When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
Abstract
This thesis explores robust risk-minimizing hedging strategies for contingent claims in incomplete markets with transaction costs, offering a spectrum of tools to balance risk and cost- effectiveness. Robust technique applications to finance and insurance have recently gained popularity due to their ability to mitigate model risk. Model risk arises when strategies (or models) become in and out of sync with the market. A model is robust if it can adapt to a wide range of market-dependent factors. However, robust models can be costly and computationally demanding, especially for complex financial and insurance products. Using a multidimensional event tree model, we employ the asymmetric norm as a semi-robust risk measure, integrating asymmetry for customized risk profiles. Three main strategies are developed: a super-replicating approach ensuring full claim coverage at a higher cost, the norm as constraint, which introduces controlled losses to reduce costs, and the norm as objective, minimizing losses directly to enhance capital efficiency. Additionally, self-financing strategies, which require no additional capital injections, offer cost-effective hedging, while portfolio as state variable strategies allow real-time adjustments, enhancing robustness un- der volatile conditions. Testing on European call options shows that semi-robust strategies - especially norm-constrained and self-financing approaches - maintain low tail risk with minimized cost, demonstrating versatility in adapting to diverse market conditions, investor goals, and risk tolerances while upholding robust risk control.