Skip to Content

Banner for HDR Expo 2012

Shiu Fung Wong

First Name: Shiu Fung
Surname: Wong
Department Dept of Applied Finance and Actuarial Studies
Supervisor(s): Tak Kuen Siu , Robert Elliott

Email Student

View Shiu Fung Wong's profile

Paper Title

Filtering a Hidden Markov-Modulated Intensity-Based Credit Risk Model

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.