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Markov Chain Monte Carlo Methods in Financial Econometrics

  • Michael Verhofen

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    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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    Article provided by Springer & Swiss Society for Financial Market Research 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
    DOI: 10.1007/s11408-005-6459-1
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    1. Ledoit, Olivier & Wolf, Michael, 2000. "Improved estimation of the covariance matrix of stock returns with an application to portfolio selection," DES - Working Papers. Statistics and Econometrics. WS 10089, Universidad Carlos III de Madrid. Departamento de Estadística.
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    6. L. Randall Wray & Stephanie Bell, 2004. "Introduction," Chapters, in: Credit and State Theories of Money, chapter 1 Edward Elgar Publishing.
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    8. 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.
    9. 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.
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