Why do absolute returns predict volatility so well?
Why do absolute returns predict volatility so well?
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DOI：
10.1093/jjfinec/nbl010
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发表时间：
2007-12-01
影响因子：
2.5
通讯作者：
Chysels, Eric
中科院分区：
文献类型：
--
作者：
Forsberg, Lars;Chysels, Eric
我们的目标是波动性预测，这是许多风险管理问题的核心。我们提供了理论解释：（i）至少自Taylor（1986）和Ding，格兰杰和Engle（1993）以来就认识到的经验程式化事实，即绝对收益比平方收益表现出更大的持续性;（ii）Ghysels，Santa-Clara，和Valkanov（2006）表明，在预测二次变化的未来增量方面，已实现的绝对值优于基于平方收益的波动性度量。本文从Barndorff-Nielsen和Shephard（2001）提出的连续时间随机资产收益波动模型出发，研究了直接观测和有抽样误差的各种波动相关过程的持续性和线性回归性质。我们还允许跳跃的资产回报过程，并研究其对持久性和线性回归的影响。大量的实证结果补充了理论分析。
Our objective is volatility forecasting, which is core to many risk management problems. We provide theoretical explanations for (i) the empirical stylized fact recognized at least since Taylor (1986) and Ding, Granger, and Engle (1993) that absolute returns show more persistence than squared returns and (ii) the empirical finding reported in recent work by Ghysels, Santa-Clara, and Valkanov (2006) showing that realized absolute values outperform square return-based volatility measures in predicting future increments in quadratic variation. We start from a continuous time stochastic volatility model for asset returns suggested by Barndorff-Nielsen and Shephard (2001) and study the persistence and linear regression properties of various volatility-related processes either observed directly or with sampling error. We also allow for jumps in the asset return processes and investigate their impact on persistence and linear regression. Extensive empirical results complement the theoretical analysis.