Moment-matching approximations for stochastic sums in non-Gaussian Ornstein–Uhlenbeck models

Riccardo Brignone, Ioannis Kyriakou and Gianluca Fusai

Insurance: Mathematics and Economics, 2021, vol. 96, issue C, 232-247

Abstract: In this paper, we recall actuarial and financial applications of sums of dependent random variables that follow a non-Gaussian mean-reverting process and contemplate distribution approximations. Our work complements previous related studies restricted to lognormal random variables; we revisit previous approximations and suggest new ones. We then derive moment-based distribution approximations for random sums attuned to Asian option pricing and computation of risk measures of random annuities. Various numerical experiments highlight the speed–accuracy benefits of the proposed methods.

Keywords: Mean reversion; Non-Gaussian processes; Moment-matching; Asian option valuation; Stochastic annuities (search for similar items in EconPapers)
JEL-codes: C15 C63 G13 G22 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167668720301645
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:96:y:2021:i:c:p:232-247

DOI: 10.1016/j.insmatheco.2020.12.002

Access Statistics for this article

Insurance: Mathematics and Economics is currently edited by R. Kaas, Hansjoerg Albrecher, M. J. Goovaerts and E. S. W. Shiu

More articles in Insurance: Mathematics and Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().