Date of Award
9-24-2021
Degree Type
Doctoral Dissertation
Degree Name
Doctor of Business Administration (DBA)
Department
Jack Welch College of Business
Dissertation Supervisor
Dr. Lorán Chollete
Committee Member
Dr. Arpita Chatterjee
Committee Member
Dr. W. Keener Hughen
Abstract
Catastrophe (CAT) bond pricing is a challenging task due to the uncertainty inherent in the incomplete market setting in which they operate as such various pricing approaches have been proposed. In this paper, we offer an alternative Bayesian methodology which is a natural approach in the context of uncertainty. Our Bayesian model is highly flexible and can be implemented under different model assumptions without losing generalization. We develop an entire Bayesian framework to model the two fundamental sources of risks in CAT bond pricing – catastrophe and interest rate risks. Using a Hierarchical Dirichlet Process model (Teh et al., 2006), we model the collective catastrophe risk via a model-based clustering approach. Interest rate risk is modeled as a CIR process via the Bayesian approach. We can account for parameter and model uncertainties through these models, which leads to more reliable CAT bond prices. Finally, we use the models to find prices, present values, and risk premia of CAT bond contracts corresponding to different grouped risk profiles via several numerical examples.
JEL Classification
C11, G13, C14, E43, D81
Recommended Citation
Nkwantabisa, D. K. (2021). A Bayesian approach to assessing the risk rremium on catastrophe bond derivatives at issuance. Jack Welch College of Business & Technology dissertation, Sacred Heart University, Fairfield CT. Retrieved from https://digitalcommons.sacredheart.edu/wcob_theses/22/
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Submitted In partial fulfillment of the requirements for the degree of Doctor of Business Administration in Finance, Sacred Heart University, Jack Welch College of Business and Technology, Fairfield, Connecticut, September 24, 2021.