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Numerical Methods for Nonlinear PDEs in Finance

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Handbook of Computational Finance

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Several examples of nonlinear Hamilton Jacobi Bellman (HJB) partial differential equations are given which arise in financial applications. The concept of a visocisity solution is introduced. Sufficient conditions which ensure that a numerical scheme converges to the viscosity solution are discussed. Numerical examples based on an uncertain volatility model are presented which show that seemingly reasonable discretization methods (which do not satisfy the sufficient conditions for convergence) fail to converge to the viscosity solution.

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References

  • Andersen, L., Andreasen, J., & Brotherton-Ratcliffe, R. (1998). The passport option. Journal of Computational Finance, 1(3), 15–36.

    Google Scholar 

  • Avellaneda, M., Levy, A., & Parás, A. (1995). Pricing and hedging derivative securities in markets with uncertain volatilities. Applied Mathematical Finance, 2, 73–88.

    Article  Google Scholar 

  • Barles, G. (1997). Convergence of numerical schemes for degenerate parabolic equations arising in finance. In L. C. G. Rogers & D. Talay (Eds.), Numerical methods in finance (pp. 1–21). Cambridge: Cambridge University Press.

    Google Scholar 

  • Barles, G., & Burdeau, J. (1995). The Dirichlet problem for semilinear second-order degenerate elliptic equations and applications to stochastic exit time control problems. Communications in Partial Differential Equations, 20, 129–178.

    Article  MathSciNet  MATH  Google Scholar 

  • Barles, G., & Rouy, E. (1998). A strong comparison result for the Bellman equation arising in stochastic exit time control problems and applications. Communications in Partial Differential Equations, 23, 1995–2033.

    Article  MathSciNet  MATH  Google Scholar 

  • Barles, G., & Souganidis, P. E. (1991). Convergence of approximation schemes for fully nonlinear equations. Asymptotic Analysis, 4, 271–283.

    MathSciNet  MATH  Google Scholar 

  • Barles, G., Daher, C. H., & Romano, M. (1995). Convergence of numerical shemes for parabolic eqations arising in finance theory. Mathematical Models and Methods in Applied Sciences, 5, 125–143.

    Article  MathSciNet  MATH  Google Scholar 

  • Basak, S., & Chabakauri, G. (2007). Dynamic mean-variance asset allocation. Working Paper, London Business School.

    Google Scholar 

  • Bergman, Y. (1995). Option pricing with differential interest rates. Review of Financial Studies, 8, 475–500.

    Article  Google Scholar 

  • Chaumont, S. (2004). A strong comparison result for viscosity solutions to Hamilton-Jacobi-Bellman equations with Dirichlet conditions on a non-smooth boundary. Working paper, Institute Elie Cartan, Université Nancy I.

    Google Scholar 

  • Chen, Z., & Forsyth, P. A. (2008). A numerical scheme for the impulse control formulation for pricing variable annuities with a guaranteed minimum withdrawal benefit (GMWB). Numerische Mathematik, 109, 535–569.

    Article  MathSciNet  MATH  Google Scholar 

  • Crandall, M. G., Ishii, H., & Lions, P. L. (1992). User’s guide to viscosity solutions of second order partial differential equations. Bulletin of the American Mathematical Society, 27, 1–67.

    Article  MathSciNet  MATH  Google Scholar 

  • Dai, M., Kwok, Y. K., & Zong, J. (2008). Guaranteed minimum withdrawal benefit in variable annuities. Mathematical Finance, 18, 595–611.

    Article  MathSciNet  MATH  Google Scholar 

  • Derman, E., & Kani, I. (1996). The ins and outs of barrier options: Part 1. Derivatives Quarterly, 3(Winter), 55–67.

    Google Scholar 

  • Forsyth, P. A., & Labahn, G. (2008). Numerical methods for controlled Hamilton-Jacobi-Bellman PDEs in finance. Journal of Computational Finance, 11, 1–44.

    Google Scholar 

  • Kushner, H. J., & Dupuis, P. G. (1991). Numerical methods for stochastic control problems in continuous time. New York: Springer.

    Google Scholar 

  • Leland, H. E. (1985). Option pricing and replication with transaction costs. Journal of Finance, 40, 1283–1301.

    Article  Google Scholar 

  • Li, D., & Ng, W.-L. (2000). Optimal dynamic portfolio selection: Multiperiod mean variance formulation. Mathematical Finance, 10, 387–406.

    Article  MathSciNet  MATH  Google Scholar 

  • Lorenz, J. (2008). Optimal trading algorithms: Portfolio transactions, mulitperiod portfolio selection, and competitive online search. PhD Thesis, ETH Zurich.

    Google Scholar 

  • Lorenz, J., & Almgren, R. (2007). Adaptive arrival price. In B. R. Bruce (Ed.), Algorithmic trading III: Precision, control, execution. New York: Institutional Investor Journals.

    Google Scholar 

  • Lyons, T. (1995). Uncertain volatility and the risk free synthesis of derivatives. Applied Mathematical Finance, 2, 117–133.

    Article  Google Scholar 

  • Milevsky, M. A., & Salisbury, T. S. (2006). Financial valuation of guaranteed minimum withdrawal benefits. Insurance: Mathematics and Economics, 38, 21–38.

    Article  MathSciNet  MATH  Google Scholar 

  • Mnif, M., & Sulem, A. (2001). Optimal risk control under excess of loss reinsurance. Working paper, Université Paris 6.

    Google Scholar 

  • Pham, H. (2005). On some recent aspects of stochastic control and their applications. Probability Surveys, 2, 506–549.

    Article  MathSciNet  MATH  Google Scholar 

  • Pooley, D. M., Forsyth, P. A., & Vetzal, K. R. (2003). Numerical convergence properties of option pricing PDEs with uncertain volatility. IMA Journal of Numerical Analysis, 23, 241–267.

    Article  MathSciNet  MATH  Google Scholar 

  • Shreve, S., & Vecer, J. (2000). Options on a traded account: Vacation calls, vacation puts, and passport options. Finance and Stochastics, 4, 255–274.

    Article  MathSciNet  MATH  Google Scholar 

  • Wang, J., & Forsyth, P. A. (2007). Maximal use of central differencing for Hamilton-Jacobi-Bellman PDEs in finance. SIAM Journal on Numerical Analysis, 46, 1580–1601.

    Article  MathSciNet  Google Scholar 

  • Zhou, X. Y., & Li, D. (2000). Continuous time mean variance portfolio selection: A stochastic LQ framework. Applied Mathematics and Optimization, 42, 19–33.

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Peter A. Forsyth .

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Forsyth, P.A., Vetzal, K.R. (2012). Numerical Methods for Nonlinear PDEs in Finance. In: Duan, JC., Härdle, W., Gentle, J. (eds) Handbook of Computational Finance. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17254-0_18

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