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A Mixtured Localized Likelihood Method for GARCH Models with Multiple Change-points

Author

Listed:
  • Haipeng Xing

    (Department of Applied Mathematics and Statistics, SUNY at Stony Brook, Stony Brook, 11790, U.S.A.)

  • Hongsong Yuan

    (School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, CHINA)

  • Sichen Zhou

    (Worldquant LLC, Shanghai, 200040, CHINA)

Abstract

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.

Suggested Citation

  • Haipeng Xing & Hongsong Yuan & Sichen Zhou, 2017. "A Mixtured Localized Likelihood Method for GARCH Models with Multiple Change-points," Review of Economics & Finance, Better Advances Press, Canada, vol. 8, pages 44-60, May.
    • Handle: RePEc:bap:journl:170204
    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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    References listed on IDEAS

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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