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Forecasting Fundamental Asset Return Distributions and Tests for Excess Volatility and Bubbles

Author

Listed:
  • Donaldson, R.G.
  • Kamstra, M.

Abstract

This paper develops an augmented Artificial Neural Network forecast-simulation procedure for estimating both the current fundamental price of a financial asset and the state-dependent distribution (including volatilities) from which future returns will be fundamentally drawn. The results provide an improved method for valuing assets, such as stocks and stock options,and suggest new applications of tests for excess volatility and bubbles in asset prices.

Suggested Citation

  • Donaldson, R.G. & Kamstra, M., 1996. "Forecasting Fundamental Asset Return Distributions and Tests for Excess Volatility and Bubbles," Discussion Papers dp96-02, Department of Economics, Simon Fraser University.
    • Handle: RePEc:sfu:sfudps:dp96-02
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    Citations

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    Cited by:

    1. Lucy Ackert & William Hunter, 2001. "An Empirical Examination of the Price-Dividend Relation with Dividend Management," Journal of Financial Services Research, Springer;Western Finance Association, vol. 19(2), pages 115-129, April.

    More about this item

    Keywords

    TESTS; FINANCIAL MARKET; STOCK MARKET; SHARES;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • N22 - Economic History - - Financial Markets and Institutions - - - U.S.; Canada: 1913-

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