Volatility Measurement with Pockets of Extreme Return Persistence
Volatility Measurement with Pockets of Extreme Return Persistence
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DOI：
10.2139/ssrn.3694403
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
T. Andersen;Yingying Li;V. Todorov;Bo Zhou
中科院分区：
文献类型：
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作者：
T. Andersen;Yingying Li;V. Todorov;Bo Zhou
摘要越来越多的证据表明，具有极端回报持久性的口袋偶尔会出现。这一概念指的是非平凡持续期的盘中时段，其股票回报是高度正相关的。这类事件包括但不限于漂移分量的逐渐跳跃和长时间的爆发。在这篇文章中，我们发展了一类集成的波动率估计量，称为差回报波动率(DV)估计量，它对这些类型的Ito半鞅违规提供了稳健性。具体地说，我们证明，通过使用连续高频回报的差异，我们的DV估计器可以减少所有常用估计器在这种明显的短期日内回报可预测期内表现出的非平凡偏差。蒙特卡罗研究证明了新开发的波动率估计器在有限样本下的可靠性。在我们对S指数期货和个股的经验波动性预测应用中，我们的基于DV的异质自回归(HAR)模型相对于标准的样本外MSE和QLIKE标准的现有方法表现得很好。
Abstract Increasing evidence points towards the episodic emergence of pockets with extreme return persistence. This notion refers to intraday periods of non-trivial duration, for which stock returns are highly positively autocorrelated. Such episodes include, but are not limited to, gradual jumps and prolonged bursts in the drift component. In this paper, we develop a family of integrated volatility estimators, labeled differenced-return volatility ( DV ) estimators, which provide robustness to these types of Ito semimartingale violations. Specifically, we show that, by using differences in consecutive high-frequency returns, our DV estimators can reduce the non-trivial bias that all commonly-used estimators exhibit during such periods of apparent short-term intraday return predictability. A Monte Carlo study demonstrates the reliability of the newly developed volatility estimators in finite samples. In our empirical volatility forecasting application to S&P 500 index futures and individual equities, our DV -based Heterogeneous Autoregressive (HAR) model performs well relative to existing procedures according to standard out-of-sample MSE and QLIKE criteria.