IDEAS home Printed from https://ideas.repec.org/a/kap/fmktpm/v19y2005i4p397-405.html
   My bibliography  Save this article

Markov Chain Monte Carlo Methods in Financial Econometrics

Author

Listed:
  • Michael Verhofen

    ()

Abstract

Markov Chain Monte Carlo (MCMC) methods have become very popular in financial econometrics during the last years. MCMC methods are applicable where classical methods fail. In this paper, we give an introduction to MCMC and present recent empirical evidence. Finally, we apply MCMC methods to portfolio choice to account for parameter uncertainty and to incorporate different degrees of belief in an asset pricing model. Copyright Swiss Society for Financial Market Research 2005

Suggested Citation

  • Michael Verhofen, 2005. "Markov Chain Monte Carlo Methods in Financial Econometrics," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 19(4), pages 397-405, December.
  • Handle: RePEc:kap:fmktpm:v:19:y:2005:i:4:p:397-405
    DOI: 10.1007/s11408-005-6459-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11408-005-6459-1
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
    2. Kalimipalli, Madhu & Susmel, Raul, 2004. "Regime-switching stochastic volatility and short-term interest rates," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 309-329, June.
    3. Ledoit, Olivier & Wolf, Michael, 2003. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 10(5), pages 603-621, December.
    4. Doron Avramov, 2004. "Stock Return Predictability and Asset Pricing Models," Review of Financial Studies, Society for Financial Studies, vol. 17(3), pages 699-738.
    5. Jorion, Philippe, 1991. "Bayesian and CAPM estimators of the means: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 15(3), pages 717-727, June.
    6. Bjørn Eraker & Michael Johannes & Nicholas Polson, 2003. "The Impact of Jumps in Volatility and Returns," Journal of Finance, American Finance Association, vol. 58(3), pages 1269-1300, June.
    7. repec:bla:restud:v:65:y:1998:i:3:p:361-93 is not listed on IDEAS
    8. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    9. L. Randall Wray & Stephanie Bell, 2004. "Introduction," Chapters,in: Credit and State Theories of Money, chapter 1 Edward Elgar Publishing.
    10. Jacquier, Eric & Polson, Nicholas G. & Rossi, P.E.Peter E., 2004. "Bayesian analysis of stochastic volatility models with fat-tails and correlated errors," Journal of Econometrics, Elsevier, vol. 122(1), pages 185-212, September.
    11. Philippe Robert-Demontrond & R. Ringoot, 2004. "Introduction," Post-Print halshs-00081823, HAL.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Günter Franke & Julia Hein, 2008. "Securitization of mezzanine capital in Germany," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 22(3), pages 219-240, September.
    2. Ming Lin & Eric A. Suess & Robert H. Shumway & Rong Chen, 2016. "Bayesian Deconvolution of Signals Observed on Arrays," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 837-850, November.
    3. Stefan Erdorf & Nicolas Heinrichs, 2011. "Co-movement of revenue: structural changes in the business cycle," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(4), pages 411-433, December.
    4. Wanke, Peter & Pestana Barros, Carlos & Chen, Zhongfei, 2015. "An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models," International Journal of Production Economics, Elsevier, vol. 169(C), pages 110-126.

    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:kap:fmktpm:v:19:y:2005:i:4:p:397-405. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Rebekah McClure). General contact details of provider: http://www.springer.com .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.