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Estimation of structural mean breaks for long-memory data sets

Title
Estimation of structural mean breaks for long-memory data sets
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. 904 - 920
Keywords
HAR modellong memorymean breakrealized volatility
Publisher
Taylor and Francis Ltd.
Indexed
SCI; SCIE; SCOPUS scopus
Abstract
In long-memory data sets such as the realized volatilities of financial assets, a sequential test is developed for the detection of structural mean breaks. The long memory, if any, is adjusted by fitting an HAR (heterogeneous autoregressive) model to the data sets and taking the residuals. Our test consists of applying the sequential test of Bai and Perron [Estimating and testing linear models with multiple structural changes. Econometrica. 1998;66:47–78] to the residuals. The large-sample validity of the proposed test is investigated in terms of the consistency of the estimated number of breaks and the asymptotic null distribution of the proposed test. A finite-sample Monte-Carlo experiment reveals that the proposed test tends to produce an unbiased break time estimate, while the usual sequential test of Bai and Perron tends to produce biased break times in the case of long memory. The experiment also reveals that the proposed test has a more stable size than the Bai and Perron test. The proposed test is applied to two realized volatility data sets of the S&P index and the Korea won-US dollar exchange rate for the past 7 years and finds 2 or 3 breaks, while the Bai and Perron test finds 8 or more breaks. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
DOI
10.1080/02331888.2017.1335314
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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