Forecasting the oil futures price volatility: Large jumps and small jumps
Forecasting the oil futures price volatility: Large jumps and small jumps
复制标题
预测石油期货价格波动：大跳和小跳
DOI：
10.1016/j.eneco.2018.04.023
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发表时间：
2018-05-01
期刊：
影响因子：
12.8
通讯作者：
Zhang, Yaojie
中科院分区：
文献类型：
--
作者：
Liu, Jing;Ma, Feng;Zhang, Yaojie
宏观消息推动价格上涨，然而，价格上涨似乎并没有提高石油期货市场简单异质自回归已实现波动率模型（HAR-RV）的可预测性。本文提供了一种新的见解，并试图研究与使用 HAR-RV 模型及其各种跳跃扩展实现的预测能力相比，截断跳跃是否有助于提高预测能力。我们的结果提供了强有力的证据，表明包含大跳跃和小跳跃的模型获得了显着优越的预测能力。具体来说，在高频模型中包含小跳跃可以显着提高 I 天预测范围内的预测精度，而同时包含大跳跃和小跳跃可以在周和月范围内实现更高的预测精度。这些发现表明，考虑具有一定阈值的分解跳跃可以提高相应模型的预测精度。 (C) 2018 Elsevier B.V. 保留所有权利。
Macro news drives jumps, however, a jump does not seem to improve the predictability of the simple heterogeneous autoregressive realized volatility model (HAR-RV) in the oil futures market. This paper provides a new insight and seeks to investigate whether truncated jumps can help improve the forecasting ability compared to that achieved using the HAR-RV model and its various extensions with jumps. Our results provide strong evidence that the models incorporating both large and small jumps gain a significantly superior forecasting ability. Specifically, including small jumps in a high-frequency model significantly improves the forecast accuracy at the I-day forecasting horizon, while including both large and small jumps can achieve a higher forecast accuracy at the weekly and monthly horizons. These findings reveal that considering the decomposed jumps with a certain threshold can increase the forecast accuracy of the corresponding model. (C) 2018 Elsevier B.V. All rights reserved.