Mean Variance, Expected Utility and Ruin Probability in Reinsurance Decisions: Suggestions and Comments on the Line of De Finetti’s Seminal Work
Luciano Daboni and
Flavio Pressacco
Additional contact information
Luciano Daboni: University of Trieste, Department of Applied Mathematics B. de Finetti
Flavio Pressacco: University of Trieste, Department of Applied Mathematics B. de Finetti
A chapter in Probability and Bayesian Statistics, 1987, pp 121-128 from Springer
Abstract:
Abstract Roughly speaking risk theory in insurance concerns the survival of (a branch of) an insurance company over some specified time horizon.* The key goal variable is usually the ruin probability of the company along that time horizon. While practical everyday problems involve mid term (e.g. five or ten years) horizon, theoretical models are mainly concerned with single period problems, or at the other extreme with (asymptotic) evaluations over an infinite time horizon. The usually relevant control variables are the initial reserve fund and the safety loading coefficient placed to obtain tariff insurance premiums. A third prominent control variable, sometimes implicitly considered, is the reinsurance strategy of the firm.
Keywords: Risk Tolerance; Portfolio Selection Problem; Risk Theory; Retention Strategy; Exponential Utility (search for similar items in EconPapers)
Date: 1987
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
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:spr:sprchp:978-1-4613-1885-9_12
Ordering information: This item can be ordered from
http://www.springer.com/9781461318859
DOI: 10.1007/978-1-4613-1885-9_12
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().