Estimation for high-frequency data under parametric market microstructure noise

Clinet, Simon; Potiron, Yoann

doi:10.1007/s10463-020-00762-3

Cited by 16 publications

(4 citation statements)

References 69 publications

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“…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), Chaker (2017) and Clinet and Potiron (2017) model the microstructure noise as a parametric function of the observable trading information and develop efficient volatility estimators. Bandi et al (2017) develop a novel measure of the staleness of stock returns under the infill asymptotic framework.…”

Section: Related Literaturementioning

confidence: 99%

A ReMeDI for Microstructure Noise

Linton

2021

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 moments of the microstructure noise process based on highfrequency data, where the noise process could be serially dependent, endogenous, and nonstationary. We characterize the limit distributions of the proposed estimators and construct confidence intervals under infill asymptotics. Our simulation and empirical studies show that the ReMeDI approach is very effective to measure the scale and the serial dependence of microstructure noise. Moreover, the estimators are quite robust to model specifications, sample sizes and data frequencies.

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Section: Related Literaturementioning

confidence: 99%

A ReMeDI for Microstructure Noise

Linton

2021

SSRN Journal

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“…Another recent strand of the literature explores the variety of microstructure data including observable information, seeking to parameterize the microstructure noise; see Li et al (2016), Chaker (2017), Clinet and Potiron (2017) and Clinet and Potiron (2019). The parametrization allows for rich dynamics of the microstructure noise and at the same time improves the convergence rates of associated volatility 7 Under this sampling scheme, Y n i (resp.…”

Section: Framework and Assumptionsmentioning

confidence: 99%

Dependent microstructure noise and integrated volatility estimation from high-frequency data

Laeven

Vellekoop

2020

Journal of Econometrics

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In this paper, we develop econometric tools to analyze the integrated volatility (IV) of the efficient price and the dynamic properties of microstructure noise in high-frequency data under general dependent noise. We first develop consistent estimators of the variance and autocovariances of noise using a variant of realized volatility. Next, we employ these estimators to adapt the pre-averaging method and derive consistent estimators of the IV, which converge stably to a mixed Gaussian distribution at the optimal rate n 1/4 . To improve the finite sample performance, we propose a multi-step approach that corrects the finite sample bias, which turns out to be crucial in applications. Our extensive simulation studies demonstrate the excellent performance of our multi-step estimators. In an empirical study, we analyze the dependence structures of microstructure noise and provide intuitive economic interpretations; we also illustrate the importance of accounting for both the serial dependence in noise and the finite sample bias when estimating IV.

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“…where I i,n is the vector of information at time τ i,n and ˜ i,n is the noisy part of the original noise i,n . See also the related papers Chaker (2017) and Clinet and Potiron (2017, 2018c, 2018d. Here again the observation times are assumed to be regular, i.e.…”

Section: Estimation Of Volatility and Higher Powers Of Volatility Inc...mentioning

confidence: 99%

Local Parametric Estimation in High Frequency Data

Potiron

Mykland

2019

Journal of Business & Economic Statistics

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In this paper, we give a general time-varying parameter model, where the multidimensional parameter possibly includes jumps. The quantity of interest is defined as the integrated value over time of the parameter process Θ = T −1 T 0 θ * t dt. We provide a local parametric estimator (LPE) of Θ and conditions under which we can show the central limit theorem. Roughly speaking those conditions correspond to some uniform limit theory in the parametric version of the problem. The framework is restricted to the specific convergence rate n 1/2 . Several examples of LPE are studied: estimation of volatility, powers of volatility, volatility when incorporating trading information and time-varying MA(1). Keywords: integrated volatility; market microstructure noise; powers of volatility; quasi maximum likelihood estimator * We are indebted to Simon Clinet, Takaki Hayashi, Dacheng Xiu, participants of the seminars in Berlin and Tokyo and conferences in Osaka, Toyama, the SoFie annual meeting in Hong Kong, the PIMS meeting in Edmonton for valuable comments, which helped in improving the quality of the paper.We assume that we observe the d-dimensional vectors Z 0,n , · · · , Z Nn,n , where N n can be random, the observation times satisfy τ 0,n := 0 < τ 1,n < · · · < τ Nn,n ≤ T . The observations and the observation times are both related to the latent parameter θ * t . As an example, the observations can satisfy Z τ i,n ,n = X τ i,n + i,n , where X t = σ t dW t stands for the efficient price, W t is a standard Brownian motion, i,n corresponds to the market microstructure noise (which will be restricted to be of order i,n = O p (1/ √ n) due to the limitation of the technology developed in Section 3), is independent and identically distributed (IID) and independent from X t , and the latent parameter is equal to the volatility, i.e. θ * t = σ 2 t . We assume that the parameter process θ * t takes values in K, a (not necessarily compact) subset of R p . We do not assume any independence between θ * t and the other quantities driving the observations, such as the Brownian motion of the efficient price process. In particular, there can be leverage effect (see e.g. Wang and Mykland (2014), Aït-Sahalia et al. (2017)). Also, the arrival times τ i,n and the parameter θ * t can be correlated, i.e. there is (some kind of) endogeneity in sampling times. AsymptoticsThere are commonly two choices of asymptotics in the literature: the high-frequency asymptotics, which makes the number of observations explode on [0, T ], and the lowfrequency asymptotics, which takes T to infinity. We choose the former one. Investigating the low-frequency implementation case is beyond the scope of this paper 1 .1 If we set down the asymptotic theory in the same way as in p.3 of Dahlhaus (1997), we conjecture that the results of this paper would stay true.

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Estimation for high-frequency data under parametric market microstructure noise

Cited by 16 publications

References 69 publications

A ReMeDI for Microstructure Noise

A ReMeDI for Microstructure Noise

Dependent microstructure noise and integrated volatility estimation from high-frequency data

Local Parametric Estimation in High Frequency Data

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