On infectious models for dependent default risk

Modeling dependent defaults is a key issue in risk measurement and management. In this paper, we introduce a Markovian infectious model to describe the dependent relationship of default processes of credit entities. The key idea of the proposed model is based on the concept of common shocks adopted in the insurance industry. We compare the proposed model to both one-sector and two-sector models considered in the credit literature using real default data. A log-likelihood ratio test is applied to compare the goodness-of- fit of the proposed model. Our empirical results reveal that the proposed model outperforms both the one-sector and two-sector models. © 2011 IEEE.published_or_final_versionThe 4th International Joint Conference on Computational Sciences and Optimization (CSO 2011), Yunnan, China, 15-19 April 2011. In Proceedings of the 4th CSO, 2011, p. 1196-120

A Markovian Model for Default Risk in a Network of Sectors

The 2nd IEEE International Conference on Business Intelligence and Financial Engineering (BIFE 2009), Beijing, 24-26, July 2009In this paper, we study the problem of modeling the dependence of defaults in different sectors. We consider multiple default data sequences as a network and model them by using a Markov chain model. The new network model allows us to compute two important risk measures, namely, Value-at-Risk (VaR) and Expected Shortfall (ES). Numerical experiments are given to illustrate the practical implementation of the model. We also perform empirical studies of the model using real default data sequences and analyze the empirical behaviors of the risk measures arising from the model. © 2009 IEEE.link_to_subscribed_fulltex