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.
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Bianchi, M.L., Rachev, S.T., Kim, Y.S., Fabozzi, F.J. (2010). Tempered stable distributions and processes in finance: numerical analysis. In: Corazza, M., Pizzi, C. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer, Milano. https://doi.org/10.1007/978-88-470-1481-7_4
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DOI: https://doi.org/10.1007/978-88-470-1481-7_4
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