On the Correlation Structure of Microstructure Noise: A Financial Economic Approach

Diebold, Francis X.; Strasser, Georg

doi:10.3386/w16469

Cited by 22 publications

(30 citation statements)

References 33 publications

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“…Barndorff-Nielsen et al 2011a, Section 6.1.2) and are consistent with the findings inUbukata & Oya (2009), Aït-Sahalia et al (2011),Diebold & Strasser (2012),Ikeda (2013), andVarneskov (2013). All simulations are performed with 1000 replications.…”

supporting

confidence: 83%

“…Assumption 2-3, which share features with (Ikeda 2011, Assumption 5) and (Barndorff-Nielsen, Hansen, Lunde & Shephard 2011a, Assumption U), requires a detailed discussion. The additive noise consists of two orthogonal components, an endogenous and an exogenous, which captures MMS features such as asymmetric information and strategic learning, Glosten & Milgrom (1985) and Diebold & Strasser (2012), and bid-ask bounce effects (Roll 1984), among others, and taken together may describe the gradual jump model of (Barndorff-Nielsen, Hansen, Lunde & Shephard 2009, p. C25). See also Hasbrouck (2007) for a textbook treatment of the MMS literature.…”

Section: The Noise Processmentioning

confidence: 99%

“…Hansen & Lunde (2006) show that this assumption is not too damaging if sampling occurs around every minute (or every 15 ticks). However, Diebold & Strasser (2012), in a comprehensive econometric analysis of theoretical MMS models, show that a general noise model, allowing for both exogenous and endogenous noise components with polynomially decaying autocovariances, is needed to avoid concerns about the underlying MMS mechanisms. These conjectures are supported by the empirical findings of Hansen & Lunde (2006), Kalnina & Linton (2008), Ubukata & Oya (2009), Aït-Sahalia, Mykland & Zhang (2011), Kalnina (2011), Ikeda (2013), and Varneskov (2013) when sampling beyond the one-minute mark, thus leaving room for desirable extensions of existing estimation methods to utilize all available observations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating the Quadratic Variation Spectrum of Noisy Asset Prices Using Generalized Flat-Top Realized Kernels

Varneskov

2016

Econom. Theory

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This paper analyzes a generalized class of flat-top realized kernel estimators for the quadratic variation spectrum, that is, the decomposition of quadratic variation into integrated variance and jump variation. The underlying log-price process is contaminated by additive noise, which consists of two orthogonal components to accommodateα-mixing dependent exogenous noise and an asymptotically non-degenerate endogenous correlation structure. In the absence of jumps, the class of estimators is shown to be consistent, asymptotically unbiased, and mixed Gaussian at the optimal rate of convergence,n1/4. Exact bounds on lower-order terms are obtained, and these are used to propose a selection rule for the flat-top shrinkage. Bounds on the optimal bandwidth for noise models of varying complexity are also provided. In theoretical and numerical comparisons with alternative estimators, including the realized kernel, the two-scale realized kernel, and a bias-corrected pre-averaging estimator, the flat-top realized kernel enjoys a higher-order advantage in terms of bias reduction, in addition to good efficiency properties. The analysis is extended to jump-diffusions where the asymptotic properties of a flat-top realized kernel estimate of the total quadratic variation are established. Apart from a larger asymptotic variance, they are similar to the no-jump case. Finally, the estimators are used to design two classes of (medium) blocked realized kernels, which produce consistent, non-negative estimates of integrated variance. The blocked estimators are shown to have no loss either of asymptotic efficiency or in the rate of consistency relative to the flat-top realized kernels when jumps are absent. However, only the medium blocked realized kernels achieve the optimal rate of convergence under the jump alternative.

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supporting

confidence: 83%

Section: The Noise Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimating the Quadratic Variation Spectrum of Noisy Asset Prices Using Generalized Flat-Top Realized Kernels

Varneskov

2016

Econom. Theory

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“…It is, however, not the first attempt to push towards the integration of the two fields. Diebold and Strasser (2013) focus on the correlation of efficient price and noise in several leading microstructure models, and study the implications for integrated volatility estimation. Li et al (2016) model the microstructure noise as a parametric function of the trading information and develop an efficient volatility estimator, see also Chaker (2017) and Clinet and Potiron (2017) for similar approaches.…”

Section: Market Microstructurementioning

confidence: 99%

A ReMeDI for Microstructure Noise

Li,

Linton

2019

SSRN Journal

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We introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary finite moments of a microstructure noise process, which could be serially dependent and nonstationary, based on high-frequency data. We characterize the limit distributions of the proposed estimators and construct robust confidence intervals under infill asymptotics. We further demonstrate that the ReMeDI approach also works on lowfrequency, non-infill data. It thus can be applied to many asset pricing and macroeconomic models, in which the time series have a permanent and a transitory component. We propose two liquidity measures that gauge the instantaneous and average bid-ask spread with potentially autocorrelated order flows. They can be consistently estimated within our framework. We provide an economic model to justify such measures as an intermediary's inventory risk measure when meeting serially dependent liquidity demand. Empirically we find our new liquidity measures are very effective to identify liquidity drains during the Flash Crash, when the market experienced extreme selling pressures. AbstractWe introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary finite moments of a microstructure noise process, which could be serially dependent and nonstationary, based on high-frequency data. We characterize the limit distributions of the proposed estimators and construct robust confidence intervals under infill asymptotics. We further demonstrate that the ReMeDI approach also works on low-frequency, non-infill data. It thus can be applied to many asset pricing and macroeconomic models, in which the time series have a permanent and a transitory component.We propose two liquidity measures that gauge the instantaneous and average bid-ask spread with potentially autocorrelated order flows. They can be consistently estimated within our framework. We provide an economic model to justify such measures as an intermediary's inventory risk measure when meeting serially dependent liquidity demand. Empirically we find our new liquidity measures are very effective to identify liquidity drains during the Flash Crash, when the market experienced extreme selling pressures.

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“…The conditions for semi-strong form efficient returns are analogous (see Diebold and Strasser, 2010). Whereas the price processes as defined in the previous subsection suffice to mechanically derive expressions for their cross-correlation, this reduced form setup alone does not give much guidance about sign and time pattern of these cross-correlations.…”

Section: Statistical Characterization Of Return/noise Correlationsmentioning

confidence: 99%

On the Correlation Structure of Microstructure Noise: A Financial Economic Approach

Diebold¹,

Strasser²

2013

The Review of Economic Studies

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We introduce the financial economics of market microstructure into the financial econometrics of asset return volatility estimation. In particular, we use market microstructure theory to derive the cross-correlation function between latent returns and market microstructure noise, which feature prominently in the recent volatility literature. The cross-correlation at zero displacement is typically negative, and crosscorrelations at nonzero displacements are positive and decay geometrically. If market makers are sufficiently risk averse, however, the cross-correlation pattern is inverted. We derive model-based volatility estimators, which we apply to stock and oil prices. Our results are useful for assessing the validity of the frequently-assumed independence of latent price and microstructure noise, for explaining observed cross-correlation patterns, for predicting as-yet undiscovered patterns, and for microstructure-based volatility estimation. Acknowledgments: For comments and suggestions we thank Rob Engle,

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On the Correlation Structure of Microstructure Noise: A Financial Economic Approach

Cited by 22 publications

References 33 publications

Estimating the Quadratic Variation Spectrum of Noisy Asset Prices Using Generalized Flat-Top Realized Kernels

Estimating the Quadratic Variation Spectrum of Noisy Asset Prices Using Generalized Flat-Top Realized Kernels

A ReMeDI for Microstructure Noise

On the Correlation Structure of Microstructure Noise: A Financial Economic Approach

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