Optimal surrender strategies for equity-indexed annuity investors with partial information

Jiaqin Wei, Rongming Wang, Hailiang Yang

Research output: Contribution to journalArticleResearchpeer-review

Abstract

In this paper we consider an equity-indexed annuity (EIA) investor who wants to determine when he should surrender the EIA in order to maximize his logarithmic utility of the wealth at surrender time. We model the dynamics of the index using a geometric Brownian motion with regime switching. To be more realistic, we consider a finite time horizon and assume that the Markov chain is unobservable. This leads to the optimal stopping problem with partial information. We give a representation of the value function and an integral equation satisfied by the boundary. In the Bayesian case which is a special case of our model, we obtain analytical results for the value function and the boundary.
LanguageEnglish
Pages1251-1258
Number of pages8
JournalStatistics and Probability Letters
Volume82
Issue number7
DOIs
Publication statusPublished - 2012
Externally publishedYes

Fingerprint

Partial Information
Equity
Optimal Strategy
Value Function
Geometric Brownian Motion
Regime Switching
Optimal Stopping Problem
Horizon
Markov chain
Integral Equations
Logarithmic
Maximise
Model
Value function
Investors
Partial information
Equity-indexed annuities
Time horizon
Integral equations
Geometric Brownian motion

Keywords

  • Equity-indexed annuity
  • Regime switching
  • Optimal stopping
  • Partial information
  • Logarithmic utility

Cite this

Wei, Jiaqin ; Wang, Rongming ; Yang, Hailiang. / Optimal surrender strategies for equity-indexed annuity investors with partial information. In: Statistics and Probability Letters. 2012 ; Vol. 82, No. 7. pp. 1251-1258.
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Optimal surrender strategies for equity-indexed annuity investors with partial information. / Wei, Jiaqin; Wang, Rongming; Yang, Hailiang.

In: Statistics and Probability Letters, Vol. 82, No. 7, 2012, p. 1251-1258.

Research output: Contribution to journalArticleResearchpeer-review

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