Random variate generation for exponential and gamma tilted stable distributions

Yan Qu, Angelos Dassios and Hongbiao Zhao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We develop a new efficient simulation scheme for sampling two families of tilted stable distributions: exponential tilted stable (ETS) and gamma tilted stable (GTS) distributions. Our scheme is based on two-dimensional single rejection. For the ETS family, its complexity is uniformly bounded over all ranges of parameters. This new algorithm outperforms all existing schemes. In particular, it is more efficient than the well-known double rejection scheme, which is the only algorithm with uniformly bounded complexity that we can find in the current literature. Beside the ETS family, our scheme is also flexible to be further extended for generating the GTS family, which cannot easily be done by extending the double rejection scheme. Our algorithms are straightforward to implement, and numerical experiments and tests are conducted to demonstrate the accuracy and efficiency.

Keywords: exponentially tilted stable distribution; gamma tilted stable distribution; exact Simulation Algorithms; Monte Carlo simulation; random variate generation; two-dimensional single rejection; tempered stable distribution; Lévy process (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2021-10-01
New Economics Papers: this item is included in nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in ACM Transactions on Modeling and Computer Simulation, 1, October, 2021, 31(4). ISSN: 1049-3301

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:108593

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