Date of Award


Degree Type

Doctoral Dissertation

Degree Name

Doctor of Business Administration (DBA)


Jack Welch College of Business


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.

Dissertation Supervisor

Dr. Lorán Chollete

Committee Member

Dr. Arpita Chatterjee

Committee Member

Dr. W. Keener Hughen


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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.



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