Estimating Covariation: Epps Effect, Microstructure Noise

By Lan Zhang. 2011

328 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/d7923ce6d05a82355f900e15db236d55e8940d07

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

Estimating the Quadratic Covariation Matrix from Noisy Observations: Local Method of Moments and Efficiency (2013, methodology): “It is well known how to estimate noise variances with faster √ nl-rates; see, for example, Zhang et al. [25].”
Estimation of the lead-lag parameter from non-synchronous data (2013, methodology): “Our estimator is robust to non-synchronous data and does not require any pre-processing contrary to the previous tick algorithm; see, for example, [27].”
FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA (2013, methodology): “We may relax the i.i.d. assumption to correlated noises such as the case considered in Ait-Sahalia, Mykland, and Zhang (2011) and still obtain the same convergence rates as in Section 4.”
REALIZED VOLATILITY WHEN SAMPLING TIMES ARE POSSIBLY ENDOGENOUS (2013, background): “See also Christensen, Podolskij, and Vetter (2011) and Zhang (2011) for the case with microstructure noise.”
A Fourier transform method for nonparametric estimation of multivariate volatility (2009, background): “Reference [42] provides an analytical study of the realized co-volatility estimator in a general framework which includes asynchronous trading; recently [20] have proposed an estimator of integrated co-volatility which is consistent under asynchronous observations.”
Incorporating Global Industrial Classification Standard Into Portfolio Allocation: A Simple Factor-Based Large Covariance Matrix Estimator With High-Frequency Data (2016, unknown): “Notable references include: Aı̈t-Sahalia, Fan, and Xiu (2010), Christensen, Kinnebrock, and Podolskij (2010), BarndorffNielsen et al. (2011), Zhang (2011), Shephard and Xiu (2012), and Bibinger et al. (2014).”
The Leverage Effect Puzzle: Disentangling Sources of Bias at High Frequency (2011, methodology): “…a modification of the realized covariance which corrects for this effect; see also Large (2007), Griffin and Oomen (2008), Voev and Lunde (2007), Zhang (2011), Barndorff-Nielsen et al. (2008b), Kinnebrock and Podolskij (2008) and Aı̈t-Sahalia et al. (2010). volatility estimator is constructed…”
Estimating the Spot Covariation of Asset Prices—Statistical Theory and Empirical Evidence (2014, methodology): “…by Ait-Sahalia, Fan, and Xiu (2010), the multivariate realized kernel estimator by Barndorff-Nielsen et al. (2011), the multivariate preaveraging estimator by Christensen, Podolskij, and Vetter (2013), the two-scale estimator by Zhang (2011), and the LMM estimator by Bibinger et al. (2014).”
Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, methodology): “Various methods are available, including Hayashi and Yoshida (2005), Aı̈t-Sahalia et al. (2010), Christensen et al. (2010), Barndorff-Nielsen et al. (2011), Zhang (2011), Shephard and Xiu (2012) and Bibinger et al. (2014).”
VOLATILITY AND COVARIATION ESTIMATION WHEN MICROSTRUCTURE NOISE AND TRADING TIMES ARE ENDOGENOUS (2012, background): “The additional presence of microstructure noise is considered in Barndorff-Nielsen et al. (2009b), Voev and Lunde (2007), and Zhang (2006b), in additive settings where the two preceding questions are still in force.”
Knowing factors or factor loadings, or neither? Evaluating estimators of large covariance matrices with noisy and asynchronous data (2019, unknown): “By comparison, the alternative previous-tick subsampling scheme by Zhang (2011) discards more data in order to avoid artificial zero returns.”