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Prequential analysis of stock market returns


  • David Bessler
  • Robert Ruffley


The Brier score and a covariance partition due to Yates are considered to study the probabilistic forecasts of a vector autoregression on stock market returns. Probabilistic forecasts from a model and data developed by Campbell (1991) are studied with ordinary least squares. Calibration measures and the Brier score and its partition are used for model assessment. The partitions indicate that the ordinary least squares version of Campbell's model does not forecast stock market returns particularly well. While the model offers honest probabilistic forecasts (they are well-calibrated), the model shows little ability to sort events that occur into different groups from events that do not occur. The Yates-partition demonstrates this shortcoming. Calibration metrics do not.

Suggested Citation

  • David Bessler & Robert Ruffley, 2004. "Prequential analysis of stock market returns," Applied Economics, Taylor & Francis Journals, vol. 36(5), pages 399-412.
  • Handle: RePEc:taf:applec:v:36:y:2004:i:5:p:399-412
    DOI: 10.1080/00036840410001682115

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    References listed on IDEAS

    1. Campbell, John Y, 1991. "A Variance Decomposition for Stock Returns," Economic Journal, Royal Economic Society, vol. 101(405), pages 157-179, March.
    2. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    3. Covey, Ted & Bessler, David A, 1992. "Testing for Granger's Full Causality," The Review of Economics and Statistics, MIT Press, vol. 74(1), pages 146-153, February.
    4. Kling, John L & Bessler, David A, 1989. "Calibration-Based Predictive Distributions: An Application of Prequential Analysis to Interest Rates, Money, Prices, and Output," The Journal of Business, University of Chicago Press, vol. 62(4), pages 477-499, October.
    5. Zellner, Arnold & Hong, Chansik & Min, Chung-ki, 1991. "Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 275-304.
    6. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1999. "Multivariate Density Forecast Evaluation And Calibration In Financial Risk Management: High-Frequency Returns On Foreign Exchange," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 661-673, November.
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    Cited by:

    1. Casillas-Olvera, Gabriel & Bessler, David A., 2006. "Probability forecasting and central bank accountability," Journal of Policy Modeling, Elsevier, vol. 28(2), pages 223-234, February.
    2. Chen, Junyi & McCarl, Bruce A. & Price, Edwin & Wu, Ximing & Bessler, David A., 2016. "Climate as a Cause of Conflict: An Econometric Analysis," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 229783, Southern Agricultural Economics Association.
    3. Dharmasena, Senarath & Bessler, David & Capps, Oral. Jr, 2016. "On the Evaluation of Probability Forecasts: An Application to Qualitative Choice Models," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235424, Agricultural and Applied Economics Association.
    4. repec:eee:eneeco:v:65:y:2017:i:c:p:411-423 is not listed on IDEAS

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