Identifying Jumps in Financial Assets: A Comparison Between Nonparametric Jump Tests
通过蒙特卡洛模拟比较九种高频数据跳跃检测方法，发现ABD-LM方法在低波动时表现最佳，并提出了有限样本改进和减少虚假跳跃的扩展方法。
We perform a comprehensive Monte Carlo comparison between nine alternative procedures available in the literature to detect jumps in financial assets using high-frequency data. We evaluate size and power properties of the procedures under alternative sampling frequencies, persistence in volatility, jump size and intensity, and degree of contamination with microstructure noise. The overall best performance is shown by the Andersen, Bollerslev, and Dobrev (2007) and Lee and Mykland (2008) intraday procedures (ABD-LM), provided the price process is not very volatile. We propose two extensions to the existing battery of tests. The first regards the finite sample improvements based on simulated critical values for the ABD-LM procedure. The second regards a procedure that combines frequencies and tests able to reduce the number of spurious jumps. Finally, we report an empirical analysis using real high frequency data on five stocks listed in the New York Stock Exchange.