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The Impact of Default Dependency and Collateralization on Asset Pricing and Credit Risk Modeling

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  • Xiao, Tim

Abstract

This article presents a comprehensive framework for valuing financial instruments subject to credit risk and collateralization. In particular, we focus on the impact of default dependence on asset pricing, as correlated default risk is one of the most pervasive threats to financial markets. Some well-known risky valuation models in the markets can be viewed as special cases of this framework. We introduce the concept of comvariance (or comrelation) into the area of credit risk modeling to capture the default relationship among three or more parties. Accounting for default correlations and comrelations becomes important, especially during the credit crisis. Moreover, we find that collateralization works well for financial instruments subject to bilateral credit risk, but fails for ones subject to multilateral credit risk.

Suggested Citation

  • Xiao, Tim, 2013. "The Impact of Default Dependency and Collateralization on Asset Pricing and Credit Risk Modeling," MPRA Paper 47136, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:47136
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    References listed on IDEAS

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    1. Michael Johannes & Suresh Sundaresan, 2007. "The Impact of Collateralization on Swap Rates," Journal of Finance, American Finance Association, vol. 62(1), pages 383-410, February.
    2. Alan V. Deardorff, 2011. "The General Validity of the Heckscher-Ohlin Theorem," World Scientific Book Chapters, in: Robert M Stern (ed.), Comparative Advantage, Growth, And The Gains From Trade And Globalization A Festschrift in Honor of Alan V Deardorff, chapter 11, pages 91-103, World Scientific Publishing Co. Pte. Ltd..
    3. Xiao, Tim, 2011. "An Efficient Lattice Algorithm for the LIBOR Market Model," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(1), pages 25-40.
    4. Robert A. Jarrow & Stuart M. Turnbull, 2008. "Pricing Derivatives on Financial Securities Subject to Credit Risk," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 17, pages 377-409, World Scientific Publishing Co. Pte. Ltd..
    5. Sanjiv R. Das & Darrell Duffie & Nikunj Kapadia & Leandro Saita, 2007. "Common Failings: How Corporate Defaults Are Correlated," Journal of Finance, American Finance Association, vol. 62(1), pages 93-117, February.
    6. Jean-Paul Laurent & Jon Gregory, 2005. "Basket default swaps, CDOs and factor copulas," Post-Print hal-03679517, HAL.
    7. Darrell Duffie & Andreas Eckner & Guillaume Horel & Leandro Saita, 2009. "Frailty Correlated Default," Journal of Finance, American Finance Association, vol. 64(5), pages 2089-2123, October.
    8. Jorion, Philippe & Zhang, Gaiyan, 2007. "Good and bad credit contagion: Evidence from credit default swaps," Journal of Financial Economics, Elsevier, vol. 84(3), pages 860-883, June.
    9. Koopman, Siem Jan & Lucas, André & Schwaab, Bernd, 2011. "Modeling frailty-correlated defaults using many macroeconomic covariates," Journal of Econometrics, Elsevier, vol. 162(2), pages 312-325, June.
    10. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    11. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    12. Arora, Navneet & Gandhi, Priyank & Longstaff, Francis A., 2012. "Counterparty credit risk and the credit default swap market," Journal of Financial Economics, Elsevier, vol. 103(2), pages 280-293.
    13. Teugels, Jozef L, 1990. "Some representations of the multivariate Bernoulli and binomial distributions," Journal of Multivariate Analysis, Elsevier, vol. 32(2), pages 256-268, February.
    14. Duffie, Darrell & Singleton, Kenneth J, 1999. "Modeling Term Structures of Defaultable Bonds," The Review of Financial Studies, Society for Financial Studies, vol. 12(4), pages 687-720.
    15. Giesecke, Kay, 2004. "Correlated default with incomplete information," Journal of Banking & Finance, Elsevier, vol. 28(7), pages 1521-1545, July.
    16. Lando, David & Nielsen, Mads Stenbo, 2010. "Correlation in corporate defaults: Contagion or conditional independence?," Journal of Financial Intermediation, Elsevier, vol. 19(3), pages 355-372, July.
    17. Robert A. Jarrow & Fan Yu, 2008. "Counterparty Risk and the Pricing of Defaultable Securities," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 20, pages 481-515, World Scientific Publishing Co. Pte. Ltd..
    18. Duffie, Darrell & Huang, Ming, 1996. "Swap Rates and Credit Quality," Journal of Finance, American Finance Association, vol. 51(3), pages 921-949, July.
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    Cited by:

    1. Xiao, Tim, 2013. "An Accurate Solution for Credit Value Adjustment (CVA) and Wrong Way Risk," MPRA Paper 47104, University Library of Munich, Germany.
    2. Tim Xiao, 2015. "Is the jump-diffusion model a good solution for credit risk modelling? The case of convertible bonds," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 4(1), pages 1-25.

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    More about this item

    Keywords

    asset pricing; credit risk modeling; unilateral; bilateral; multilateral credit risk; collateralization; comvariance; comrelation; correlation.;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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