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Application of the Kusuoka approximation with a tree-based branching algorithm to the pricing of interest-rate derivatives under the HJM model

Published online by Cambridge University Press:  01 July 2010

Mariko Ninomiya*
Affiliation:
3-1, Hongo 7-chome, Bunkyo-ku, Tokyo, 113-0033, Japan (email: [email protected])

Abstract

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This paper demonstrates the application of a new higher-order weak approximation, called the Kusuoka approximation, with discrete random variables to non-commutative multi-factor models. Our experiments show that using the Heath–Jarrow–Morton model to price interest-rate derivatives can be practically feasible if the Kusuoka approximation is used along with the tree-based branching algorithm.

Type
Research Article
Copyright
Copyright © London Mathematical Society 2010

References

[1] Brigo, D. and Mercurio, F., Interest rate models – theory and practice: with smile, inflation and credit, 2nd edn (Springer, Berlin, 2007).Google Scholar
[2] Crisan, D. and Lyons, T., ‘Minimal entropy approximations and optimal algorithms for the filtering problem’, Monte Carlo Methods Appl. 8 (2002) 343355.CrossRefGoogle Scholar
[3] Hull, J., Options, futures, and other derivatives (Prentice Hall, Upper Saddle River, NJ, 2000).Google Scholar
[4] Kusuoka, S., ‘Approximation of expectation of diffusion process and mathematical finance’, Taniguchi Conference on Mathematics Nara ’98, Advanced Studies in Pure Mathematics 31 (eds Maruyama, M. and Sunada, T.; Mathematical Society of Japan, Tokyo, 2001) 147165.CrossRefGoogle Scholar
[5] Kusuoka, S., ‘Approximation of expectation of diffusion processes based on Lie algebra and Malliavin calculus’, Adv. Math. Econ. 6 (2004) 6983.CrossRefGoogle Scholar
[6] Lyons, T. and Victoir, N., ‘Cubature on Wiener space’, Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. 460 (2004) 169198.CrossRefGoogle Scholar
[7] Ninomiya, S., ‘A new simulation scheme of diffusion processes: application of the Kusuoka approximation to finance problems’, Math. Comput. Simulation 62 (2003) no. 3–6, 479486.CrossRefGoogle Scholar
[8] Ninomiya, S., ‘A partial sampling method applied to the Kusuoka approximation’, Monte Carlo Methods Appl. 9 (2003) 2738.CrossRefGoogle Scholar
[9] Ninomiya, M., ‘A new weak approximation scheme of stochastic differential equations by using the Runge–Kutta method’, PhD Thesis, The University of Tokyo, Tokyo, 2008.CrossRefGoogle Scholar
[10] Ninomiya, M. and Ninomiya, S., ‘A new weak approximation scheme of stochastic differential equations by using the Runge–Kutta method’, Finance Stoch. 13 (2009) no. 3, 415443.CrossRefGoogle Scholar
[11] Ninomiya, S. and Victoir, N., ‘Weak approximation of stochastic differential equations and application to derivative pricing’, Appl. Math. Finance 15 (2008) 107121.CrossRefGoogle Scholar
[12] Rebonato, R., Interest-rate option models, 2nd edn (John Wiley & Sons, Chichester, 1998).Google Scholar
[13] Shimizu, M., ‘Application of the Kusuoka approximation with tree based branching algorithm to pricing interest-rate derivatives with the HJM model’, Master Thesis, Imperial College of Science, Technology and Medicine, London, 2002.Google Scholar