一种非参数检验法用于检测日内波动率相关过程
A nonparametric test for diurnal variation in spot correlation processes
AI总结：
本文提出一种非参数检验法，用于检测日内波动对现货相关过程的影响，并通过实证分析证明其有效性。
摘要：
日间范围内，交易所交易股票的对数价格变动之间的关联性，通过高频数据估计的现货相关性来衡量，表现出显著的上升趋势且几乎是分段线性的关系。早晨的平均正相关性明显低于下午。我们开发了一种非参数检验程序，以检测相关过程中的这种变化。检验统计量在原假设下有已知的分布，而在备择假设下会发散。我们进行蒙特卡洛模拟以发现检验统计量的有限样本性质，这些性质接近大样本预测，即使在小样本和现实的日内波动水平下也是如此。在一项应用中，我们在覆盖股票市场长时间段的高频数据集上实施了该检验。检验在大多数情况下拒绝原假设。这表明相关过程中的日内波动是一个非平凡的影响。我们展示了宏观经济新闻和公司盈利公告的条件信息如何影响日内相关曲线。
英文摘要：
The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upward-sloping and almost piecewise linear relationship at the intraday horizon. There is notably lower-on average less positive-correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such variation in a correlation process. The test statistic has a known distribution under the null hypothesis, whereas it diverges under the alternative. We run a Monte Carlo simulation to discover the finite sample properties of the test statistic, which are close to the large sample predictions, even for small sample sizes and realistic levels of diurnal variation. In an application, we implement the test on a high-frequency dataset covering the stock market over an extended period. The test leads to rejection of the null most of the time. This suggests diurnal variation in the correlation process is a nontrivial effect in practice. We show how conditioning information about macroeconomic news and corporate earnings announcements affect the intraday correlation curve.
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