TITLE:
Multi-Name Extension to the Credit Grades and an Efficient Monte Carlo Method
AUTHORS:
Hideyuki Takada
KEYWORDS:
Credit Risk, Default Contagion, Monte Carlo Method, Interacting Particle System
JOURNAL NAME:
Journal of Mathematical Finance,
Vol.4 No.3,
May
28,
2014
ABSTRACT:
In this paper, we present a multi-name incomplete information structural model which possess the contagion mechanism and its efficient Monte Carlo algorithm based on Interacting Particle System. Along with the Credit Grades, which is industrially used single-name credit model, we suppose that investors can observe firm values and defaults but are not informed of the threshold level at which a firm is deemed to default. Additionally, in order to model the possibility of crisis normalization, we introduce the concept of memory period after default. During the memory period after a default, public investors remember when the previous default occurred and directly reflect that information for updating their belief. When the memory period after a default finish, investors forget about that default and shift their interest to recent defaults if exist. One of the variance reduction techniques, relying upon Interacting Particle System, is combined with the standard Monte Carlo method to address the rare but critical events represented by the tail of loss distribution of portfolio.