BAYESIAN RISK MEASURES FOR DERIVATIVES VIA RANDOM ESSCHER TRANSFORM

Tak Kuen Siu, Howell Tong, Hailiang Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

This paper proposes a model for measuring risks for derivatives that is easy to implement and
satisfies a set of four coherent properties introduced in Artzner et al. (1999). We construct our
model within the context of Gerber-Shiu’s option-pricing framework. A new concept, namely
Bayesian Esscher scenarios, which extends the concept of generalized scenarios, is introduced via
a random Esscher transform. Our risk measure involves the use of the risk-neutral Bayesian Esscher
scenario for pricing and a family of real-world Bayesian Esscher scenarios for risk measurement.
Closed-form expressions for our risk measure can be obtained in some special cases.
Original languageEnglish
Pages (from-to)78-91
JournalNorth American Actuarial Journal
Volume5
Issue number3
DOIs
Publication statusPublished - 2001
Externally publishedYes

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