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Abstract

Control theory has gained a widespread use in almost every area of decision making problems. In this thesis, we seek to construct a premium setting strategy and an asset allocation strategy of a non-life insurance company whose goal is to maximise a metric of her utility function.

As insurance companies do not have perfect insight into future market and cannot assume any given scenario with certainty, stochasticity is introduced to model the market conditions and the risk processes that the running of the insurance business is subject to.

The problem is formulated as a continuous time and continuous space control problem where the state process is controlled continuously in a way to achieve the target. Bellman optimality principle in a stochastic environment is used to reduce the continuous time decision problem into a fixed point decision problem under the umbrella of Hamilton- Jacobi-Bellman equation.

We also consider the pricing of financial derivative products written on catastrophe losses. Since the market of catastrophe insurance is incomplete, we make use of the concept of indifference of utility theory of a market participant to derive the so-called affordable price.

Publication Type:	Thesis (Doctoral)
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HG Finance
Departments:	Bayes Business School > Actuarial Science & Insurance > Statistical Research Reports Bayes Business School > Bayes Business School Doctoral Theses Doctoral Theses

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