Extending the intensity model with joint defaults to incorporate the lasting effects from common credit events

Daniel Wei‐Chung Miao, Xenos Chang‐Shuo Lin, Steve Hsin‐Ting Yu and Yung‐Hsin Lee

Applied Stochastic Models in Business and Industry, 2019, vol. 35, issue 3, 681-703

Abstract: This paper studies how the lasting effects of common credit events influence default probability distribution and the prices of multiname credit derivatives. Based on a joint defaults model where common credit events are used to generate simultaneous defaults, we extend the model to allow for their impacts to last for a longer while. The default intensity of each entity is heightened significantly while the impact still has an influence, until some time later when this effect fades away. Incorporating these lasting effects helps to generate higher default correlation, which is more consistent with today's highly correlated financial markets. The proposed model can be either formulated as a Markov chain or implemented by Monte Carlo simulation in order to calculate the default probability distributions and multiname derivatives prices. Our numerical results demonstrate the strong influences from the lasting effects and provide a justification of their incorporation.

Date: 2019
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https://doi.org/10.1002/asmb.2371

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:35:y:2019:i:3:p:681-703

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