Term Crashes," Tinbergen Institute Discussion Papers 14-067/III, Tinbergen Institute.

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