IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v135y2019icp95-106.html
   My bibliography  Save this article

Moving block bootstrapping for a CUSUM test for correlation change

Author

Listed:
  • Choi, Ji-Eun
  • Shin, Dong Wan

Abstract

Based on the test of Wied et al. (2012), we construct a bootstrapping CUSUM test for correlation change. The bootstrap test uses the bootstrap critical value obtained from the distribution of the moving block bootstrap samples. The asymptotic null distribution of the bootstrap test is shown to be the same as that of the original test. Consistency of the bootstrap test is proved under an alternative hypothesis of a correlation change. A Monte Carlo simulation shows that the proposed bootstrap test has a good size performance while the existing tests have serious size distortion for conditionally heteroscedastic samples and for serially correlated samples. The better size of the bootstrap test than the existing tests is achieved at the cost of some power loss in some cases.

Suggested Citation

  • Choi, Ji-Eun & Shin, Dong Wan, 2019. "Moving block bootstrapping for a CUSUM test for correlation change," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 95-106.
  • Handle: RePEc:eee:csdana:v:135:y:2019:i:c:p:95-106
    DOI: 10.1016/j.csda.2018.10.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947319300301
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2018.10.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    2. Andrew Patton & Dimitris Politis & Halbert White, 2009. "Correction to “Automatic Block-Length Selection for the Dependent Bootstrap” by D. Politis and H. White," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 372-375.
    3. Gonçalves, Sílvia & White, Halbert, 2002. "The Bootstrap Of The Mean For Dependent Heterogeneous Arrays," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1367-1384, December.
    4. Galeano, Pedro & Wied, Dominik, 2014. "Multiple break detection in the correlation structure of random variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 262-282.
    5. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    6. Wied, Dominik & Krämer, Walter & Dehling, Herold, 2012. "Testing For A Change In Correlation At An Unknown Point In Time Using An Extended Functional Delta Method," Econometric Theory, Cambridge University Press, vol. 28(3), pages 570-589, June.
    7. Cavaliere, Giuseppe & Taylor, A.M. Robert, 2008. "Bootstrap Unit Root Tests For Time Series With Nonstationary Volatility," Econometric Theory, Cambridge University Press, vol. 24(1), pages 43-71, February.
    8. Lahiri, S. N., 1993. "On the moving block bootstrap under long range dependence," Statistics & Probability Letters, Elsevier, vol. 18(5), pages 405-413, December.
    9. Hansen, Bruce E., 1991. "GARCH(1, 1) processes are near epoch dependent," Economics Letters, Elsevier, vol. 36(2), pages 181-186, June.
    10. Jamel Jouini, 2010. "Bootstrap methods for single structural change tests: power versus corrected size and empirical illustration," Statistical Papers, Springer, vol. 51(1), pages 85-109, January.
    11. Dominik Wied, 2017. "A nonparametric test for a constant correlation matrix," Econometric Reviews, Taylor & Francis Journals, vol. 36(10), pages 1157-1172, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fang Duan & Dominik Wied, 2018. "A residual-based multivariate constant correlation test," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(6), pages 653-687, August.
    2. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    3. Gonçalves, Sílvia & White, Halbert, 2002. "The Bootstrap Of The Mean For Dependent Heterogeneous Arrays," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1367-1384, December.
    4. Allen, Jason & Gregory, Allan W. & Shimotsu, Katsumi, 2011. "Empirical likelihood block bootstrapping," Journal of Econometrics, Elsevier, vol. 161(2), pages 110-121, April.
    5. Aleksejs Krecetovs & Pasquale Della Corte, 2016. "Macro uncertainty and currency premia," 2016 Meeting Papers 624, Society for Economic Dynamics.
    6. Horváth, Lajos & Rice, Gregory & Zhao, Yuqian, 2022. "Change point analysis of covariance functions: A weighted cumulative sum approach," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    7. Martin Hrba & Matúš Maciak & Barbora Peštová & Michal Pešta, 2022. "Bootstrapping Not Independent and Not Identically Distributed Data," Mathematics, MDPI, vol. 10(24), pages 1-26, December.
    8. Barbora Peštová & Michal Pešta, 2018. "Abrupt change in mean using block bootstrap and avoiding variance estimation," Computational Statistics, Springer, vol. 33(1), pages 413-441, March.
    9. Sarno, Lucio & Schneider, Paul & Wagner, Christian, 2012. "Properties of foreign exchange risk premiums," Journal of Financial Economics, Elsevier, vol. 105(2), pages 279-310.
    10. Hong, Yongmiao & Linton, Oliver & McCabe, Brendan & Sun, Jiajing & Wang, Shouyang, 2024. "Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach," Journal of Econometrics, Elsevier, vol. 238(2).
    11. Ardia, David & Bluteau, Keven & Tran, Thien Duy, 2022. "How easy is it for investment managers to deploy their talent in green and brown stocks?," Finance Research Letters, Elsevier, vol. 48(C).
    12. Eunju Hwang & Dong Wan Shin, 2017. "Stationary bootstrapping for common mean change detection in cross-sectionally dependent panels," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(6), pages 767-787, November.
    13. Sevan Gulesserian & Mohitosh Kejriwal, 2014. "On the power of bootstrap tests for stationarity: a Monte Carlo comparison," Empirical Economics, Springer, vol. 46(3), pages 973-998, May.
    14. Fu, Zhonghao & Hong, Yongmiao, 2019. "A model-free consistent test for structural change in regression possibly with endogeneity," Journal of Econometrics, Elsevier, vol. 211(1), pages 206-242.
    15. Politis, D N, 2009. "Higher-Order Accurate, Positive Semi-definite Estimation of Large-Sample Covariance and Spectral Density Matrices," University of California at San Diego, Economics Working Paper Series qt66w826hz, Department of Economics, UC San Diego.
    16. Ji-Eun Choi & Dong Wan Shin, 2021. "A self-normalization break test for correlation matrix," Statistical Papers, Springer, vol. 62(5), pages 2333-2353, October.
    17. Javier Hidalgo & Marcia M Schafgans, 2017. "Inference Without Smoothing for Large Panels with Cross- Sectional and Temporal Dependence," STICERD - Econometrics Paper Series 597, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    18. David Ardia & Kris Boudt, 2013. "The Peer Performance of Hedge Funds," Cahiers de recherche 1329, CIRPEE.
    19. J. Hidalgo & M. Schafgans, 2020. "Inference without smoothing for large panels with cross-sectional and temporal dependence," Papers 2006.14409, arXiv.org.
    20. Lyócsa, Štefan & Todorova, Neda, 2020. "Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 628-645.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:135:y:2019:i:c:p:95-106. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.