Bipower-type estimation in a noisy diffusion setting

By Mark Podolskij, Mathias Vetter. 2008

119 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/64ca33d8e4987e158faeef10bd2b0c4176f11765

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Citations in the corpus

Large-dimensional factor modeling based on high-frequency observations (2019, unknown): “the arbitrage pricing theory of Ross (1976) are based on factor models.”
Limit theorems for moving averages of discretized processes plus noise (2010, background): “The asymptotic theory for the functionals V (Z, g, p, 0)t in the absence of jumps is (partially) derived in [11] and [14], but here we extend these results to the case of general semimartingales.”
Preaveraging-Based Estimation of Quadratic Variation in the Presence of Noise and Jumps: Theory, Implementation, and Empirical Evidence (2013, unknown): “(2.6) The preaveraging method as originally introduced by Podolskij and Vetter (2009a) is based on certain local moving averages that reduce the influence of the noise process U.”
Systemic Co-Jumps (2017, unknown): “In principle, the theory 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…”
The information content of high-frequency data for estimating equity return models and forecasting risk (2010, unknown): “…at higher frequencies one can resort to noise-reduction techniques such as pre-averaging, introduced in the context of multipower variations by Podolskij and Vetter (2009).15 Quite similarly to the way we obtained equation (9) above, it is possible to single out also the jump part of…”
On the Estimation of Integrated Volatility With Jumps and Microstructure Noise (2014, methodology): “All the coefficients were selected from Podolskij and Vetter (2009a) to allow a comparison.”
Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment (2017, unknown): “About the noise distribution, we follow Podolskij and Vetter (2009a): (i) ϵ is distributed symmetrically around zero, and (ii) for any 0 > a > − 1, Assumption (A’) : Cramer’s condition is fulfilled, that is lim sup t →∞ | χ ( t ) | < 1, where χ denotes the characteristic function of ϵ .”
The Estimation of Leverage Effect With High-Frequency Data (2014, unknown): “The preaveraging method (Jacod et al. 2009; Podolskij and Vetter 2009a; Mykland and Zhang 2011a) provides a plausible way to solve the problem with microstructure.”
Fact or friction: jumps at ultra high frequency (2014, unknown): “…r ∗ i,K and r ∗ i + K,K are asymptotically uncorrelated and (b) conditionally on F t i , r ∗ i,K is asymptotically distributed according to As in Podolskij and Vetter (2009a), we conclude that Applying the mean value theorem t j − t j − 1 ∼ 1 N f ′ ( j/N ) and using the continuity of f ′ and σ…”
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes (2015, unknown): “The sixth class of estimators are pre-averaged realized variances (RVpa) estimators first introduced by Podolskij and Vetter (2009a) and further studied in Jacod et al. (2009).”