Loss-Given-Default Modeling by Post-Last Passage Time Process

TL;DR

This paper introduces a hybrid stochastic model for loss-given-default that combines firm-value and intensity-based approaches using last passage time of a diffusion process, providing explicit LGD distributions and practical estimation methods.

Contribution

It develops a novel hybrid LGD model based on last passage time, integrating different modeling approaches for improved default and LGD prediction.

Findings

Explicit distributions for default time and LGD derived

Estimation procedure demonstrated with real CDS market data

Model offers a flexible framework for credit risk analysis

Abstract

This study proposes a stochastic model for loss-given-default (LGD) which provides the LGD distribution based on credit market and company-specific financial conditions. The model utilizes last passage time of a linear diffusion (representing firm value) to a certain threshold point, after which default occurs as a surprising event. By treating the post-last passage time process in a continuum of the original process, we are able to use firm-value approach before and intensity-based approach after the last passage time, leading to a hybrid model. Under minimal and standard assumptions, we obtain the distributions of default time and LGD explicitly. We provide a computationally simple estimation procedure and real-world examples of estimated LGD distribution implied in CDS market.