TY - JOUR
T1 - Estimation for high-frequency data under parametric market microstructure noise
AU - Clinet, Simon
AU - Potiron, Yoann
N1 - Funding Information:
We would like to thank Selma Chaker, Yingying Li, Xinghua Zheng, Manh Cuong Pham, Mathias Vetter,?two anonymous referees, the participants of the 2nd International Conference on Econometrics and Statistics in Hong Kong, and the Econometric Society Australasian Meeting 2018 in Auckland for helpful discussions and advice. The research of Yoann Potiron is supported by a special private grant from Keio University and Japanese Society for the Promotion of Science Grant-in-Aid for Young Scientists No. 60781119. The research of Simon Clinet is supported by Japanese Society for the Promotion of Science Grant-in-Aid for Young Scientists No. 19K13671.
Funding Information:
We would like to thank Selma Chaker, Yingying Li, Xinghua Zheng, Manh Cuong Pham, Mathias Vetter, two anonymous referees, the participants of the 2nd International Conference on Econometrics and Statistics in Hong Kong, and the Econometric Society Australasian Meeting 2018 in Auckland for helpful discussions and advice. The research of Yoann Potiron is supported by a special private grant from Keio University and Japanese Society for the Promotion of Science Grant-in-Aid for Young Scientists No. 60781119. The research of Simon Clinet is supported by Japanese Society for the Promotion of Science Grant-in-Aid for Young Scientists No. 19K13671.
Publisher Copyright:
© 2020, The Institute of Statistical Mathematics, Tokyo.
PY - 2021/8
Y1 - 2021/8
N2 - We develop a general class of noise-robust estimators based on the existing estimators in the non-noisy high-frequency data literature. The microstructure noise is a parametric function of the limit order book. The noise-robust estimators are constructed as plug-in versions of their counterparts, where we replace the efficient price, which is non-observable, by an estimator based on the raw price and limit order book data. We show that the technology can be applied to five leading examples where, depending on the problem, price possibly includes infinite jump activity and sampling times encompass asynchronicity and endogeneity.
AB - We develop a general class of noise-robust estimators based on the existing estimators in the non-noisy high-frequency data literature. The microstructure noise is a parametric function of the limit order book. The noise-robust estimators are constructed as plug-in versions of their counterparts, where we replace the efficient price, which is non-observable, by an estimator based on the raw price and limit order book data. We show that the technology can be applied to five leading examples where, depending on the problem, price possibly includes infinite jump activity and sampling times encompass asynchronicity and endogeneity.
KW - Functionals of volatility
KW - High-frequency covariance
KW - High-frequency data
KW - Limit order book
KW - Parametric market microstructure noise
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U2 - 10.1007/s10463-020-00762-3
DO - 10.1007/s10463-020-00762-3
M3 - Article
AN - SCOPUS:85091070429
VL - 73
SP - 649
EP - 669
JO - Annals of the Institute of Statistical Mathematics
JF - Annals of the Institute of Statistical Mathematics
SN - 0020-3157
IS - 4
ER -