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Bayesian Variable Selection of Risk Factors in the APT Model

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  • Robert Kohn

    ()
    (School of Economics, The University of New South Wales)

  • Rachida Ouysse

    ()
    (School of Economics, The University of New South Wales)

Abstract

In this paper we use a probabilistic approach to risk factor selection in the arbitrage pricing theory model. The methodology uses a bayesian framework to simultaneously select the pervasive risk factors and estimate the model. This will enable correct inference and testing of the implications of the APT model. Furthermore, we are able to make inference on any function of the parameters, in particular the pricing errors. We can also carry out tests of efficiency of the APT using the posterior odds ratio and bayesian confidence intervals. We investigate the macroeconomic risk factors of Chen, Roll, and Ross (1986) and the firm characteristic factors of Fama and French (1992,1993). Using monthly portfolio returns grouped by size and book to market, we find that the economic variables have zero risk premia although some appear to have non zero posterior probability. The "Market" factor is not priced. An APT model with factors mimicking size (SMB), book to market equity (HML), value-weighted portfolio and Standard and Poor, is supported by a conditionally independent prior and offers a significant decrease in the pricing error over a two-factor APT with SMB and HML. The posterior probability and cumulative distributions functions of the average risk premia and the pricing errors are compared to the normal distribution. The results show that under certain conditions the distortions are very small.

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Bibliographic Info

Paper provided by School of Economics, The University of New South Wales in its series Discussion Papers with number 2007-32.

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Length: 21 pages
Date of creation: Oct 2007
Date of revision:
Handle: RePEc:swe:wpaper:2007-32

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Keywords: Variable selection; Posterior density; Bayes factors; MCMC; APT models;

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  1. Burmeister, Edwin & McElroy, Marjorie B, 1988. " Joint Estimation of Factor Sensitivities and Risk Premia for the Arbitrage Pricing Theory," Journal of Finance, American Finance Association, vol. 43(3), pages 721-33, July.
  2. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
  3. Smith M. & Kohn R., 2002. "Parsimonious Covariance Matrix Estimation for Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1141-1153, December.
  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. Chen, Nai-Fu & Roll, Richard & Ross, Stephen A, 1986. "Economic Forces and the Stock Market," The Journal of Business, University of Chicago Press, vol. 59(3), pages 383-403, July.
  6. Ferson, Wayne E & Harvey, Campbell R, 1991. "The Variation of Economic Risk Premiums," Journal of Political Economy, University of Chicago Press, vol. 99(2), pages 385-415, April.
  7. John Geweke & Guofu Zhou, 1996. "Measuring the Pricing Error of the Arbitrage Pricing Theory," CEMA Working Papers 276, China Economics and Management Academy, Central University of Finance and Economics.
  8. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
  9. Ouysse, Rachida, 2006. "Consistent variable selection in large panels when factors are observable," Journal of Multivariate Analysis, Elsevier, vol. 97(4), pages 946-984, April.
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Cited by:
  1. Rachida Ouysse & Chris Nicholas, 2008. "Time Varying Determinants of Cross-Country Growth," Discussion Papers 2008-03, School of Economics, The University of New South Wales.

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