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A Bayesian Inference Approach to Testing Mean Reversion in the Swedish Stock Market

  • Graflund, Andreas


    (Department of Economics, Lund University)

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    In this paper we use a Bayesian approach to test for mean reversion in the Swedish stock market on monthly data 1918-1998. By simply account for the heteroscedasticty of the data with a two state hidden Markov model of normal distributions and taking estimation bias into account via Gibbs sampling we can find no support of mean reversion. This is a contradiction to previous result from Sweden. Our findings suggest that the Swedish stock market can be characterized by two regimes, a tranquil and a volatile, and within the regimes the stock market is random. This finding of randomness is in line with recent evidence for the U.S stock market.

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    Paper provided by Lund University, Department of Economics in its series Working Papers with number 2000:8.

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    Length: 21 pages
    Date of creation: 03 Oct 2000
    Date of revision: 09 Nov 2000
    Handle: RePEc:hhs:lunewp:2000_008
    Contact details of provider: Postal: Department of Economics, School of Economics and Management, Lund University, Box 7082, S-220 07 Lund,Sweden
    Phone: +46 +46 222 0000
    Fax: +46 +46 2224613
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    1. Lo, Andrew W. & MacKinlay, A. Craig, 1989. "The size and power of the variance ratio test in finite samples : A Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 40(2), pages 203-238, February.
    2. Berg, Lennart & Lyhagen, Johan, 1996. "Short and Long Run Dependence in Swedish Stock Returns," Working Paper Series 1996:19, Uppsala University, Department of Economics.
    3. Malliaropulos, Dimitrios & Priestley, Richard, 1999. "Mean reversion in Southeast Asian stock markets," Journal of Empirical Finance, Elsevier, vol. 6(4), pages 355-384, October.
    4. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    5. Myung Jig Kim & Charles R. Nelson & Richard Startz, 1991. "Mean Reversion in Stock Prices? A Reappraisal of the Empirical Evidence," Review of Economic Studies, Oxford University Press, vol. 58(3), pages 515-528.
    6. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    7. Billio, M. & Monfort, A. & Robert, C. P., 1999. "Bayesian estimation of switching ARMA models," Journal of Econometrics, Elsevier, vol. 93(2), pages 229-255, December.
    8. Kim, Chang-Jin & Nelson, Charles R., 1998. "Testing for mean reversion in heteroskedastic data II: Autoregression tests based on Gibbs-sampling-augmented randomization1," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 385-396, October.
    9. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
    10. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
    11. Cochrane, John H, 1988. "How Big Is the Random Walk in GNP?," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 893-920, October.
    12. repec:bla:restud:v:58:y:1991:i:3:p:515-28 is not listed on IDEAS
    13. Kim, Chang-Jin & Nelson, Charles R. & Startz, Richard, 1998. "Testing for mean reversion in heteroskedastic data based on Gibbs-sampling-augmented randomization1," Journal of Empirical Finance, Elsevier, vol. 5(2), pages 131-154, June.
    14. Myung Jig Kim & Charles R. Nelson & Richard Startz, 1988. "Mean Reversion in Stock Prices? A Reappraisal of the Empirical Evidence," NBER Working Papers 2795, National Bureau of Economic Research, Inc.
    15. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
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