Abstract
We propose a new numerical method for a certain type of boundary value problems for 3-dimensional partial differential equations, which are related to first passage time distributions of Itô diffusions. We consider the Kolmogorov backward equation, which arises in various applications including mathematical finance. The technique presented is based on a probabilistic interpretation of the problem, which involves a Markov chain approximation, and a Wiener–Hopf factorization. First, we use Carr’s time randomization and approximate the second component of the related diffusion process with a Markov chain. As the result, we reduce the original problem to a sequence of 1-dimensional differential equations with suitable boundary conditions, associated with Gaussian processes, whose constant coefficients are defined by the Markov chain constructed. We also suggest an improvement for the approximation procedure, which lowers the number of nodes used. Then we express an analytic solution to each problem in terms of a probabilistic form of Wiener–Hopf factorization. We implement explicit and approximate factorization formulae numerically using the Fast Fourier Transform algorithm and provide the results of numerical experiments to illustrate the performance of the method developed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alfonsi, A.: High order discretization schemes for the CIR process: application to affine term structure and Heston models. Math. Comput. 79, 209–237 (2010)
Apolloni, E., Caramellino, L., Zanette, A.: A robust tree method for pricing American options with CIR stochastic interest rate. IMA J. Manag. Math. 26(4), 377–401 (2015)
Abate, J., Whitt, W.: A unified framework for numerically inverting Laplace transforms. INFORMS J. Comput. 18(4), 408–421 (2006)
Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973)
Bouchard, B., El Karoui, N., Touzi, N.: Maturity randomization for stochastic control problems. Ann. Appl. Probab. 15(4), 2575–2605 (2005)
Boyarchenko, M.: Carr’s randomization for finite-lived Barrier options: proof of convergence. Working Paper, Available at SSRN: http://papers.ssrn.com/abstract=1275666 (2008)
Boyarchenko, M., Levendorskiǐ, S.: Prices and sensitivities of barrier and first-touch digital options in Levy-driven models. Int. J. Theor. Appl. Financ. 12(08), 1125–1170 (2009)
Boyarchenko, M., Levendorskiǐ, S.: Ghost calibration and pricing Barrier options and credit default swaps in spectrally one-sided Lévy models: the parabolic laplace inversion method. Quant. Financ. 15(3), 421–441 (2015)
Boyarchenko, S.I., Levendorskiǐ, S.Z.: American options: the EPV pricing model. Ann. Financ. 1(3), 267–292 (2005)
Boyarchenko, S.I., Levendorskiǐ, S.Z.: American options in Lévy models with stochastic interest rates. J. Comput. Financ. 12(4) (2009)
Boyarchenko, S., Levendorskiǐ, S.: American options in the Heston model with stochastic interest rate and its generalizations. Appl. Math. Financ. 20(1), 26–49 (2013)
Boyarchenko, S., Levendorskiǐ, S.: Efficient pricing barrier options and CDS in Lévy models with stochastic interest rate. Math. Financ. (2016). https://doi.org/10.1111/mafi.12121
Boyarchenko, S.I., Levendorskiǐ, S.Z.: Non-Gaussian Merton-Black-Scholes Theory. Advanced series on statistical science and applied probability, vol. 8. World Scientific Publishing Co, Singapore (2002)
Boyarchenko, S., Levendorskiǐ, S.: SINH-acceleration: efficient evaluation of probability distributions, option pricing, and Monte-Carlo simulations (2018). https://doi.org/10.2139/ssrn.3129881
Briani, D.M., Caramellino, L., Zanette, A.: A hybrid approach for the implementation of the Heston model. IMA J. Manag. Math. 28(4), 467–500 (2017)
Carr, P.: Randomization and the American put. Rev. Financ. Stud. 11, 597–626 (1998)
Chiarella, C., Kang, B., Meyer, G.H.: The evaluation of barrier option prices under stochastic volatility. Comput. Math. Appl. 64, 2034–2048 (2010)
Chourdakis, K.: Levy processes driven by stochastic volatility. Asia Pac. Financ. Mark. 12, 333–352 (2005)
Costabile, M., Leccadito, A., Massabó, I.: Computationally simple lattice methods for option and bond pricing. Decis. Econ. Financ. 32, 161–181 (2009). https://doi.org/10.1007/s10203-009-0092-9
Cox, J.C., Ingersoll, J.E., Ross, S.A.: A theory of the term structure of interest rates. Econometrica 53, 385–408 (1985)
Eström, E., Tysk, J.: The Black-Scholes equation in stochastic volatility models. J. Math. Anal. Appl. 368, 498–507 (2010)
Fusai, G., Germano, G., Marazzina, D.: Spitzer identity, Wiener-Hopf factorization and pricing of discretely monitored exotic options. Eur. J. Oper. Res. 251(1), 124–134 (2016)
Green, R., Fusai, G., Abrahams, I.D.: The Wiener-Hopf technique and discretely monitored path-dependent option pricing. Math. Financ. Int. J. Math., Stat. Financ. Econ. 20(2), 259–288 (2010)
Heston, L.A.: Closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev. Financ. Stud. 6, 327–343 (1993)
Heston, S.L., Loewenstein, M., Willard, G.A.: Options and bubbles. Rev. Financ. Stud. 20(2), 359–390 (2006). https://doi.org/10.1093/rfs/hhl005
Hieber, P.: Pricing exotic options in a regime switching economy: a Fourier transform method. Rev. Deriv. Res. 21, 231–252 (2018)
Hilliard, J.E., Schwartz, A.L., Tucker, A.L.: Bivariate binomial pricing with generalized interest rate processes. J. Financ. Res. XIX4, 585–602 (1996)
Ikonen, S., Toivanen, J.: Componentwise splitting methods for pricing American options under stochastic volatilityInt. J. Theor. Appl. Financ. 10, 331–361 (2007)
de Innocentis, M., Levendorskiǐ, S.: Calibration Heston model for credit risk. Risk. 90–95 (2017)
Itkin, A.: Pricing Derivatives Under Levy Models. Birkhauser, Basel (2017)
Kudryavtsev, O.: Finite difference methods for option pricing under Levy processes: Wiener-Hopf factorization approach. Sci. World J. (Article ID 963625), 12 (2013)
Kudryavtsev, O.: Advantages of the Laplace transform approach in pricing first touch digital options in Lévy-driven models. Boletin de la Sociedad Matematica Mexicana 22(2), 711–731 (2016)
Kudryavtsev, O., Levendorskiǐ, S.: Fast and accurate pricing of barrier options under Lévy processes. Financ. Stoch. 13(4), 531–562 (2009)
Kudryavtsev, O., Rodochenko, V.: A Wiener-Hopf factorization approach for pricing barrier options in the Heston model. Appl. Math. Sci. 11(2), 93–100 (2017)
Kushner, H.J.: Numerical methods for stochastic control problems in continuous time. SIAM J. Control. Optim. 28(5), 999–1048 (1990)
Levendorskiǐ, S.: Convergence of Carr’s randomization approximation near barrier. SIAM FM 2(1), 79–111 (2011)
Nelson, D.B., Ramaswamy, K.: Simple binomial processes as diffusion approximations in financial models. Rev. Financ. Stud. 3(3), 393–430 (1990)
Nieuwenhuis, H., Vellekoop, M.: A tree-based method to price American options in the Heston model. J. Comput. Financ. 13, 1–21 (2009)
Oksendal, B.: Stochastic Differential Equations. Springer, New York (2012)
Phelan, C.E., Marazzina, D., Fusai, G., Germano, G.: Fluctuation identities with continuous monitoring and their application to price barrier options. Eur. J. Oper. Res. 271(1), 210–223 (2018)
Phelan, C.E., Marazzina, D., Fusai, G., Germano, G.: Hilbert transform, spectral filters and option pricing. Ann. Oper. Res. 1–26 (2018)
Premia: a platform for pricing financial derivatives. https://www.rocq.inria.fr/mathfi/Premia
Rothe, E.: Zweidimensionale parabolische Randwertaufgaben als Grenzfall eindimensinaler Randwerttaufgaben. Math. Ann. 102 (1930)
Sato, K.: Lévy processes and infinitely divisible distributions. Cambridge University Press, Cambridge (1999)
Yiran, C., Rollin, d.B., Guido, S.G.: Full and fast calibration of the Heston stochastic volatility model. Eur. J. Oper. Res. 263 (2015). https://doi.org/10.1016/j.ejor.2017.05.018
Wei, J.Z.: Valuing American equity options with a stochastic interest rate: a note. J. Financ. Eng. 2, 195–206 (1996)
Zvan, R., Forsyth, P., and Vetzal, K.: A penalty method for American options with stochastic volatility. J. Comput. Appl. Math. 92, 199–218 (1998)
Acknowledgements
The reported study was funded by the Russian Foundation for Basic Research according to the research project No 18-01-00910.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Kudryavtsev, O., Rodochenko, V. (2019). A Numerical Realization of the Wiener–Hopf Method for the Kolmogorov Backward Equation. In: Karapetyants, A., Kravchenko, V., Liflyand, E. (eds) Modern Methods in Operator Theory and Harmonic Analysis. OTHA 2018. Springer Proceedings in Mathematics & Statistics, vol 291. Springer, Cham. https://doi.org/10.1007/978-3-030-26748-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-26748-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26747-6
Online ISBN: 978-3-030-26748-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)