Fitting Financial Returns Distributions: A Mixture Normality Approach

Riccardo Bramante () and Diego Zappa ()
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Riccardo Bramante: University Cattolica del Sacro Cuore, Department of Statistical Sciences
Diego Zappa: University Cattolica del Sacro Cuore, Department of Statistical Sciences

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2014, pp 81-88 from Springer

Abstract: Abstract An important research field in finance is the identification of probability distribution model that fits at the best the empirical distribution of time series returns. In this paper we propose the use of mixtures of truncated normal distributions in modelling returns. An optimization algorithm has been developed to obtain the best fit by using the minimum distance approach. Empirical results show evidence of the capability of the method to fit return distributions at a satisfactory level, completely maintaining local normality properties in the model. Moreover, the model provides a good tail fit thus improving the accuracy of Value at Risk estimates.

Keywords: Minimum Approach Distance; Return Time Series; Probability Distribution Model; Benchmark Relationship; Morgan Stanley Capital International (MSCI) (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-02499-8_7

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DOI: 10.1007/978-3-319-02499-8_7

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