Abstract
ABSTRACTIt is widely recognized that financial data distributions have heavier tails than normal distributions. These tails can be modeled as Student distributions and normal mixture distributions. However, selecting between these distributions has not been previously studied. To this end, this study proposes an information-criterion approach. The efficacy of the proposed method for selecting the number of mixture components is examined via simulation studies that compare the conventional hypothesis test. Empirical results using Japanese stock returns data are also provided.
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