Estimating the probability of large negative stock market
Correct assessment of the risks associated with likely economic outcomes is vital for effective decision making. The objective of investment in the stock market is to obtain positive market returns. The risk, however, is the danger of suffering large negative market returns. A variety of parametric models can be used in assessing this type of risk. A major disadvantage of these techniques is that they require a specific assumption to be made about the nature of the statistical distribution. Projections based on this method are conditional on the validity of this underlying assumption, which itself is not testable. An alternative approach is to use a non-parametric methodology, based on the statistical extreme value theory, which provides a means for evaluating the unconditional distribution (or at least the tails of this distribution) beyond the historically observed values. The methodology involves the calculation of the tail index, which is used to estimate the relevant exceedence probabilities (for different critical levels of loss) for a selection of food industry companies. Information about these downside risks is critically important for investment decision making. In addition, the tail index estimates permit examination of the stable Paretian hypothesis.
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- Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, vol. 36, pages 394.
- Sullivan, Ryan & Timmermann, Allan & White, Halbert, 1998. "Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns," University of California at San Diego, Economics Working Paper Series qt2z02z6d9, Department of Economics, UC San Diego.
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- Allan Timmermann & Halbert White & Ryan Sullivan, 1998. "The Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns," FMG Discussion Papers dp304, Financial Markets Group.
- Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-16, April.
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