Prequential analysis of stock market returns
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.
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Volume (Year): 36 (2004)
Issue (Month): 5 ()
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- Robert J. Shiller & John Y. Campbell, 1986.
"The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors,"
Cowles Foundation Discussion Papers
812, Cowles Foundation for Research in Economics, Yale University.
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"A Variance Decomposition for Stock Returns,"
3207695, Harvard University Department of Economics.
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- 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.
- Covey, Ted & Bessler, David A, 1992. "Testing for Granger's Full Causality," The Review of Economics and Statistics, MIT Press, vol. 74(1), pages 146-53, February.
- 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 477-99, October.
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