Threshold bipower variation and the impact of jumps on volatility forecasting
Threshold bipower variation and the impact of jumps on volatility forecasting
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DOI：
10.1016/j.jeconom.2010.07.008
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发表时间：
2010-12-01
影响因子：
6.3
通讯作者：
Reno, Roberto
中科院分区：
文献类型：
--
作者：
Corsi, Fulvio;Pirino, Davide;Reno, Roberto
本研究重新考虑跳跃的波动性预测的作用，表明跳跃有一个积极的和最显着的影响，未来的波动性。这个结果变得明显，一旦波动率被分离成连续和不连续的分量，使用的估计，这不仅是一致的，但也几乎不受小样本偏差。为了实现这一目标，我们引入了阈值双幂变化的概念，这是基于双幂变化和阈值估计的联合使用。我们表明，它的推广（阈值多幂变差）承认一个可行的中心极限定理在跳跃的存在下，并提供较少的偏差估计，相对于标准的多幂变差，在有限样本的连续二次变差。我们还提供了一个新的测试跳跃检测，它有更多的权力比测试的基础上多功率变化。实证分析（对标准普尔500指数，个股和美国债券收益率）表明，所提出的技术，特别是在发生跳跃后的时期，显著提高波动率预测的准确性。(C)2010 Elsevier B.V.保留所有权利。
This study reconsiders the role of jumps for volatility forecasting by showing that jumps have a positive and mostly significant impact on future volatility. This result becomes apparent once volatility is separated into its continuous and discontinuous components using estimators which are not only consistent, but also scarcely plagued by small sample bias. With the aim of achieving this, we introduce the concept of threshold bipower variation, which is based on the joint use of bipower variation and threshold estimation. We show that its generalization (threshold multipower variation) admits a feasible central limit theorem in the presence of jumps and provides less biased estimates, with respect to the standard multipower variation, of the continuous quadratic variation in finite samples. We further provide a new test for jump detection which has substantially more power than tests based on multipower variation. Empirical analysis (on the S&P500 index, individual stocks and US bond yields) shows that the proposed techniques improve significantly the accuracy of volatility forecasts especially in periods following the occurrence of a jump. (C) 2010 Elsevier B.V. All rights reserved.