Skip to main content
Log in

Reduced models for sparse grid discretizations of the multi-asset Black-Scholes equation

  • Published:
Advances in Computational Mathematics Aims and scope Submit manuscript

Abstract

This work presents reduced models for pricing basket options with the Black-Scholes and the Heston model. Basket options lead to multi-dimensional partial differential equations (PDEs) that quickly become computationally infeasible to discretize on full tensor grids. We therefore rely on sparse grid discretizations of the PDEs, which allow us to cope with the curse of dimensionality to some extent. We then derive reduced models with proper orthogonal decomposition. Our numerical results with the Black-Scholes model show that sufficiently accurate results are achieved while gaining speedups between 80 and 160 compared to the high-fidelity sparse grid model for 2-, 3-, and 4-asset options. For the Heston model, results are presented for a single-asset option that leads to a two-dimensional pricing problem, where we achieve significant speedups with our model reduction approach based on high-fidelity sparse grid models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Amsallem, D., Zahr, M., Farhat, C.: Nonlinear model order reduction based on local reduced-order bases. Int. J. Numer. Methods Eng. 92(10), 891–916 (2012)

    Article  MathSciNet  Google Scholar 

  2. Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973)

    Article  MATH  Google Scholar 

  3. Bui-Thanh, T., Willcox, K., Ghattas, O.: Model reduction for large-scale systems with high-dimensional parametric input space. SIAM J. Sci. Comput. 30(6), 3270–3288 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  4. Bungartz, H.J., Griebel, M.: A note on the complexity of solving Poisson’s equation for spaces of bounded mixed derivatives. J. Complex. 15(2), 167–199 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  5. Bungartz, H.J., Griebel, M. Sparse grids. Acta Numerica 13, 1–123 (2004)

    Article  MathSciNet  Google Scholar 

  6. Bungartz, H.J., Heinecke, A., Pfluger, D., Schraufstetter, S.: Option pricing with a direct adaptive sparse grid approach. J. Comput. Applied Math. 236(15), 3741–3750 (2011)

    Article  MathSciNet  Google Scholar 

  7. Bungartz, H.J., Heinecke, A., Pflüger, D., Schraufstetter, S.: Parallelizing a Black-Scholes solver based on finite elements and sparse grids. Concurrency Comput. Practice Experience, 1640–1653 (2012)

  8. Burkovska, O., Haasdonk, B., Salomon, J., Wohlmuth, B. (2014)

  9. Cont, R., Lantos, N., Pironneau, O.: A reduced basis for option pricing. SIAM J. Financ. Math. 2(1), 287–316 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  10. Falc, A., Chinesta, F., Gonzlez, M.: Model reduction methods in option pricing. Working Papers. Serie AD 2006-16, Instituto Valenciano de Investigaciones Econmicas, S.A. (Ivie). http://ideas.repec.org/p/ivi/wpasad/2006-16.html (2006)

  11. Feuersänger, C.: Sparse grid methods for higher dimensional approximation. PhD thesis, Institut für Numerische Simulation, Universität Bonn (2010)

  12. Garcke, J., Gerstner, T., Griebel, M.: Intraday foreign exchange rate forecasting using sparse grids. In: Garcke, J., Griebel, M. (eds.) Sparse Grids and Applications, Lecture Notes in Computational Science and Engineering, vol. 88, pp 81–105. Springer (2013)

  13. Gerstner, T., Griebel, M.: Dimension-adaptive tensor-product quadrature. Computing 71(1), 65–87 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  14. Giles, M.: Multi-level Monte Carlo path simulation. Oper. Res. 56(3), 607–617 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  15. Glasserman, P.: Monte Carlo Methods in Financial Engineering. Springer (2004)

  16. Griebel, M., Holtz, M.: Dimension-wise integration of high-dimensional functions with applications to finance. J. Complex. 26, 455–489 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  17. Griebel, M., Hullmann, A.: An efficient sparse grid Galerkin approach for the numerical valuation of basket options under Kou’s jump-diffusion model. In: Sparse Grids and Applications, Lecture Notes in Computational Science and Engineering, pp. 121–150. Springer (2013)

  18. Griebel, M., Schneider, M., Zenger, C.: A combination technique for the solution of sparse grid problems. In: de Groen, P., Beauwens, R. (eds.) Iterative Methods in Linear Algebra, pp 263–281. Elsevier (1992)

  19. Guennebaud, G., Jacob, B., et al.: Eigen v3. http://eigen.tuxfamily.org (2010)

  20. Haasdonk, B.: Convergence rates of the POD-Greedy method. M2AN Math. Model. Numer. Anal 47, 859–873 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  21. Haasdonk, B., Salomon, J., Wohlmuth, B.: A reduced basis method for the simulation of American options. In: ENUMATH 2012 (2012)

  22. Heinecke, A., Schraufstetter, S., Bungartz, H.J.: A highly-parallel Black-Scholes solver based on adaptive sparse grids. Int. J. Comput. Math. 89(9), 1212–1238 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  23. Hepperger, P.: Option pricing in Hilbert space-valued jump-diffusion models using partial integro-differential equations. SIAM J. Finan. Math. 1(1), 454–489 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  24. Hesthaven, J.S., Stamm, B., Zhang, S.: Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods. ESAIM: Math. Model. Numer. Anal. 48, 259–283 (2014)

    Article  MATH  MathSciNet  Google Scholar 

  25. Heston, S.L.: A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev. Finan. Stud. 6, 327–343 (1993)

    Article  Google Scholar 

  26. Holtz, M.: Sparse grid quadrature in high dimensions with applications in finance and insurance. PhD thesis, Institut für Numerische Simulation, Universität Bonn (2008)

  27. Jolliffe, I.: Principal Component Analysis. Springer (2002)

  28. Kangro, R., Nicolaides, R.: Far field boundary conditions for Black-Scholes equations. SIAM J. Numer. Anal. 38(4), 1357–1368 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  29. Kiesel, R., Rupp, A., Urban, K.: Valuation of structured financial products by adaptive multiwavelet methods in high dimensions, Tech. rep., University of Ulm (2013)

  30. Leentvaar, C., Oosterlee, C.: On coordinate transformation and grid stretching for sparse grid pricing of basket options. J. Comput. Appl. Math. 222(1), 193–209 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  31. Leentvaar, C.C.W.: Pricing multi-asset options with sparse grids, PhD thesis, TU Delft (2008)

  32. Linde, G., Persson, J., Von Sydow, L.: A highly accurate adaptive finite difference solver for the Black-Scholes equation. Int. J. Comput. Math. 86(12), 2104–2121 (2008)

    Article  MathSciNet  Google Scholar 

  33. Lötstedt, P., Persson, J., von Sydow, L., Tysk, J.: Space time adaptive finite difference method for European multi-asset options. Comput. Math. Appl. 53(8), 1159–1180 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  34. Maurus, S.: A multi-dimensional PDE solver for option pricing based on the Heston model and sparse grids. Thesis (S.M.), Institut fur Informatik, Technische Universitat Munchen (2012)

  35. Mayerhofer, A.: Urban, K. A reduced basis method for parabolic partial differential equations with parameter functions and application to option pricing. Submitted (2014)

  36. Peherstorfer, B., Butnaru, D., Willcox, K., Bungartz, H.: Localized discrete empirical interpolation method. SIAM J. Sci. Comput. 36(1), A168—A192 (2014)

    Article  MathSciNet  Google Scholar 

  37. Peherstorfer, B., Kowitz, C., Pflüger, D., Bungartz, H.J.: Selected recent applications of sparse grids. Numer. Math. Theory Methods Appl. 8, 47–77 (2015)

    Article  MathSciNet  Google Scholar 

  38. Peherstorfer, B., Zimmer, S., Bungartz, H.J.: Model reduction with the reduced basis method and sparse grids. In: Sparse Grids and Applications. Springer (2013)

  39. Pironneau, O.: Calibration of options on a reduced basis. J. Comput. Appl. Math. 232(1), 139–147 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  40. Pironneau, O.: Reduced basis for vanilla and basket options. Risk Decis. Anal. 2, 185–194 (2011)

    MathSciNet  Google Scholar 

  41. Pironneau, O.: Proper orthogonal decomposition for pricing options 16(1) (2012)

  42. Reisinger, C.: Analysis of linear difference schemes in the sparse grid combination technique. IMA J. Numer. Anal. 33(2), 544–581 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  43. Reisinger, C., Wittum, G.: On multigrid for anisotropic equations and variational inequalities. Pricing multi-dimensional European and American options. Comput. Vis. Sci. 7(3–4), 189–197 (2004)

    MATH  MathSciNet  Google Scholar 

  44. Reisinger, C., Wittum, G.: Efficient hierarchical approximation of high-dimensional option pricing problems. SIAM J. Sci. Comput. 29(1), 440–458 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  45. Rozza, G., Huynh, D., Patera, A.: Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Archives Comput. Methods Eng. 15(3), 1–47 (2007)

    Article  MathSciNet  Google Scholar 

  46. Sachs, E., Schu, M.: Reduced order models (POD) for calibration problems in finance. In: Numerical Mathematics and Advanced Applications, pp. 735–742. Springer (2008)

  47. Schraufstetter, S.: A pricing framework for the efficient evaluation of financial derivatives based on Theta calculus and adaptive sparse grids. PhD thesis, Institut fur Informatik, Technische Universitat Munchen (2012)

  48. Schröter, T., Monoyios, M., Rometsch, M., Urban, K.: Model uncertainty and the robustness of hedging models. Submitted (2012)

  49. Sirovich, L.: Turbulence and the dynamics of coherent structures. Q. Appl. Math., 561–571 (1987)

  50. Veroy, K., Patera, A.: Certified real-time solution of the parametrized steady incompressible Navier-Stokes equations: Rigorous reduced-basis a posteriori error bounds. Int. J. Numer. Methods Fluids 47(8–9), 773–788 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  51. Wilmott, P., Dewynne, J., Howison, S.: Option Pricing: Mathematical Models and Computation. Oxford Financial Press (1994)

  52. Yserentant, H.: Hierarchical bases give conjugate gradient type methods a multigrid speed of convergence. Appl. Math. Comput. 19(1-4), 347–358 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  53. Zenger, C.: Sparse grids. In: Hackbusch, W. (ed.) Parallel Algorithms for Partial Differential Equations, Notes on Numerical Fluid Mechanics, vol. 31, pp. 241–251. Vieweg (1991)

  54. Zhou, Y.B.: Model reduction for nonlinear dynamical systems with parametric uncertainties. Thesis (S.M.), Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics (2012)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benjamin Peherstorfer.

Additional information

Communicated by: Karsten Urban

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peherstorfer, B., Gómez, P. & Bungartz, HJ. Reduced models for sparse grid discretizations of the multi-asset Black-Scholes equation. Adv Comput Math 41, 1365–1389 (2015). https://doi.org/10.1007/s10444-015-9421-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10444-015-9421-4

Keywords

Mathematics Subject Classifications (2010)

Navigation