Realized Volatility Forecasting of Agricultural Commodity Futures Using Long Memory and Regime Switching
Realized Volatility Forecasting of Agricultural Commodity Futures Using Long Memory and Regime Switching
复制标题
利用长记忆和机制切换实现农产品期货波动率预测
DOI：
10.1002/for.2443
复制
发表时间：
2016
影响因子：
3.4
通讯作者：
LANGNAN CHEN
中科院分区：
文献类型：
--
作者：
FENGPING TIAN;KE YANG;LANGNAN CHEN
我们利用中国市场的高频数据研究了五种农产品期货的实际波动率的动态特性，发现实际波动率同时表现出长记忆和政权转换。为了同时捕获这些属性，我们利用马尔可夫切换自回归分数积分移动平均（MS-ARFIMA）模型，通过将长记忆过程与状态切换组件相结合来预测已实现的波动性，并将其预测性能与不同范围内的竞争模型进行比较。全样本估计结果表明，农产品期货已实现波动率的动态特征具有两个层次的长记忆：一是与低波动率区间相关，二是与高波动率区间相关，且保持在低波动率区间的概率高于高波动率区间。样本外波动率预测结果表明，长记忆与切换机制的结合提高了已实现波动率预测的性能，并且所提出的模型代表了优于竞争模型的样本外已实现波动率预测。版权所有 © 2016 约翰·威利父子有限公司
We investigate the dynamic properties of the realized volatility of five agricultural commodity futures by employing the high-frequency data from Chinese markets and find that the realized volatility exhibits both long memory and regime switching. To capture these properties simultaneously, we utilize a Markov switching autoregressive fractionally integrated moving average (MS-ARFIMA) model to forecast the realized volatility by combining the long memory process with regime switching component, and compare its forecast performances with the competing models at various horizons. The full-sample estimation results show that the dynamics of the realized volatility of agricultural commodity futures are characterized by two levels of long memory: one associated with the low-volatility regime and the other with the high-volatility regime, and the probability to stay in the low-volatility regime is higher than that in the high-volatility regime. The out-of-sample volatility forecast results show that the combination of long memory with switching regimes improves the performance of realized volatility forecast, and the proposed model represents a superior out-of-sample realized volatility forecast to the competing models. Copyright © 2016 John Wiley & Sons, Ltd.
登录
查看更多内容
影响因子：
2.5
作者：
Choi, Kyongwook;Yu, Wei-Choun;Zivot, Eric
通讯作者：
Zivot, Eric
DOI：
10.1016/j.csda.2010.03.005
发表时间：
2010-11
期刊：
Comput. Stat. Data Anal.
影响因子：
--
作者：
T. Lux;Leonardo Morales-Arias
通讯作者：
T. Lux;Leonardo Morales-Arias
影响因子：
3
作者：
R. Baillie;G. Kapetanios
通讯作者：
R. Baillie;G. Kapetanios
DOI：
10.2139/ssrn.267792
发表时间：
2001-01
期刊：
Capital Markets: Asset Pricing & Valuation eJournal
影响因子：
--
作者：
T. Andersen;T. Bollerslev;F. Diebold;Paul Labys
通讯作者：
T. Andersen;T. Bollerslev;F. Diebold;Paul Labys
DOI：
10.1111/j.0092-5853.2004.00589.x
发表时间：
2004-05
期刊：
Derivatives eJournal
影响因子：
--
作者：
H. Jin;Darren L. Frechette
通讯作者：
H. Jin;Darren L. Frechette