Tempered stable distributions and processes in finance: numerical analysis

Michele Leonardo Bianchi, Svetlozar T. Rachev, Young Shin Kim and Frank J. Fabozzi
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Michele Leonardo Bianchi: Bank of Italy, Specialized Intermediaries Supervision Department
Svetlozar T. Rachev: University of Karlsruhe and KIT, School of Economics and Business Engineering
Young Shin Kim: University of Karlsruhe and KIT, School of Economics and Business Engineering
Frank J. Fabozzi: Yale School of Management

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2010, pp 33-42 from Springer

Abstract: Abstract Most of the important models in finance rest on the assumption that randomness is explained through a normal random variable. However there is ample empirical evidence againstthe normality assumption, since stockreturns are heavy-tailed, leptokurtic and skewed. Partly in response to those empirical inconsistencies relative to the properties of the normal distribution, a suitable alternative distribution is the family of tempered stable distributions. In general, the use of infinitely divisible distributions is obstructed the difficulty of calibrating and simulating them. In this paper, we address some numerical issues resulting from tempered stable modelling, with a view toward the density approximation and simulation.

Keywords: stable distribution; tempered stable distributions; Monte Carlo (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-88-470-1481-7_4

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DOI: 10.1007/978-88-470-1481-7_4

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