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https://doi.org/10.1007/s11146-011-9315-2
Title: | Default Clustering Risks in Commercial Mortgage-Backed Securities | Authors: | Fan, G.-Z. Sing, T.F. Ong, S.E. |
Keywords: | Counterparty risk Default cluster Intensity model Subordination structure |
Issue Date: | 2012 | Citation: | Fan, G.-Z., Sing, T.F., Ong, S.E. (2012). Default Clustering Risks in Commercial Mortgage-Backed Securities. Journal of Real Estate Finance and Economics 45 (1) : 110-127. ScholarBank@NUS Repository. https://doi.org/10.1007/s11146-011-9315-2 | Abstract: | This paper proposes an intensity-based pricing model with default dependence structure for CMBS bonds. Three features are incorporated into the proposed model. First, default is a Poisson jump process defined by a function of mortgage rating information. Second, property risks are modeled using a high dimensional Brownian motion process that captures both systematic risk and idiosyncratic risk in property value. Third, default dependence structure is built into the extended model. Based on a set of input parameters, we simulate various pricing effects on a hypothetical CMBS using the proposed model structure. The results of the base-line intensity model show that yield spreads on CMBS bonds increase in the recovery rate, but decreases in the hazard rate. Security structured with smaller subordination tranche exposes CMBS bonds to higher default risks. The model predicts that default clustering increases required yield spreads of CMBS bonds. At a 70% recovery rate and a 3% default hazard rate, yield spreads of Junior bonds are expected to increase by 169 basis points when counterparty risks increase by 50%. The results highlight the importance of clustering risks associated with counterparty default when valuing CMBS bonds. © 2011 Springer Science+Business Media, LLC. | Source Title: | Journal of Real Estate Finance and Economics | URI: | http://scholarbank.nus.edu.sg/handle/10635/46114 | ISSN: | 08955638 | DOI: | 10.1007/s11146-011-9315-2 |
Appears in Collections: | Staff Publications |
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