Preaveraging-Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence

87 citations recorded by the source corpus.

How researchers see this paper

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

Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment (2017, unknown): “Hautsch and Podolskij (2013, Lemma 2) discuss an estimator of ρ ( k ) , k = 0 , . . . , q + 1 , which is obtained from a simple recursion formula.”
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2015, unknown): “Following the empirical work of Christensen et al. (2014) and Hautsch and Podolskij (2013), we use a kernel bandwidth K = dθ √ ne, where θ = 1 and n is the number of sampled intraday returns.”
Systemic Co-Jumps (2017, methodology): “…could be extended to include market microstructure noise by pre-averaging returns before applying smoothed estimators, as in Podolskij and Vetter (2009) and Hautsch and Podolskij (2013), or using spectral methods along the lines of Mancino and Sanfelici (2011) and Bibinger and Winkelmann (2015).”
Estimating the Spot Covariation of Asset Prices—Statistical Theory and Empirical Evidence (2014, background): “The moving-average-type dependence structure in the noise process in part (iii) of Assumption 3 follows, for example, Hautsch and Podolskij (2013), implying the long-run variance (4).”