Shiu Fung Wong
- Position: -
- Load: PhD Student Full Time
- Principal supervisor: Dr Xian Zhou
- Associate supervisor: Professor Robert Elliott
- Date of submission: 27/09/2014
- Thesis title: An Efficient Valuation of Participating Insurance Contracts
- Purpose: To use an intensity-based model for portfolio credit risk using a collection of hidden Markov-modulated single jump processes.
- Originality: The default observations are single jump processes which contain very limited information. This is the first filtering scheme which provides practical implementation and results.
- Key Literature/theoretical perspective: The proposed model adopts a frailty-based approach to describe the dependent default risk, where firms are exposed to a common unobservable factor, namely, a frailty factor. We model this frailty factor by another continuous-time, finite-state, hidden Markov chain.
- Design/methodology/approach: A (robust) filter for the frailty factor is derived and a corresponding (robust) filter-based EM algorithm for the recursive estimates of unknown parameters is developed. We also evaluate the joint default probability of entities in a credit portfolio in the proposed model. Using the joint characteristic function of the occupation times of the chain and the inverse Fourier transform, a (semi)-analytical formula for the joint default probability of the constituents in the credit portfolio is derived, which can be evaluated based on observed default information.
- Findings: Hidden Markov-modulated model parameters, default intensities and filtered hidden Markov states are estimated in very high accuracy from limited information in the single-jump processes.
- Keywords: Intensity-Based Credit Model; Hidden Markov Chain; Frailty Model; Robust Filters; Joint Default Probability.
Name: Shiu Fung Wong
Department: Department of Actuarial Studies
Load: PhD Student Full Time
- Principal: Prof. Ken Siu
Email Address: email@example.com
An Efficient Valuation of Participating Insurance Contracts