Modeling The Highly Improbable Events And Their Large Impact Is It Exponetial Or Power Law Driven
Author: Saleem Khan

We study the distribution of fluctuations of daily both aggregated returns of 33 KSE stocks and individually stock and index for the period of june-2004 to Feb-2012. We presents evidence that econometric techniques based on normality assumption cannot be trusted in true fat-tailed distribution. The result is un-computability of role of tail events where one single observation explain 99% of total kurtosis properties. It also classifies decision payoffs in two types: simple payoffs (true/false or binary) and complex (higher moments); and randomness into type-1 (thin tails) and type-2 (true fat tails). The Fourth Quadrant is where payoffs are complex with type-2randomness. We find that both positive and negative tail follow power law and tail exponent ( ̂ >2) lie outside the levy stable regime, but not consistent with universal cubic law and shows asymmetry. To test the robustness of the result we perform two hypothesis testing test Goodness –of-fit test and comparing distribution and find support for asymptotic power law against the exponential model which some classical study support for developing countries Supervisor:- Dr. Zahid Asghar

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Keywords : Improbable Events, Parameters uncertainty, Power Law Hypothesis
Supervisor: Zahid Asghar

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