Forecasting the return distribution using high-frequency volatility measures

Jian Hua and Sebastiano Manzan

Journal of Banking & Finance, 2013, vol. 37, issue 11, 4381-4403

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.

Keywords: Realized volatility; Quantile regression; Density forecast; Value-at-risk (search for similar items in EconPapers)
JEL-codes: C14 C22 C53 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:37:y:2013:i:11:p:4381-4403

DOI: 10.1016/j.jbankfin.2013.08.002

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