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• W2624941750 endingPage "637" @default.
• W2624941750 startingPage "628" @default.
• W2624941750 abstract "Financial time series are commonly modeled with methods assuming data normality. However, the real distribution can be nontrivial, also not having an explicitly formulated probability density function. In this work we introduce novel parameter estimation and high-powered distribution testing methods which do not rely on closed form densities, but use the characteristic functions for comparison. The approach applied to a pair of stock index returns demonstrates that such a bivariate vector can be a sample coming from a bivariate sub-Gaussian distribution. The methods presented here can be applied to any nontrivially distributed financial data, among others." @default.
• W2624941750 created "2017-06-23" @default.
• W2624941750 creator A5046463747 @default.
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• W2624941750 date "2017-11-01" @default.
• W2624941750 modified "2023-09-30" @default.
• W2624941750 title "Bivariate sub-Gaussian model for stock index returns" @default.
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• W2624941750 doi "https://doi.org/10.1016/j.physa.2017.05.080" @default.
• W2624941750 hasPublicationYear "2017" @default.
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