Forecasting the return distribution using high-frequency volatility measures

被引:17
|
作者
Hua, Jian [1 ]
Manzan, Sebastiano [1 ]
机构
[1] CUNY Bernard M Baruch Coll, Dept Econ & Finance, Zicklin Sch Business, New York, NY 10010 USA
关键词
Realized volatility; Quantile regression; Density forecast; Value-at-risk; AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY; DENSITY FORECASTS; REALIZED VARIANCE; MODEL; RISK; INFORMATION; PROVIDE; PRICES;
D O I
10.1016/j.jbankfin.2013.08.002
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:4381 / 4403
页数:23
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