CUSUM-Based Monitoring for Explosive Episodes in Financial Data in the Presence of Time-Varying Volatility

IF 1.8 3区经济学 Q2 BUSINESS, FINANCE

Journal of Financial Econometrics Pub Date : 2021-05-05 DOI:10.1093/JJFINEC/NBAB009

Sam Astill, David I. Harvey, S. Leybourne, A. Taylor, Yang Zu

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引用次数: 7

Abstract

We generalize the Homm and Breitung (2012) CUSUM-based procedure for the real-time detection of explosive autoregressive episodes in financial price data to allow for time-varying volatility. Such behavior can heavily inflate the false positive rate (FPR) of the CUSUM-based procedure to spuriously signal the presence of an explosive episode. Our modified procedure involves replacing the standard variance estimate in the CUSUM statistics with a nonparametric kernel-based spot variance estimate. We show that the sequence of modified CUSUM statistics has a joint limiting null distribution which is invariant to any time-varying volatility present in the innovations and that this delivers a real-time monitoring procedure whose theoretical FPR is controlled. Simulations show that the modification is effective in controlling the empirical FPR of the procedure, yet sacrifices only a small amount of power to detect explosive episodes, relative to the standard procedure, when the shocks are homoskedastic. An empirical illustration using Bitcoin price data is provided.

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基于cusum的时变波动金融数据爆炸事件监测

我们推广了Homm和Breitung(2012)基于cusum的程序，用于实时检测金融价格数据中的爆炸性自回归事件，以允许时变波动。这种行为会严重提高基于cusum的程序的假阳性率(FPR)，从而虚假地发出爆炸事件的信号。我们修改的程序包括用基于非参数核的点方差估计替换CUSUM统计中的标准方差估计。我们表明，修改的CUSUM统计序列具有联合限制零分布，该分布对创新中存在的任何时变波动都是不变的，并且这提供了一个实时监控过程，其理论FPR是可控的。仿真结果表明，该改进方法在控制经验FPR过程中是有效的，但相对于标准程序，当冲击为等方差时，仅牺牲少量的功率来检测爆炸事件。使用比特币价格数据提供了一个实证说明。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Financial Econometrics Multiple-

CiteScore

5.60

自引率

8.00%

发文量

期刊介绍： "The Journal of Financial Econometrics is well situated to become the premier journal in its field. It has started with an excellent first year and I expect many more."