Bivariate sub-Gaussian model for stock index returns

Matylda Jabłońska-Sabuka, Marek Teuerle and Agnieszka Wyłomańska

Physica A: Statistical Mechanics and its Applications, 2017, vol. 486, issue C, 628-637

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.

Keywords: Nonparametric methods; Characteristic function; Bivariate sub-Gaussian distribution; α-stable process (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:486:y:2017:i:c:p:628-637

DOI: 10.1016/j.physa.2017.05.080

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