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Subordinations Levels in Structured Financing

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  • An, Xudong

    (U of Southern California)

  • Deng, Yongheng
  • Sanders, Anthony B.

    (Ohio State U)

Abstract

Subordination levels are of critical importance in the classic senior-subordinated structure for securitized financing (such as collateralized debt obligations and commercial mortgage-backed securities). Subordination levels determine the amount of credit support that the senior bonds (or tranches) require from the subordinated bonds (or tranches) and are provided by the rating agencies. Thus, ratings agencies play an important role in the pricing and risk management of structured finance products. The finance literature has numerous studies examining whether securities with higher risk (as predicted by asset pricing models, such as the CAPM) earn higher ex-post average returns. In a similar vein, it is of interest to examine whether securities (or tranches) with greater levels of subordination experience higher ex-post levels of delinquencies and default. In this paper, we examine whether bonds (or tranches) with greater levels of subordination do, in fact, experience higher ex-post levels of delinquencies and default. Recent studies have found that rating agencies follow a "learning by doing" approach in subordination structuring (Riddiough and Chiang, 2004). As expected, the rating agencies were conservative in the early stages with regard to subordination levels given the paucity of information about delinquencies, defaults and prepayments on loans. As time progresses and more information is available regarding loan performance, subordination levels adjusted to new levels. This paper focuses on cross sectional differences in subordination levels. We examine if this relationship between subordination and ex-post delinquencies and defaults is conforming to rational expectation.We perform both a deal level and a loan level analysis using commercial mortgage-backed securities (CMBS). Our results show that the expected loss for CMBS pools are a statistically significant factor in explaining both AAA and BBB bond subordinations; however, expected loss accounts for less than 30 percent of the variation. Even considering the rating agencies' practice of incorporating differences in loan terms, borrower quality, deal structural and information quality into their subordination structure, the empirical fit is still too low. These findings indicate the difficulty in determining subordination levels apriori.

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Paper provided by Ohio State University, Charles A. Dice Center for Research in Financial Economics in its series Working Paper Series with number 2006-18.

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Date of creation: Aug 2006
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Handle: RePEc:ecl:ohidic:2006-18

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  1. Peter M. DeMarzo, 2005. "The Pooling and Tranching of Securities: A Model of Informed Intermediation," Review of Financial Studies, Society for Financial Studies, vol. 18(1), pages 1-35.
  2. Peter DeMarzo & Darrell Duffie, 1999. "A Liquidity-Based Model of Security Design," Econometrica, Econometric Society, vol. 67(1), pages 65-100, January.
  3. Ciochetti, Brian A, et al, 2003. "A Proportional Hazards Model of Commercial Mortgage Default with Originator Bias," The Journal of Real Estate Finance and Economics, Springer, vol. 27(1), pages 5-23, July.
  4. Yongheng Deng & John M. Quigley & Robert Van Order, . "Mortgage Terminations, Heterogeneity and the Exercise of Mortgage Options," Zell/Lurie Center Working Papers 322, Wharton School Samuel Zell and Robert Lurie Real Estate Center, University of Pennsylvania.
  5. Jun Chen & Yongheng Deng, 2003. "Commercial Mortgage Workout Strategy and Conditional Default Probability: Evidence from Special Serviced CMBS Loans," Working Paper 8614, USC Lusk Center for Real Estate.
  6. Ambrose, Brent W & Sanders, Anthony B, 2003. "Commercial Mortgage-Backed Securities: Prepayment and Default," The Journal of Real Estate Finance and Economics, Springer, vol. 26(2-3), pages 179-96, March-May.
  7. Riddiough, Timothy J., 1997. "Optimal Design and Governance of Asset-Backed Securities," Journal of Financial Intermediation, Elsevier, vol. 6(2), pages 121-152, April.
  8. Jun Chen & Yongheng Deng, 2013. "Commercial Mortgage Workout Strategy and Conditional Default Probability: Evidence from Special Serviced CMBS Loans," The Journal of Real Estate Finance and Economics, Springer, vol. 46(4), pages 609-632, May.
  9. Brian A. Ciochetti & Yongheng Deng & Bin Gao & Rui Yao, 2002. "The Termination of Commercial Mortgage Contracts through Prepayment and Default: A Proportional Hazard Approach with Competing Risks," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 30(4), pages 595-633.
  10. Quigley, John M., 2006. "Urban Economics," Berkeley Program on Housing and Urban Policy, Working Paper Series qt0jr0p2tk, Berkeley Program on Housing and Urban Policy.
  11. Kerry D. Vandell & Walter Barnes & David Hartzell & Dennis Kraft & William Wendt, 1993. "Commercial Mortgage Defaults: Proportional Hazards Estimation Using Individual Loan Histories," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 21(4), pages 451-480.
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Cited by:
  1. An, Xudong & Deng, Yongheng & Gabriel, Stuart A., 2011. "Asymmetric information, adverse selection, and the pricing of CMBS," Journal of Financial Economics, Elsevier, vol. 100(2), pages 304-325, May.

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