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An empirical comparison of alternative credit default swap pricing models

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  • Michele Leonardo Bianchi

    (Bank of Italy)

Abstract

Most of the important models in finance rest on the assumption that randomness is explained through a normal random variable because, in general, the use of alternative models is obstructed by the difficulty of calibrating and simulating them. In this paper, we empirically study models for pricing credit default swaps under a reduced-form framework, assuming different dynamics for the default intensity process. After reviewing the most recent results on this subject, we explore both pricing performance and parameter stability during the highly volatile period from 30 June 2008 to 31 December 2010 for different classes of processes: one driven by the Brownian motion, three driven by non-Gaussian L�vy processes, and the last one driven by a Sato process. The models are analysed from both a static and dynamic perspective.

Suggested Citation

  • Michele Leonardo Bianchi, 2012. "An empirical comparison of alternative credit default swap pricing models," Temi di discussione (Economic working papers) 882, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:wptemi:td_882_12
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    References listed on IDEAS

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    1. Sergio Mayordomo & Juan Ignacio Peña & Eduardo S. Schwartz, 2014. "Are All Credit Default Swap Databases Equal?," European Financial Management, European Financial Management Association, vol. 20(4), pages 677-713, September.
    2. Ernst Eberlein & Sebastian Raible, 1999. "Term Structure Models Driven by General Lévy Processes," Mathematical Finance, Wiley Blackwell, vol. 9(1), pages 31-53, January.
    3. Robert A. Jarrow, 2009. "Credit Risk Models," Annual Review of Financial Economics, Annual Reviews, vol. 1(1), pages 37-68, November.
    4. Li, Junye, 2011. "Sequential Bayesian Analysis of Time-Changed Infinite Activity Derivatives Pricing Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 468-480.
    5. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    6. Robert A. Jarrow & David Lando & Fan Yu, 2008. "Default Risk And Diversification: Theory And Empirical Implications," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 19, pages 455-480, World Scientific Publishing Co. Pte. Ltd..
    7. repec:dau:papers:123456789/1380 is not listed on IDEAS
    8. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    9. Duan, Jin-Chuan & Simonato, Jean-Guy, 1999. "Estimating and Testing Exponential-Affine Term Structure Models by Kalman Filter," Review of Quantitative Finance and Accounting, Springer, vol. 13(2), pages 111-135, September.
    10. Rama Cont, 2010. "Credit default swaps and financial stability," Post-Print hal-00545742, HAL.
    11. Hull, John & Predescu, Mirela & White, Alan, 2004. "The relationship between credit default swap spreads, bond yields, and credit rating announcements," Journal of Banking & Finance, Elsevier, vol. 28(11), pages 2789-2811, November.
    12. Kwamie Dunbar, 2008. "US corporate default swap valuation: the market liquidity hypothesis and autonomous credit risk," Quantitative Finance, Taylor & Francis Journals, vol. 8(3), pages 321-334.
    13. Damiano Brigo & Naoufel El-Bachir, 2007. "An exact formula for default swaptions' pricing in the SSRJD stochastic intensity model," ICMA Centre Discussion Papers in Finance icma-dp2007-14, Henley Business School, University of Reading.
    14. Chen, Ren-Raw & Cheng, Xiaolin & Fabozzi, Frank J. & Liu, Bo, 2008. "An Explicit, Multi-Factor Credit Default Swap Pricing Model with Correlated Factors," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(1), pages 123-160, March.
    15. Duan, Jin-Chuan & Fulop, Andras, 2009. "Estimating the structural credit risk model when equity prices are contaminated by trading noises," Journal of Econometrics, Elsevier, vol. 150(2), pages 288-296, June.
    16. Helyette Geman & C. Peter M. Dilip Y. Marc, 2007. "Self decomposability and option pricing," Post-Print halshs-00144193, HAL.
    17. Fred Espen Benth & Jan Kallsen & Thilo Meyer-Brandis, 2007. "A Non-Gaussian Ornstein-Uhlenbeck Process for Electricity Spot Price Modeling and Derivatives Pricing," Applied Mathematical Finance, Taylor & Francis Journals, vol. 14(2), pages 153-169.
    18. Berg, Tobias, 2010. "The term structure of risk premia: new evidence from the financial crisis," Working Paper Series 1165, European Central Bank.
    19. Cont, R., 2010. "Credit default swaps and financial stability," Financial Stability Review, Banque de France, issue 14, pages 35-43, July.
    20. Malik, S. & Pitt, M. K., 2011. "Modelling Stochastic Volatility with Leverage and Jumps: A Simulated Maximum Likelihood Approach via Particle Filtering," Working papers 318, Banque de France.
    21. Jarrow, Robert A., 2011. "Credit market equilibrium theory and evidence: Revisiting the structural versus reduced form credit risk model debate," Finance Research Letters, Elsevier, vol. 8(1), pages 2-7, March.
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    More about this item

    Keywords

    credit default swap; Cox-Ingersoll-Ross; non-Gaussian Ornstein-Uhlenbeck processes; L�vy processes; Sato processes; filtering methods; unscented Kalman filter; particle filter;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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