Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?
Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?
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中国燃料油期货波动率预测：GARCH型、SV型还是已实现波动率模型？
DOI：
10.1016/j.physa.2011.08.071
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发表时间：
2012-11
期刊：
影响因子：
--
通讯作者：
Wei Yu
中科院分区：
文献类型：
--
作者：
Wei Yu
在大多数以前预测石油市场波动的工作中，每日收益率的平方被用作未观察到的实际波动的代理。然而，正如Andersen和Bollerslev（1998）[22]所证明的那样，这种具有太高测量噪声的代理可能完全优于所谓的已实现波动率（RV）测量，该测量由日内收益平方的累积和计算。基于这一动机，本文在前人工作的基础上，利用日内高频数据，比较了GARCH型、随机波动率（SV）和已实现波动率模型在上海期货交易所燃料油期货日样本外波动率预测中的表现。通过将RV作为实际日波动率的代理，计算预测误差，我们发现基于日内高频数据的已实现波动率模型比基于日收益率的GARCH型和SV模型产生了更准确的波动率预测。此外，SV模型优于许多线性和非线性GARCH型模型，捕捉长记忆波动和/或波动的非对称杠杆效应。这些结果也证明了日内高频数据中蕴含着丰富的波动信息，可以用来构建更准确的石油波动预测模型。
In most previous works on forecasting oil market volatility, squared daily returns were taken as the proxy of unobserved actual volatility. However, as demonstrated by Andersen and Bollerslev (1998) [22], this proxy with too high measurement noise could be perfectly outperformed by a so-called realized volatility (RV) measure calculated by the cumulative sum of squared intraday returns. With this motivation, we further extend earlier works by employing intraday high-frequency data to compare the performance of three typical volatility models in the daily out-of-sample volatility forecasting of fuel oil futures on the Shanghai Futures Exchange (SHFE): the GARCH-type, stochastic volatility (SV) and realized volatility models. By taking RV as the proxy of actual daily volatility and then computing forecasting errors, we find that the realized volatility model based on intraday high-frequency data produces significantly more accurate volatility forecasts than the GARCH-type and SV models based on daily returns. Furthermore, the SV model outperforms many linear and nonlinear GARCH-type models that capture long-memory volatility and/or the asymmetric leverage effect in volatility. These results also prove that abundant volatility information is available in intraday high-frequency data, and can be used to construct more accurate oil volatility forecasting models.
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DOI：
10.1016/j.physa.2010.07.012
发表时间：
2010-11
影响因子：
3.3
作者：
Zongrun Wang;Xiao-hong Chen;Yanbo Jin;Yan-ju Zhou
通讯作者：
Zongrun Wang;Xiao-hong Chen;Yanbo Jin;Yan-ju Zhou
DOI：
10.2139/ssrn.499744
发表时间：
2004-01
期刊：
Econometrics eJournal
影响因子：
--
作者：
S. J. Koopman;Borus Jungbacker;Eugenie Hol Uspensky
通讯作者：
S. J. Koopman;Borus Jungbacker;Eugenie Hol Uspensky
影响因子：
12.8
作者：
Fong, WM;See, KH
通讯作者：
See, KH
影响因子：
6.1
作者：
ENGLE, RF
通讯作者：
ENGLE, RF
DOI：
10.1016/j.physa.2010.03.044
发表时间：
2010-08
影响因子：
3.3
作者：
F. Slanina
通讯作者：
F. Slanina