High-Frequency Factor Models and Regressions
High-Frequency Factor Models and Regressions
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DOI：
10.2139/ssrn.3319890
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
Yacine Ait-Sahalia;I. Kalniņa;D. Xiu
中科院分区：
文献类型：
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作者：
Yacine Ait-Sahalia;I. Kalniņa;D. Xiu
我们考虑一个非参数时间序列回归模型。我们的框架允许对贝塔进行精确的估计，而不需要通常假设贝塔是分段常数。这一特性使我们的框架特别适合研究个股。我们为模型的所有组成部分提供了一个推理框架，包括特殊波动和特殊跳跃。我们的实证分析调查了高频文献中最大的数据集。首先，我们使用1996-2017年纽约证券交易所、美国证券交易所和纳斯达克股票市场的所有交易股票来构建5分钟频率下的五个Fama-French因子和动量因子。其次，我们记录了所有股票和新因素的关键经验属性，并应用具有新高频Fama-French因素的非参数时间序列回归模型。我们发现，该因子模型能够有效地解释个股风险的系统成分。此外，我们提供的证据表明，特殊跳跃与特殊事件有关，如盈利令人失望。
We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the usual assumption of betas being piecewise constant. This property makes our framework particularly suitable to study individual stocks. We provide an inference framework for all components of the model, including idiosyncratic volatility and idiosyncratic jumps. Our empirical analysis investigates the largest dataset in the high-frequency literature. First, we use all traded stocks from NYSE, AMEX, and NASDAQ stock markets for 1996–2017 to construct the five Fama–French factors and the momentum factor at the 5-minute frequency. Second, we document the key empirical properties across all the stocks and the new factors, and apply the nonparametric time series regression model with the new high-frequency Fama–French factors. We find that this factor model is effective in explaining the systematic component of the risk of individual stocks. In addition, we provide evidence that idiosyncratic jumps are related to idiosyncratic events such as earnings disappointments.