Asymptotics for a discrete-time risk model with Gamma-like insurance risks
Yang Yang and
Kam C. Yuen
Scandinavian Actuarial Journal, 2016, vol. 2016, issue 6, 565-579
Abstract:
Consider a discrete-time insurance risk model with insurance and financial risks. Within period i$ i $, the net insurance loss is denoted by Xi$ X_i $ and the stochastic discount factor over the same time period is denoted by Yi$ Y_i $. Assume that {Xi,i≥1}$ \{X_i,\ i \ge 1\} $ form a sequence of independent and identically distributed real-valued random variables with common distribution F$ F $; {Yi,i≥1}$ \{Y_i,\ i \ge 1\} $ are another sequence of independent and identically distributed positive random variables with common distribution G$ G $; and the two sequences are mutually independent. Under the assumptions that F$ F $ is Gamma-like tailed and G$ G $ has a finite upper endpoint, we derive some precise formulas for the tail probability of the present value of aggregate net losses and the finite-time and infinite-time ruin probabilities. As an extension, a dependent risk model is considered, where each random pair of the net loss and the discount factor follows a bivariate Sarmanov distribution.
Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/03461238.2015.1004802 (text/html)
Access to full text is restricted to subscribers.
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:taf:sactxx:v:2016:y:2016:i:6:p:565-579
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/sact20
DOI: 10.1080/03461238.2015.1004802
Access Statistics for this article
Scandinavian Actuarial Journal is currently edited by Boualem Djehiche
More articles in Scandinavian Actuarial Journal from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().