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Markov Chain Monte Carlo Methods in Financial Econometrics

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  • Michael Verhofen

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    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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    File URL: http://hdl.handle.net/10.1007/s11408-005-6459-1
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    Bibliographic Info

    Article provided by Springer in its journal Financial Markets and Portfolio Management.

    Volume (Year): 19 (2005)
    Issue (Month): 4 (December)
    Pages: 397-405

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    Handle: RePEc:kap:fmktpm:v:19:y:2005:i:4:p:397-405

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    Web page: http://www.springerlink.com/link.asp?id=119763

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    1. 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.
    2. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 371-89, October.
    3. 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, 06.
    4. 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.
    5. 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.
    6. Doron Avramov, 2004. "Stock Return Predictability and Asset Pricing Models," Review of Financial Studies, Society for Financial Studies, vol. 17(3), pages 699-738.
    7. Olivier Ledoit & Michael Wolf, 2001. "Improved estimation of the covariance matrix of stock returns with an application to portofolio selection," Economics Working Papers 586, Department of Economics and Business, Universitat Pompeu Fabra.
    8. 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.
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    Cited by:
    1. Stefan Erdorf & Nicolas Heinrichs, 2011. "Co-movement of revenue: structural changes in the business cycle," Financial Markets and Portfolio Management, Springer, vol. 25(4), pages 411-433, December.
    2. Günter Franke & Julia Hein, 2007. "Securitisation of Mezzanine Capital in Germany," CoFE Discussion Paper 07-07, Center of Finance and Econometrics, University of Konstanz.

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