A Mixtured Localized Likelihood Method for GARCH Models with Multiple Change-points
Haipeng Xing (),
Hongsong Yuan () and
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Haipeng Xing: Department of Applied Mathematics and Statistics, SUNY at Stony Brook, Stony Brook, 11790, U.S.A., http://www.ams.sunysb.edu/~xing/
Hongsong Yuan: School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, CHINA
Sichen Zhou: Worldquant LLC, Shanghai, 200040, CHINA, https://www.worldquant.com/contact-us/
Review of Economics & Finance, 2017, vol. 8, 44-60
This paper discusses GARCH models with multiple change-points and proposes a mixture localized likelihood method to estimate the piecewise constant GARCH parameters. The proposed method is statistically and computationally attractive as it synthesizes two degenerated and basic inference procedures. A bounded complexity mixture approximation, whose computational complexity is linear only, is also proposed for the estimates of time-varying GARCH parameters. These procedures are further applied to solve challenging problems such as inference on the number and locations of change-points that partition the unknown parameter sequence into segments of constant values. An illustrative analysis of the S&P500 index is provided.
Keywords: Localized likelihood; GARCH; Multiple change-points; Segmentation (search for similar items in EconPapers)
JEL-codes: C14 C22 C51 (search for similar items in EconPapers)
Note: The first author¡¯s research is supported by the National Science Foundation under grant DMS- 1206321 and DMS-1612501 at SUNY at Stony Brook. The second author¡¯s research is supported by the China Scholarship Council (File No. 201505990277). The authors would like to thank two anonymous referees for their helpful suggestions. The usual disclaimer applies.
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