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- W757627987 abstract "The exotic options that are examined in this thesis have a combination of non-standard characteristics which can be found in shout, multi-callable, pathdependent and Bermudan options. These options are called reset options. A reset option is an option which allows the holder to reset, one or more times, certain terms of the contract based on pre-specified rules during the life of the option. Overall in this thesis, an attempt has been made to tackle the modeling challenges that arise from the exotic properties of the reset option embedded in segregated funds. Initially, the relevant literature was reviewed and the lack of published work, advanced enough to deal with the complexities of the reset option, was identified. Hence, there appears to be a clear and urgent need to have more sophisticated approaches which will model the reset option. The reset option on the maturity guarantee of segregated funds is formulated as a non-stationary finite horizon Markov Decision Process. The returns from the underlying asset are modeled using a discrete time approximation of the lognormal model. An Optimal Exercise Boundary of the reset option is derived where a threshold value is depicted such that if the value of the underlying asset price exceeds it then it is optimal for the policyholder to reset his maturity guarantee. Otherwise, it is optimal for the policyholder to rollover his maturity guarantee. It is noteworthy that the model is able to depict the Optimal Exercise Boundary of not just the first but of all the segregated fund contracts which can be issued throughout the planning horizon of the policyholder. The main finding of the model is that as the segregated fund contract approaches its maturity, the threshold value in the Optimal Exercise Boundary" @default.
- W757627987 created "2016-06-24" @default.
- W757627987 creator A5030158058 @default.
- W757627987 date "2009-01-01" @default.
- W757627987 modified "2023-09-26" @default.
- W757627987 title "Modeling exotic options with maturity extensions by stochastic dynamic programming" @default.
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