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

Brignone, R., Kyriakou, I. ORCID: 0000-0001-9592-596X & Fusai, G. ORCID: 0000-0001-9215-2586 (2020). Moment-matching approximations for stochastic sums in non-Gaussian Ornstein-Uhlenbeck models. Insurance: Mathematics and Economics, 96, pp. 232-247. doi: 10.1016/j.insmatheco.2020.12.002


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.

Publication Type: Article
Additional Information: © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Mean reversion, non-Gaussian processes, moment-matching, Asian option valuation, stochastic annuities
Subjects: H Social Sciences > HF Commerce
Q Science > QA Mathematics
Departments: Bayes Business School > Actuarial Science & Insurance
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