Distribution-free specification test for volatility function based on high-frequency data with microstructure noise

Financial practices often need to estimate an integrated volatility matrix of a large number of assets using noisy high-frequency data. Many existing estimators of a volatility matrix of small dimensions become inconsistent when the size of the matrix is close to or larger than the sample size. This paper introduces a new type of large volatility matrix estimator based on nonsynchronized high-frequency data, allowing for the presence of microstructure noise. When both the number of assets and the sample size go to infinity, we show that our new estimator is consistent and achieves a fast convergence rate, where the rate is optimal with respect to the sample size. A simulation study is conducted to check the finite sample performance of the proposed estimator.

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Fourier volatility forecasting with high-frequency data and microstructure noise

Quantitative Finance ◽

10.1080/14697680903413589 ◽

2012 ◽

Vol 12 (2) ◽

pp. 281-293 ◽

Cited By ~ 5

Author(s):

Emilio Barucci ◽

Davide Magno ◽

Maria Elvira Mancino

Keyword(s):

High Frequency ◽

High Frequency Data ◽

Frequency Data ◽

Microstructure Noise ◽

Volatility Forecasting

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Improved algorithm for cleaning high frequency data: An analysis of foreign currency

Corporate Ownership and Control ◽

10.22495/cocv12i3c1p1 ◽

2015 ◽

Vol 12 (3) ◽

pp. 125-132

Author(s):

Nirodha I. Jayawardena ◽

Jason West ◽

Neda Todorova ◽

Bin Li

Keyword(s):

High Frequency ◽

Foreign Currency ◽

High Frequency Data ◽

Frequency Data ◽

Microstructure Noise ◽

Volatility Clustering ◽

Time Series Modelling ◽

Non Gaussian ◽

Irregular Spacing ◽

The Empirical Analysis

High-frequency data are notorious for their noise and asynchrony, which may bias or contaminate the empirical analysis of prices and returns. In this study, we develop a novel data filtering approach that simultaneously addresses volatility clustering and irregular spacing, which are inherent characteristics of high-frequency data. Using high frequency currency data collected at five-minute intervals, we find the presence of vast microstructure noise coupled with random volatility clusters, and observe an extremely non-Gaussian distribution of returns. To process non-Gaussian high-frequency data for time series modelling, we propose two efficient and robust standardisation methods that cater for volatility clusters, which clean the data and achieve near-normal distributions. We show that the filtering process efficiently cleans high-frequency data for use in empirical settings while retaining the underlying distributional properties

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Performance of estimators of quadratic variation based on high frequency data—empirical review

WORLD OF ECONOMICS AND MANAGEMENT ◽

10.25205/2542-0429-2020-20-3-47-69 ◽

2020 ◽

Vol 20 (3) ◽

pp. 47-69

Author(s):

John Gayomey ◽

Andrei V. Kostin

Keyword(s):

High Frequency ◽

Quadratic Variation ◽

Financial Data ◽

High Frequency Data ◽

Estimation Accuracy ◽

Frequency Data ◽

Microstructure Noise ◽

Asset Return ◽

Functional Forms ◽

Realised Volatility

Recently, advances in computer technology and data recording and storage have made high-frequency financial data readily available to researchers. As a result, the volatility literature has steadily progressed toward the use of higher-frequency data. However, the move towards the use of higher-frequency financial data in the estimation of volatility of financial returns has resulted in the development of many realised volatility measures of asset return variability based on a variety of different assumptions and functional forms and thus making theoretical comparison and selection of the estimators for empirical applications very difficult if not impossible. This article provides an empirical review on the performance of estimators of quadratic variation/integrated variance based on high-frequency data to aid their application in empirical analysis. The result of the review shows that no single estimator works best in all situations; however, the more sophisticated realised measures, in particular the TSRV and KRV, are superior to the other estimators in terms of their estimation accuracy in the presence of market microstructure noise.

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