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Estimating investors' behavior and errors in probabilistic forecasts by the Kolmogorov entropy and noise colors of non-hyperbolic attractors

Dominique, C-Rene (2013): Estimating investors' behavior and errors in probabilistic forecasts by the Kolmogorov entropy and noise colors of non-hyperbolic attractors.

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Abstract

This paper investigates the impact of the Kolmogorov-Sinai entropy on both the accuracy of probabilistic forecasts and the sluggishness of economic growth. It first posits the Gaussian process Zt (indexed by the Hurst exponent H) as the output of a reflexive dynamic input/output system governed by a non-hyperbolic of attractor. It next indexes families of attractors by the Hausdorff measure (D0) and assesses the uncertainty level plaguing probabilistic forecast in each family. The D0 signature of attractors is next applied to the S&P-500 Index The result allows the construction of the dynamic history of the index and establishes robust links between the Hausdorff dimension, investors’ behavior, and economic growth

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