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Markov Chain Monte Carlo Methods in Financial Econometrics

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  • 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

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  • 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
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    References listed on IDEAS

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    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.
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    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. Polson, Nicholas G & Tew, Bernard V, 2000. "Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970-1996," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 164-173, April.
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    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. Yochanan Shachmurove & Reuel Shinnar (Deceased), 2012. "Do Chemical Reactors Hold the Solution for Global Economic Crises?," PIER Working Paper Archive 12-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    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. 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.
    5. 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.

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