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Modelling daily returns of New York Stock Exchange by time series with noises having stable distributions Wang, Xiaohua


This thesis provides some modelling procedures for the New York Stock Exchange (NYSE) daily data, using stable distributions. The use of stable distributions was motivated by the fact that traditional normality assumptions are not appropriate for the daily stock returns. Four selected series, two stock indices and two individual industrial returns, were examined. Estimates for the characteristic exponent parameter α were obtained. Following the estimation of the exponent parameter α, serial-dependence was studied by fitting autoregressive models, using two different minimization criteria. The final conclusions are the following. The four daily stock returns were stably distributed with characteristic exponent α less than two. The time series behavior of these data was suitably described by autoregressive models.

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