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Pricing Credit Default Swaps Under Multifactor Reduced-Form Models: A Differential Quadrature Approach

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Abstract

We present a new numerical method for pricing credit default swaps under fully correlated multifactor reduced-form models. In particular, the proposed approach combines an implicit/explicit operator splitting procedure with the harmonic differential quadrature scheme, and is so efficient that it can be applied to models with up to six stochastic factors. This is a remarkable advantage, as we can use two factors to describe the interest rate, other two factors to describe the default probability, and other two factors to take into account, for example, the so-called counterparty risk. The performances of the novel method are demonstrated by extensive simulation, in which various kinds of models with four and six fully correlated factors are considered.

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References

  • Andritzky, J., & Singh, M. (2007). The pricing of credit default swaps during distress. Journal of Investment Management, 5, 1–17.

    Google Scholar 

  • Badaoui, S., Cathcart, L., & El-Jahel, L. (2013). Do sovereign credit default swaps represent a clean measure of sovereign default risk? A factor model approach. Journal of Banking & Finance, 37, 2392–2407.

    Article  Google Scholar 

  • Ballestra, L. V., Ottaviani, M., & Pacelli, G. (2012). An operator splitting harmonic differential quadrature approach to solve the Young’s model for life insurance risk. Insurance: Mathematics and Economics, 51, 442–448.

    Google Scholar 

  • Ballestra, L. V., & Pacelli, G. (2013). Pricing European and American options with two stochastic factors: A highly efficient radial basis function approach. Journal of Economic Dynamics & Control, 37, 1142–1167.

    Article  Google Scholar 

  • Bellman, R., Kashef, B. G., & Casti, J. (1972). Differential quadrature: A technique for the rapid solution of nonlinear partial differential equations. Journal of Computational Physics, 10, 40–52.

    Article  Google Scholar 

  • Bielecki, T. R., & Rutkowski, M. (2002). Credit risk: Modelling, valuation and hedging. Berlin: Springer.

    Google Scholar 

  • Brandes, A. J. (2009). A better way to understand the speculative use of credit default swaps. Stanford Journal of Law, Business and Finance, 14, 263–304.

    Google Scholar 

  • Brezinski, C., & Redivo Zaglia, M. (1991). Extrapolation methods: Theory and practice. Cambridge: Cambridge University Press.

    Google Scholar 

  • Brigo, D., & Alfonsi, A. (2005). Credit default swap calibration and derivatives pricing with the SSRD stochastic intensity model. Finance and Stochastics, 9, 29–42.

    Article  Google Scholar 

  • Brigo, D., & Chourdakis, K. (2009). Counterparty risk for credit default swaps: Impact of spread volatility and default correlation. International Journal of Theoretical and Applied Finance, 12, 1007–1026.

    Article  Google Scholar 

  • Brigo, D., & Mercurio, F. (2001). Interest rate models-theory and practice: With smile, inflation and credit. Berlin: Springer.

    Book  Google Scholar 

  • Chang, C. C., Chung, S. L., & Stapleton, R. C. (2007). Richardson extrapolation techniques for the pricing of American-style options. Journal of Futures Markets, 27, 791–817.

    Article  Google Scholar 

  • Che, Y. K., & Sethi, R. (2014). Credit market speculation and the cost of capital. American Economic Journal: Microeconomics, 6, 1–34.

    Google Scholar 

  • Chen, R. R., Cheng, X., Fabozzi, F., & Liu, B. (2008). An explicit, multi-factor credit default swap pricing model with correlated factors. Journal of Financial and Quantitative Analysis, 43, 123–160.

    Article  Google Scholar 

  • Chiarella, C., Fanelli, V., & Musti, S. (2011). Modelling the evolution of credit spreads using the Cox process within the HJM framework: A CDS option pricing model. European Journal of Operational Research, 208, 95–108.

    Article  Google Scholar 

  • Chung, S. L., & Shih, P. T. (2009). Static hedging and pricing American options. Journal of Banking & Finance, 33, 2140–2149.

    Article  Google Scholar 

  • Coudert, V., & Gex, M. (2010). The credit default swap market and the settlement of large defaults. International Economics, 123, 91–120.

    Article  Google Scholar 

  • D’Amico, G., Janssen, J., & Manca, R. (2007). Valuing credit default swap in a non-homogeneous semi-Markovian rating based model. Computational Economics, 29, 119–138.

    Article  Google Scholar 

  • Das, S. R., & Tufano, P. (1995). Pricing credit sensitive debt when interest rates, credit ratings and credit spreads are stochastic. Journal of Financial Engineering, 5, 161–198.

    Google Scholar 

  • Duffee, G. R. (1999). Estimating the price of default risk. Review of Financial Studies, 12, 197–226.

    Article  Google Scholar 

  • Duffie, D., & Singleton, K. J. (1997). An econometric model for the term structure of interest-rate swap yields. Journal of Finance, 52, 1287–1321.

    Article  Google Scholar 

  • Duffie, D., & Singleton, K. J. (1999). Modeling term structures of defaultable bonds. Review of Financial Studies, 12, 687–720.

    Article  Google Scholar 

  • Ericsson, J., Jacobs, K., & Oviedo, R. (2009). The determinants of credit default swap premia. Journal of Financial and Quantitative Analysis, 44, 109–132.

    Article  Google Scholar 

  • Feldhütter, P., & Lando, D. (2008). Decomposing swap spreads. Journal of Financial Economics, 88, 375–405.

    Article  Google Scholar 

  • Galil, K., Shapir, O. M., Amiran, D., & Ben-Zion, U. (2014). The determinants of CDS spreads. Journal of Banking & Finance, 41, 271–282.

    Article  Google Scholar 

  • Guarin, A., Liu, X., & Ng, W. L. (2011). Enhancing credit default swap valuation with meshfree methods. European Journal of Operational Research, 214, 805–813.

    Article  Google Scholar 

  • Guarin, A., Liu, X., & Ng, W. L. (2014). Recovering default risk from CDS spreads with a nonlinear filter. Journal of Economic Dynamics & Control, 38, 87–104.

    Article  Google Scholar 

  • Hull, J., Predescu, M., & White, A. (2004). The relationship between credit default swap spreads, bond yields, and credit rating announcements. Journal of Banking & Finance, 28, 2789–2811.

    Article  Google Scholar 

  • Hull, J., & White, A. (2001). Valuing credit default swaps II: Modeling default correlations. Journal of Derivatives, 8, 12–21.

    Article  Google Scholar 

  • Ikonen, S., & Toivanen, J. (2009). Operator splitting methods for pricing American options under stochastic volatility. Numerische Mathematik, 113, 299–324.

    Article  Google Scholar 

  • Jacobs, K., & Li, X. (2008). Modeling the dynamics of credit spreads with stochastic volatility. Management Science, 54, 1176–1188.

    Article  Google Scholar 

  • Karatzas, I., & Shreve, S. E. (1991). Brownian motion and stochastic calculus. New York: Springer.

    Google Scholar 

  • Leung, S. Y., & Kwok, Y. K. (2005). Credit default swap valuation with counterparty risk. Kyoto Economic Review, 75, 25–45.

    Google Scholar 

  • Litterman, R. B., & Scheinkman, J. (1991). Common factors affecting bond returns. Journal of Fixed Income, 1, 54–61.

    Article  Google Scholar 

  • Longstaff, F. A., Mithal, S., & Neis, E. (2005). Corporate yield spreads: Default risk or liquidity? New evidence from the credit default swap market. Journal of Finance, 60, 2213–2253.

    Article  Google Scholar 

  • Longstaff, F. A., Pan, J., Pedersen, L. H., & Singleton, K. J. (2011). How sovereign is sovereign credit risk? American Economic Journal: Macroeconomics, 3, 75–103.

    Google Scholar 

  • Malekzadeh, P., & Karami, G. (2005). Polynomial and harmonic differential quadrature methods for free vibration of variable thickness thick skew plates. Engineering Structures, 27, 1563–1574.

    Article  Google Scholar 

  • Morgan, J. P. (2001). The J. P. Morgan guide to credit derivatives. London: Risk Publications.

    Google Scholar 

  • Morkötter, S., Pleus, J., & Westerfeld, S. (2012). The impact of counterparty risk on credit default swap pricing dynamics. Journal of Credit Risk, 8, 63–88.

    Article  Google Scholar 

  • Pan, J., & Singleton, K. J. (2008). Default and recovery implicit in the term structure of sovereign CDS spreads. Journal of Finance, 63, 2345–2384.

    Article  Google Scholar 

  • Papageorgiou, E., & Sircar, R. (2008). Multiscale intensity models for single name credit derivatives. Applied Mathematical Finance, 15, 73–105.

    Article  Google Scholar 

  • Quarteroni, A., Sacco, R., & Saleri, F. (2006). Numerical mathematics. New York: Springer.

    Google Scholar 

  • Rambeerich, N., Tangman, D. Y., Gopaul, A., & Bhuruth, M. (2009). Exponential time integration for fast finite element solutions of some financial engineering problems. Journal of Computational and Applied Mathematics, 224, 668–678.

    Article  Google Scholar 

  • Rebonato, R. (1996). Interest-rate option models. Chichester: Wiley.

    Google Scholar 

  • Saib, A. A. E. F., Tangman, D. Y., & Bhuruth, M. (2012). A new radial basis functions method for pricing American options under Merton’s jump-diffusion model. International Journal of Computer Mathematics, 89, 1164–1185.

    Article  Google Scholar 

  • Sarra, J. (2009). Financial market destabilization and the role of credit default swaps: An international perspective on the SEC’s role going forward. University of Cincinnati Law Review, 2, 626–659.

    Google Scholar 

  • Schönbucher, P. J. (2003). Credit derivatives pricing models: Models, pricing and implementation. Chichester: Wiley.

    Google Scholar 

  • Wong, H. Y., & Guan, P. (2011). An FFT-network for Lévy option pricing. Journal of Banking & Finance, 35, 988–999.

    Article  Google Scholar 

Download references

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Correspondence to Graziella Pacelli.

Appendix

Appendix

In this section we show how to compute the matrix \(M_1\) appearing in equation (54).

Let \(\epsilon _{j,l}\), \(\omega _{j,l}\) be defined as in (47)–(51), and let \(a_1\), \( b_1\) be the coefficients of Eq. (6). Let us define:

$$\begin{aligned} h_{j,l}=\epsilon _{j,l}\, a_1\big (x^l_1\big ) +\frac{1}{2} b_1^2\big (x^l_1\big ) \omega _{j,l}, \quad j,l=1,2,\ldots , N_1, \end{aligned}$$
(82)

and

$$\begin{aligned} z_{j,j}= & {} 1+f_1(x^j_1)\,\Delta t, \quad j=1,2,\ldots , N_1, \end{aligned}$$
(83)
$$\begin{aligned} z_{j,l}= & {} 0,\;\; j=1,2,\ldots , N_1, \quad l=1,2,\ldots , N_1,\;\;j\ne l. \end{aligned}$$
(84)

Moreover, let us define the matrices (of size \(N_1 \times N_1\)):

$$\begin{aligned} H= & {} \left[ \begin{array}{cccc} h_{1,1} &{}h_{1,2}&{} \dots &{}h_{1,N_1}\\ h_{2,1} &{}h_{2,2}&{} \dots &{}h_{2,N_1}\\ \vdots &{}\vdots &{} &{}\vdots \\ h_{N_1,1} &{}h_{N_1,2}&{} \dots &{}h_{N_1,N_1}\\ \end{array}\right] , \end{aligned}$$
(85)
$$\begin{aligned} Z= & {} \left[ \begin{array}{cccc} z_{1,1} &{}z_{1,2}&{} \dots &{}z_{1,N_1}\\ z_{2,1} &{}z_{2,2}&{} \dots &{}z_{2,N_1}\\ \vdots &{}\vdots &{} &{}\vdots \\ z_{N_1,1} &{}z_{N_1,2}&{} \dots &{}z_{N_1,N_1}\\ \end{array}\right] . \end{aligned}$$
(86)

The matrix \(M_1\) is obtained as follows:

$$\begin{aligned} M_1 = Z - H\,\Delta t. \end{aligned}$$
(87)

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Andreoli, A., Ballestra, L.V. & Pacelli, G. Pricing Credit Default Swaps Under Multifactor Reduced-Form Models: A Differential Quadrature Approach. Comput Econ 51, 379–406 (2018). https://doi.org/10.1007/s10614-016-9608-x

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