IDEAS home Printed from https://ideas.repec.org/a/bla/acctfi/v42y2002i2p111-130.html
   My bibliography  Save this article

Bayesian estimation of financial models

Author

Listed:
  • Philip Gray

Abstract

This paper outlines a general methodology for estimating the parameters of financial models commonly employed in the literature. A numerical Bayesian technique is utilised to obtain the posterior density of model parameters and functions thereof. Unlike maximum likelihood estimation, where inference is only justified in large samples, the Bayesian densities are exact for any sample size. A series of simulation studies are conducted to compare the properties of point estimates, the distribution of option and bond prices, and the power of specification tests under maximum likelihood and Bayesian methods. Results suggest that maximum–likelihood–based asymptotic distributions have poor finite–sampleproperties.

Suggested Citation

  • Philip Gray, 2002. "Bayesian estimation of financial models," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 42(2), pages 111-130, June.
    • Handle: RePEc:bla:acctfi:v:42:y:2002:i:2:p:111-130
    DOI: 10.1111/1467-629X.00070
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-629X.00070
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-629X.00070?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. L. Steinruecke & R. Zagst & A. Swishchuk, 2015. "The Markov-switching jump diffusion LIBOR market model," Quantitative Finance, Taylor & Francis Journals, vol. 15(3), pages 455-476, March.
    2. Alcock, Jamie & Burrage, Kevin, 2004. "A genetic estimation algorithm for parameters of stochastic ordinary differential equations," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 255-275, September.

    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:bla:acctfi:v:42:y:2002:i:2:p:111-130. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley-Blackwell Digital Licensing or Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/aaanzea.html .

    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.