Robust estimation of intraweek periodicity in volatility and jump detection

By Kris Boudt, Christophe Croux, Sabéastien Laurent. 2011

182 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/4684a1562691413565e4629ee6cff6dad01db68b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Outlyingness weighted covariation (2011, unknown): “We follow Boudt et al. (2011) by computing the outlyingness statistic on the returns divided by their jump robust weighted standard deviation periodicity estimate.”
Estimating spot volatility with high-frequency financial data (2014, methodology): “Intraday variation of volatility was previously studied mostly with parametric approaches (e.g. Andersen and Bollerslev (1998), Taylor and Xu (1997), Boudt et al. (2011) and Bos et al. (2012)), where it is usually assumed that there exist a periodic part in the volatility process and one needs to…”
Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment (2017, unknown): “It extends previous work of, e.g., Andersen and Bollerslev (1997); Andersen, Dobrev, and Schaumburg (2012); Boudt, Croux, and Laurent (2011); Taylor and Xu (1997); Todorov and Tauchen (2012) to the noisy setting (see also Hecq, Laurent, and Palm, 2012).”
Time-Varying Periodicity in Intraday Volatility (2019, background): “The (constant) intraday periodicity is further explicitly modeled or accounted for in papers estimating volatility models and detecting jumps, such as [9], [12], [19] and [30].”
Identifying Jumps in Financial Assets: A Comparison Between Nonparametric Jump Tests (2011, background): “Boudt et al. (2009) try to solve this issue by proposing parametric and nonparametric estimators of the periodicity factor that are robust to the presence of jumps.”
Robust Forecasting of Dynamic Conditional Correlation GARCH Models (2013, methodology): “…with jumps processes, i.e., the realized covariance (RCov) of Andersen, Bollerslev, Diebold, and Labys (2003), the realized bipower covariation (RBPCov) of Barndorff-Nielsen and Shephard (2004) and the realized outlyingness weighted covariation (ROWCov) of Boudt, Croux, and Laurent (2011a).”