Robust Estimation of Intraweek Periodicity in Volatility and Jump Detection

Abstract:     
Opening, lunch and closing of financial markets induce a periodic component in the volatility of high-frequency returns. We show that price jumps cause a large bias in the classical periodicity estimators and propose robust alternatives. We find that accounting for periodicity greatly improves the accuracy of intraday jump detection methods. It increases the power to detect the relatively small jumps occurring at times for which volatility is periodically low and reduces the number of spurious jump detections at times of periodically high volatility. We use the series of detected jumps to estimate robustly the long memory parameter of the squared EUR/USD, GBP/USD and YEN/USD returns.

Number of Pages in PDF File: 35

Keywords: high-frequency data, jump detection, periodicity, long memory, robust statistics

JEL Classification: C13, C14, C22

Accepted Paper Series