View : 35 Download: 0

Stationary bootstrapping for realized covariations of high frequency financial data

Title
Stationary bootstrapping for realized covariations of high frequency financial data
Authors
Hwang E.Shin D.W.
Ewha Authors
신동완
SCOPUS Author ID
신동완scopus
Issue Date
2017
Journal Title
Statistics
ISSN
0233-1888JCR Link
Citation
vol. 51, no. 4, pp. 844 - 861
Keywords
realized correlation coefficientrealized covariancerealized regression coefficientStationary bootstrap
Publisher
Taylor and Francis Ltd.
Indexed
SCI; SCIE; SCOPUS scopus
Abstract
This paper studies the stationary bootstrap applicability for realized covariations of high frequency asynchronous financial data. The stationary bootstrap method, which is characterized by a block-bootstrap with random block length, is applied to estimate the integrated covariations. The bootstrap realized covariance, bootstrap realized regression coefficient and bootstrap realized correlation coefficient are proposed, and the validity of the stationary bootstrapping for them is established both for large sample and for finite sample. Consistencies of bootstrap distributions are established, which provide us valid stationary bootstrap confidence intervals. The bootstrap confidence intervals do not require a consistent estimator of a nuisance parameter arising from nonsynchronous unequally spaced sampling while those based on a normal asymptotic theory require a consistent estimator. A Monte-Carlo comparison reveals that the proposed stationary bootstrap confidence intervals have better coverage probabilities than those based on normal approximation. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
DOI
10.1080/02331888.2017.1344241
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
Files in This Item:
There are no files associated with this item.


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE