Is the Diurnal Pattern Sufficient to Explain Intraday Variation In Volatility? A Nonparametric Assessment

2016-08-01

Journal of Econometrics

AI Summary

This study investigates whether intraday volatility dynamics are entirely driven by deterministic diurnal patterns. To this end, the authors propose a nonparametric approach that extends pre-averaged bipower variation to high-frequency data featuring jumps and market microstructure noise within a general Itô semimartingale framework, yielding a robust estimator of the diurnal scaling factor. A test statistic is constructed based on seasonally adjusted returns, and an improved bootstrap procedure is introduced to enhance finite-sample inference. Empirical results show that while the diurnal pattern accounts for a substantial portion of intraday volatility, significant residual heteroskedasticity remains, indicating the presence of additional time-varying sources of volatility beyond the deterministic seasonal component.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Other Foundations of Machine Learning
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Problem

• intraday volatility
• diurnal pattern
• stochastic volatility
• heteroskedasticity
• high-frequency data

Innovation

• nonparametric test
• pre-averaged bipower variation
• diurnal pattern
• stochastic volatility
• bootstrap inference