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June 2019 Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data
Yuta Koike
Ann. Statist. 47(3): 1663-1687 (June 2019). DOI: 10.1214/18-AOS1731


This paper establishes an upper bound for the Kolmogorov distance between the maximum of a high-dimensional vector of smooth Wiener functionals and the maximum of a Gaussian random vector. As a special case, we show that the maximum of multiple Wiener–Itô integrals with common orders is well approximated by its Gaussian analog in terms of the Kolmogorov distance if their covariance matrices are close to each other and the maximum of the fourth cumulants of the multiple Wiener–Itô integrals is close to zero. This may be viewed as a new kind of fourth moment phenomenon, which has attracted considerable attention in the recent studies of probability. This type of Gaussian approximation result has many potential applications to statistics. To illustrate this point, we present two statistical applications in high-frequency financial econometrics: One is the hypothesis testing problem for the absence of lead-lag effects and the other is the construction of uniform confidence bands for spot volatility.


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Yuta Koike. "Gaussian approximation of maxima of Wiener functionals and its application to high-frequency data." Ann. Statist. 47 (3) 1663 - 1687, June 2019.


Received: 1 September 2017; Revised: 1 March 2018; Published: June 2019
First available in Project Euclid: 13 February 2019

zbMATH: 07053522
MathSciNet: MR3911126
Digital Object Identifier: 10.1214/18-AOS1731

Primary: 60G15 , 60H07 , 62E17 , 62G20

Keywords: bootstrap , fourth moment phenomenon , Malliavin calculus , Maximum , Stein’s method , uniform confidence bands

Rights: Copyright © 2019 Institute of Mathematical Statistics


Vol.47 • No. 3 • June 2019
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