Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment
日内周期模式能否解释日内波动率变化？一项非参数评估AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要使用含微观结构噪声和跳跃的股票高频数据，检验日内波动率的确定性与随机成分，直接支持这两个金融分类。
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日内周期模式能否解释日内波动率变化？一项非参数评估
- 研究问题
- 含噪且可能跳跃的高频价格数据中，日内波动率变化是否完全来自确定性重复周期，还是仍存在随机波动率成分？
- 方法与思路
- 作者对收益进行季节性调整，结合预平均、截断与双幂变差构造检验，推导极限分布，并提出改善跳跃影响的自助法。
- 材料陈述的贡献
- 摘要称，估计季节因子不改变渐近理论；股票应用显示周期解释了显著部分变化，但仍有异方差，小型无限活动跳跃会扭曲检验。
- 阅读关注点
- 应核对噪声与跳跃条件、季节因子的估计、预平均及截断参数，以及自助法在无限活动跳跃下改善检验尺寸的范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核实；摘要未给出样本规模、解释比例或精确条件，不能量化剩余随机波动。
原文摘要
In this paper, we propose a nonparametric way to test the hypothesis that time-variation in intraday volatility is caused solely by a deterministic and recurrent diurnal pattern. We assume that noisy high-frequency data from a discretely sampled jump-diffusion process are available. The test is then based on asset returns, which are deflated by the seasonal component and therefore homoskedastic under the null. To construct our test statistic, we extend the concept of pre-averaged bipower variation to a general Itô semimartingale setting via a truncation device. We prove a central limit theorem for this statistic and construct a positive semi-definite estimator of the asymptotic covariance matrix. The $t$-statistic (after pre-averaging and jump-truncation) diverges in the presence of stochastic volatility and has a standard normal distribution otherwise. We show that replacing the true diurnal factor with a model-free jump- and noise-robust estimator does not affect the asymptotic theory. A Monte Carlo simulation also shows this substitution has no discernable impact in finite samples. The test is, however, distorted by small infinite-activity price jumps. To improve inference, we propose a new bootstrap approach, which leads to almost correctly sized tests of the null hypothesis. We apply the developed framework to a large cross-section of equity high-frequency data and find that the diurnal pattern accounts for a rather significant fraction of intraday variation in volatility, but important sources of heteroskedasticity remain present in the data.