IDEAS home Printed from https://ideas.repec.org/a/rnd/arjebs/v13y2021i4p1-7.html
   My bibliography  Save this article

A Performance Comparison of Various Bootstrap Methods for Diffusion Processes

Author

Listed:
  • Jung S You
  • Minsoo Jeong

Abstract

In this paper, we compare the finite sample performances of various bootstrap methods for diffusion processes. Though diffusion processes are widely used to analyze stocks, bonds, and many other financial derivatives, they are known to heavily suffer from size distortions of hypothesis tests. While there are many bootstrap methods applicable to diffusion models to reduce such size distortions, their finite sample performances are yet to be investigated. We perform a Monte Carlo simulation comparing the finite sample properties, and our results show that the strong Taylor approximation method produces the best performance, followed by the Hermite expansion method.

Suggested Citation

  • Jung S You & Minsoo Jeong, 2021. "A Performance Comparison of Various Bootstrap Methods for Diffusion Processes," Journal of Economics and Behavioral Studies, AMH International, vol. 13(4), pages 1-7.
    • Handle: RePEc:rnd:arjebs:v:13:y:2021:i:4:p:1-7
    DOI: 10.22610/jebs.v13i4(J).3185
    as

    Download full text from publisher

    File URL: https://ojs.amhinternational.com/index.php/jebs/article/view/3185/2030
    Download Restriction: no
    File URL: https://ojs.amhinternational.com/index.php/jebs/article/view/3185
    Download Restriction: no
    File URL: https://libkey.io/10.22610/jebs.v13i4(J).3185?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
    ---><---

    References listed on IDEAS

    as
    1. Joel L. Horowitz, 2003. "Bootstrap Methods for Markov Processes," Econometrica, Econometric Society, vol. 71(4), pages 1049-1082, July.
    2. Donald W. K. Andrews, 2004. "the Block-Block Bootstrap: Improved Asymptotic Refinements," Econometrica, Econometric Society, vol. 72(3), pages 673-700, May.
    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. D. S. Poskitt, 2008. "Properties of the Sieve Bootstrap for Fractionally Integrated and Non‐Invertible Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(2), pages 224-250, March.
    2. Joel L. Horowitz, 2018. "Bootstrap Methods in Econometrics," Papers 1809.04016, arXiv.org.
    3. Joel L. Horowitz, 2018. "Bootstrap methods in econometrics," CeMMAP working papers CWP53/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2013. "Testing Many Moment Inequalities," CeMMAP working papers 65/13, Institute for Fiscal Studies.
    5. 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.
    6. L. Hospido, 2012. "Modelling heterogeneity and dynamics in the volatility of individual wages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 386-414, April.
    7. Pesendorfer, Martin & Takahashi, Yuya & Otsu, Taisuke, 2014. "Testing Equilibrium Multiplicity in Dynamic Games," CEPR Discussion Papers 10111, C.E.P.R. Discussion Papers.
    8. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    9. Marco Giacoletti & Kristoffer T. Laursen & Kenneth J. Singleton, 2021. "Learning From Disagreement in the U.S. Treasury Bond Market," Journal of Finance, American Finance Association, vol. 76(1), pages 395-441, February.
    10. Jinyong Hahn & Zhipeng Liao, 2021. "Bootstrap Standard Error Estimates and Inference," Econometrica, Econometric Society, vol. 89(4), pages 1963-1977, July.
    11. repec:wyi:journl:002120 is not listed on IDEAS
    12. Ligon, Ethan, 2017. "Estimating household welfare from disaggregate expenditures," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt5gc4h1fm, Department of Agricultural & Resource Economics, UC Berkeley.
    13. Kristensen, Dennis, 2008. "Estimation of partial differential equations with applications in finance," Journal of Econometrics, Elsevier, vol. 144(2), pages 392-408, June.
    14. Li, Y. & Donkers, A.C.D. & Melenberg, B., 2006. "The Non- and Semiparametric Analysis of MS Models : Some Applications," Other publications TiSEM c14adc9f-f490-40d6-81b7-8, Tilburg University, School of Economics and Management.
    15. Mihai C. Giurcanu, 2017. "Oracle M-Estimation for Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 479-504, May.
    16. Corradi, Valentina & Swanson, Norman R., 2006. "Predictive density and conditional confidence interval accuracy tests," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 187-228.
    17. Park, Joon Y. & Wang, Bin, 2021. "Nonparametric estimation of jump diffusion models," Journal of Econometrics, Elsevier, vol. 222(1), pages 688-715.
    18. Bertail, Patrice & Clemencon, Stephan, 2008. "Approximate regenerative-block bootstrap for Markov chains," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2739-2756, January.
    19. Andrey Rafalson, 2012. "Bootstrap inference about integrated volatility (in Russian)," Quantile, Quantile, issue 10, pages 91-108, December.
    20. Victor Chernozhukov & Denis Chetverikov & Kengo Kato & Aureo de Paula, 2019. "Inference on Causal and Structural Parameters using Many Moment Inequalities," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(5), pages 1867-1900.
    21. Yongmiao Hong & Yoon-Jin Lee, 2007. "Detecting Misspecifications in Autoregressive Conditional Duration Models," CAEPR Working Papers 2007-019, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.

    More about this item

    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:rnd:arjebs:v:13:y:2021:i:4:p:1-7. 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: Muhammad Tayyab (email available below). General contact details of provider: https://ojs.amhinternational.com/index.php/jebs .

    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.