American Institute of Mathematical Sciences

Advanced
October  2013, 18(8): 2083-2100. doi: 10.3934/dcdsb.2013.18.2083

Convergence, non-negativity and stability of a new Milstein scheme with applications to finance

Desmond J. Higham 1, , Xuerong Mao 2, and  Lukasz Szpruch 3,

1. 

Department of Mathematics and Statistics, University of Strathclyde, Glasgow, G1 1XH, United Kingdom
2. 

Department of Statistics and Modelling Science, University of Strathclyde, Glasgow, G1 1XH, Scotland
3. 

School of Mathematics, The University of Edinburgh, Edinburgh, EH9 3JZ, United Kingdom

Received  April 2012 Revised  February 2013 Published  July 2013

We propose and analyse a new Milstein type scheme for simulating stochastic differential equations (SDEs) with highly nonlinear coefficients. Our work is motivated by the need to justify multi-level Monte Carlo simulations for mean-reverting financial models with polynomial growth in the diffusion term. We introduce a double implicit Milstein scheme and show that it possesses desirable properties. It converges strongly and preserves non-negativity for a rich family of financial models and can reproduce linear and nonlinear stability behaviour of the underlying SDE without severe restriction on the time step. Although the scheme is implicit, we point out examples of financial models where an explicit formula for the solution to the scheme can be found.
Keywords: strong convergence, stability, non-negativity., implicit schemes stochastic differential equation, Milstein scheme.
Mathematics Subject Classification: Primary: 60H10, 60H35; Secondary: 65J1.
Citation: Desmond J. Higham, Xuerong Mao, Lukasz Szpruch. Convergence, non-negativity and stability of a new Milstein scheme with applications to finance. Discrete & Continuous Dynamical Systems - B, 2013, 18 (8) : 2083-2100. doi: 10.3934/dcdsb.2013.18.2083
References:
[1]

Assyr Abdulle and Adrian Blumenthal, Stabilized multilevel Monte Carlo method for stiff stochastic differential equations,, EPFL-ARTICLE-183502, (2013). doi: 10.1016/j.jcp.2013.05.039. Google Scholar

[2]

D. H. Ahn and B. Gao, A parametric nonlinear model of term structure dynamics,, Review of Financial Studies, 12 (1999). doi: 10.1093/rfs/12.4.721. Google Scholar

[3]

Y. Ait-Sahalia, Testing continuous-time models of the spot interest rate,, Review of Financial Studies, 9 (1996), 385. Google Scholar

[4]

J. A. D. Appleby, M. Guzowska, C. Kelly and A. Rodkina, Preserving positivity in solutions of discretised stochastic differential equations,, Applied Mathematics and Computation, 217 (2010), 763. doi: 10.1016/j.amc.2010.06.015. Google Scholar

[5]

E. Buckwar and T. Sickenberger, A comparative linear mean-square stability analysis of Maruyama-and Milstein-type methods,, Mathematics and Computers in Simulation, 81 (2011), 1110. doi: 10.1016/j.matcom.2010.09.015. Google Scholar

[6]

T. C. Gard, "Introduction to Stochastic Differential Equations,", Marcel Dekker, (1988). Google Scholar

[7]

M. Giles, Improved multilevel Monte Carlo convergence using the Milstein scheme,, Monte Carlo and Quasi-Monte Carlo Methods, 2006 (2008), 343. doi: 10.1007/978-3-540-74496-2_20. Google Scholar

[8]

M. B. Giles, Multilevel Monte Carlo path simulation,, Operations Research-Baltimore, 56 (2008), 607. doi: 10.1287/opre.1070.0496. Google Scholar

[9]

J. Goard and M. Mazur, "Stochastic Volatility Models and the Pricing of Vix Options,", Mathematical Finance, (2011). doi: 10.1111/j.1467-9965.2011.00506.x. Google Scholar

[10]

I. Gyöngy, A note on Euler's approximations,, Potential Analysis, 8 (1998), 205. doi: 10.1023/A:1008605221617. Google Scholar

[11]

S. L. Heston, "A Simple New Formula for Options with Stochastic Volatility,", Course Notes of Washington University in St. Louis, (1997). Google Scholar

[12]

D. J. Higham, A-stability and stochastic mean-square stability,, BIT Numerical Mathematics, 40 (2000), 404. doi: 10.1023/A:1022355410570. Google Scholar

[13]

D. J. Higham, Mean-square and asymptotic stability of the stochastic theta method,, SIAM Journal on Numerical Analysis, 38 (2000), 753. doi: 10.1137/S003614299834736X. Google Scholar

[14]

M. Hutzenthaler, A. Jentzen and P. E. Kloeden, Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients,, Proceedings of the Royal Society A, 467 (2011), 1563. Google Scholar

[15]

M. Hutzenthaler, A. Jentzen and P. E. Kloeden, Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients,, The Annals of Applied Probability, 22 (2012), 1611. doi: 10.1214/11-AAP803. Google Scholar

[16]

A. Jentzen, P. E. Kloeden and A. Neuenkirch, Pathwise approximation of stochastic differential equations on domains: Higher order convergence rates without global Lipschitz coefficients,, Numerische Mathematik, 112 (2009), 41. doi: 10.1007/s00211-008-0200-8. Google Scholar

[17]

C. Kahl, M. Gunther and T. Rosberg, Structure preserving stochastic integration schemes in interest rate derivative modeling,, Applied Numerical Mathematics, 58 (2008), 284. doi: 10.1016/j.apnum.2006.11.013. Google Scholar

[18]

C. Kahl and H. Schurz, Balanced Milstein methods for ordinary SDEs,, Monte Carlo Methods and Applications, 12 (2006), 143. doi: 10.1515/156939606777488842. Google Scholar

[19]

I. Karatzas and S. E. Shreve, "Brownian Motion and Stochastic Calculus,", Springer, (1991). doi: 10.1007/978-1-4612-0949-2. Google Scholar

[20]

P. E. Kloeden and E. Platen, "Numerical Solution of Stochastic Differential Equations,", Springer, (1992). Google Scholar

[21]

A. L. Lewis, "Option Valuation Under Stochastic Volatility,", Finance Press, (2000). Google Scholar

[22]

R. S. Liptser and A. N. Shiryayev, "Theory of Martingales,", Kluwer Academic Publishers, (1989). doi: 10.1007/978-94-009-2438-3. Google Scholar

[23]

R. Lord, R. Koekkoek and D. J. C. Van Dijk, A comparison of biased simulation schemes for stochastic volatility models,, Quantitative Finance, 10 (2010), 177. doi: 10.1080/14697680802392496. Google Scholar

[24]

X. Mao, "Stability of Stochastic Differential Equations with Respect to Semimartingales,", Longman Scientific & Technical, (1991). Google Scholar

[25]

X. Mao, "Stochastic Differential Equations and Their Applications,", Horwood Pub Ltd, (1997). Google Scholar

[26]

X. Mao and L. Szpruch, Strong convergence rates for backward Euler-Maruyama method for non-linear dissipative-type stochastic differential equations with super-linear diffusion coefficients,, Stochastics, 85 (2012), 144. doi: 10.1080/17442508.2011.651213. Google Scholar

[27]

X. Mao and L. Szpruch, Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients,, J. Comput. Appl. Math., 238 (2013), 14. doi: 10.1016/j.cam.2012.08.015. Google Scholar

[28]

G. N. Milstein, E. Platen and H. Schurz, Balanced implicit methods for stiff stochastic systems,, SIAM Journal on Numerical Analysis, 35 (1998), 1010. doi: 10.1137/S0036142994273525. Google Scholar

[29]

G. N. Milstein and M. V. Tretyakov, "Stochastic Numerics for Mathematical Physics. Scientific Computation,", Springer-Verlag, (2004). Google Scholar

[30]

Tetsuya Misawa, A lie algebraic approach to numerical integration of stochastic differential equations,, SIAM Journal on Scientific Computing, 23 (2001), 866. doi: 10.1137/S106482750037024X. Google Scholar

[31]

A. Neuenkirch and L. Szpruch, First order strong approximations of scalar sdes with values in a domain,, Preprint. , (). Google Scholar

[32]

H. Schurz, Convergence and stability of balanced implicit methods for systems of SDEs,, Int. J. Numer. Anal. Model, 2 (2005), 197. Google Scholar

[33]

Y. Shen, Q. Luo and X. Mao, The improved LaSalle-type theorems for stochastic functional differential equations,, Journal of Mathematical Analysis and Applications, 318 (2006), 134. doi: 10.1016/j.jmaa.2005.05.026. Google Scholar

[34]

A. N. Shiryaev, "Probability,", Springer-Verlag, (1996). Google Scholar

[35]

L. Szpruch, X. Mao, D. J. Higham and J. Pan, Numerical simulation of a strongly nonlinear Ait-Sahalia type interest rate model,, BIT Numerical Mathematics, 51 (2011), 405. doi: 10.1007/s10543-010-0288-y. Google Scholar

[36]

X. Wang, S. Gan and D. Wang, A family of fully implicit Milstein methods for stiff stochastic differential equations with multiplicative noise,, BIT, 52 (2012), 741. doi: 10.1007/s10543-012-0370-8. Google Scholar

[37]

E. Zeidler, "Nonlinear Functional Analysis and Its Applications,", Springer Verlag, (1990). Google Scholar

show all references

References:
[1]

Assyr Abdulle and Adrian Blumenthal, Stabilized multilevel Monte Carlo method for stiff stochastic differential equations,, EPFL-ARTICLE-183502, (2013). doi: 10.1016/j.jcp.2013.05.039. Google Scholar

[2]

D. H. Ahn and B. Gao, A parametric nonlinear model of term structure dynamics,, Review of Financial Studies, 12 (1999). doi: 10.1093/rfs/12.4.721. Google Scholar

[3]

Y. Ait-Sahalia, Testing continuous-time models of the spot interest rate,, Review of Financial Studies, 9 (1996), 385. Google Scholar

[4]

J. A. D. Appleby, M. Guzowska, C. Kelly and A. Rodkina, Preserving positivity in solutions of discretised stochastic differential equations,, Applied Mathematics and Computation, 217 (2010), 763. doi: 10.1016/j.amc.2010.06.015. Google Scholar

[5]

E. Buckwar and T. Sickenberger, A comparative linear mean-square stability analysis of Maruyama-and Milstein-type methods,, Mathematics and Computers in Simulation, 81 (2011), 1110. doi: 10.1016/j.matcom.2010.09.015. Google Scholar

[6]

T. C. Gard, "Introduction to Stochastic Differential Equations,", Marcel Dekker, (1988). Google Scholar

[7]

M. Giles, Improved multilevel Monte Carlo convergence using the Milstein scheme,, Monte Carlo and Quasi-Monte Carlo Methods, 2006 (2008), 343. doi: 10.1007/978-3-540-74496-2_20. Google Scholar

[8]

M. B. Giles, Multilevel Monte Carlo path simulation,, Operations Research-Baltimore, 56 (2008), 607. doi: 10.1287/opre.1070.0496. Google Scholar

[9]

J. Goard and M. Mazur, "Stochastic Volatility Models and the Pricing of Vix Options,", Mathematical Finance, (2011). doi: 10.1111/j.1467-9965.2011.00506.x. Google Scholar

[10]

I. Gyöngy, A note on Euler's approximations,, Potential Analysis, 8 (1998), 205. doi: 10.1023/A:1008605221617. Google Scholar

[11]

S. L. Heston, "A Simple New Formula for Options with Stochastic Volatility,", Course Notes of Washington University in St. Louis, (1997). Google Scholar

[12]

D. J. Higham, A-stability and stochastic mean-square stability,, BIT Numerical Mathematics, 40 (2000), 404. doi: 10.1023/A:1022355410570. Google Scholar

[13]

D. J. Higham, Mean-square and asymptotic stability of the stochastic theta method,, SIAM Journal on Numerical Analysis, 38 (2000), 753. doi: 10.1137/S003614299834736X. Google Scholar

[14]

M. Hutzenthaler, A. Jentzen and P. E. Kloeden, Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients,, Proceedings of the Royal Society A, 467 (2011), 1563. Google Scholar

[15]

M. Hutzenthaler, A. Jentzen and P. E. Kloeden, Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients,, The Annals of Applied Probability, 22 (2012), 1611. doi: 10.1214/11-AAP803. Google Scholar

[16]

A. Jentzen, P. E. Kloeden and A. Neuenkirch, Pathwise approximation of stochastic differential equations on domains: Higher order convergence rates without global Lipschitz coefficients,, Numerische Mathematik, 112 (2009), 41. doi: 10.1007/s00211-008-0200-8. Google Scholar

[17]

C. Kahl, M. Gunther and T. Rosberg, Structure preserving stochastic integration schemes in interest rate derivative modeling,, Applied Numerical Mathematics, 58 (2008), 284. doi: 10.1016/j.apnum.2006.11.013. Google Scholar

[18]

C. Kahl and H. Schurz, Balanced Milstein methods for ordinary SDEs,, Monte Carlo Methods and Applications, 12 (2006), 143. doi: 10.1515/156939606777488842. Google Scholar

[19]

I. Karatzas and S. E. Shreve, "Brownian Motion and Stochastic Calculus,", Springer, (1991). doi: 10.1007/978-1-4612-0949-2. Google Scholar

[20]

P. E. Kloeden and E. Platen, "Numerical Solution of Stochastic Differential Equations,", Springer, (1992). Google Scholar

[21]

A. L. Lewis, "Option Valuation Under Stochastic Volatility,", Finance Press, (2000). Google Scholar

[22]

R. S. Liptser and A. N. Shiryayev, "Theory of Martingales,", Kluwer Academic Publishers, (1989). doi: 10.1007/978-94-009-2438-3. Google Scholar

[23]

R. Lord, R. Koekkoek and D. J. C. Van Dijk, A comparison of biased simulation schemes for stochastic volatility models,, Quantitative Finance, 10 (2010), 177. doi: 10.1080/14697680802392496. Google Scholar

[24]

X. Mao, "Stability of Stochastic Differential Equations with Respect to Semimartingales,", Longman Scientific & Technical, (1991). Google Scholar

[25]

X. Mao, "Stochastic Differential Equations and Their Applications,", Horwood Pub Ltd, (1997). Google Scholar

[26]

X. Mao and L. Szpruch, Strong convergence rates for backward Euler-Maruyama method for non-linear dissipative-type stochastic differential equations with super-linear diffusion coefficients,, Stochastics, 85 (2012), 144. doi: 10.1080/17442508.2011.651213. Google Scholar

[27]

X. Mao and L. Szpruch, Strong convergence and stability of implicit numerical methods for stochastic differential equations with non-globally Lipschitz continuous coefficients,, J. Comput. Appl. Math., 238 (2013), 14. doi: 10.1016/j.cam.2012.08.015. Google Scholar

[28]

G. N. Milstein, E. Platen and H. Schurz, Balanced implicit methods for stiff stochastic systems,, SIAM Journal on Numerical Analysis, 35 (1998), 1010. doi: 10.1137/S0036142994273525. Google Scholar

[29]

G. N. Milstein and M. V. Tretyakov, "Stochastic Numerics for Mathematical Physics. Scientific Computation,", Springer-Verlag, (2004). Google Scholar

[30]

Tetsuya Misawa, A lie algebraic approach to numerical integration of stochastic differential equations,, SIAM Journal on Scientific Computing, 23 (2001), 866. doi: 10.1137/S106482750037024X. Google Scholar

[31]

A. Neuenkirch and L. Szpruch, First order strong approximations of scalar sdes with values in a domain,, Preprint. , (). Google Scholar

[32]

H. Schurz, Convergence and stability of balanced implicit methods for systems of SDEs,, Int. J. Numer. Anal. Model, 2 (2005), 197. Google Scholar

[33]

Y. Shen, Q. Luo and X. Mao, The improved LaSalle-type theorems for stochastic functional differential equations,, Journal of Mathematical Analysis and Applications, 318 (2006), 134. doi: 10.1016/j.jmaa.2005.05.026. Google Scholar

[34]

A. N. Shiryaev, "Probability,", Springer-Verlag, (1996). Google Scholar

[35]

L. Szpruch, X. Mao, D. J. Higham and J. Pan, Numerical simulation of a strongly nonlinear Ait-Sahalia type interest rate model,, BIT Numerical Mathematics, 51 (2011), 405. doi: 10.1007/s10543-010-0288-y. Google Scholar

[36]

X. Wang, S. Gan and D. Wang, A family of fully implicit Milstein methods for stiff stochastic differential equations with multiplicative noise,, BIT, 52 (2012), 741. doi: 10.1007/s10543-012-0370-8. Google Scholar

[37]

E. Zeidler, "Nonlinear Functional Analysis and Its Applications,", Springer Verlag, (1990). Google Scholar

[1]

Graeme D. Chalmers, Desmond J. Higham. Convergence and stability analysis for implicit simulations of stochastic differential equations with random jump magnitudes. Discrete & Continuous Dynamical Systems - B, 2008, 9 (1) : 47-64. doi: 10.3934/dcdsb.2008.9.47
[2]

Raphael Kruse, Yue Wu. A randomized Milstein method for stochastic differential equations with non-differentiable drift coefficients. Discrete & Continuous Dynamical Systems - B, 2019, 24 (8) : 3475-3502. doi: 10.3934/dcdsb.2018253
[3]

Bahareh Akhtari, Esmail Babolian, Andreas Neuenkirch. An Euler scheme for stochastic delay differential equations on unbounded domains: Pathwise convergence. Discrete & Continuous Dynamical Systems - B, 2015, 20 (1) : 23-38. doi: 10.3934/dcdsb.2015.20.23
[4]

Junhao Hu, Chenggui Yuan. Strong convergence of neutral stochastic functional differential equations with two time-scales. Discrete & Continuous Dynamical Systems - B, 2019, 24 (11) : 5831-5848. doi: 10.3934/dcdsb.2019108
[5]

Wolf-Jüergen Beyn, Janosch Rieger. The implicit Euler scheme for one-sided Lipschitz differential inclusions. Discrete & Continuous Dynamical Systems - B, 2010, 14 (2) : 409-428. doi: 10.3934/dcdsb.2010.14.409
[6]

Arnaud Debussche, Jacques Printems. Convergence of a semi-discrete scheme for the stochastic Korteweg-de Vries equation. Discrete & Continuous Dynamical Systems - B, 2006, 6 (4) : 761-781. doi: 10.3934/dcdsb.2006.6.761
[7]

Monika Eisenmann, Etienne Emmrich, Volker Mehrmann. Convergence of the backward Euler scheme for the operator-valued Riccati differential equation with semi-definite data. Evolution Equations & Control Theory, 2019, 8 (2) : 315-342. doi: 10.3934/eect.2019017
[8]

Farid Tari. Geometric properties of the integral curves of an implicit differential equation. Discrete & Continuous Dynamical Systems - A, 2007, 17 (2) : 349-364. doi: 10.3934/dcds.2007.17.349
[9]

Jian Su, Yinnian He. The almost unconditional convergence of the Euler implicit/explicit scheme for the three dimensional nonstationary Navier-Stokes equations. Discrete & Continuous Dynamical Systems - B, 2017, 22 (9) : 3421-3438. doi: 10.3934/dcdsb.2017173
[10]

Chunhong Li, Jiaowan Luo. Stochastic invariance for neutral functional differential equation with non-lipschitz coefficients. Discrete & Continuous Dynamical Systems - B, 2019, 24 (7) : 3299-3318. doi: 10.3934/dcdsb.2018321
[11]

Yulan Lu, Minghui Song, Mingzhu Liu. Convergence rate and stability of the split-step theta method for stochastic differential equations with piecewise continuous arguments. Discrete & Continuous Dynamical Systems - B, 2019, 24 (2) : 695-717. doi: 10.3934/dcdsb.2018203
[12]

B. S. Lee, Arif Rafiq. Strong convergence of an implicit iteration process for a finite family of Lipschitz $\phi -$uniformly pseudocontractive mappings in Banach spaces. Numerical Algebra, Control & Optimization, 2014, 4 (4) : 287-293. doi: 10.3934/naco.2014.4.287
[13]

Nikolaos Halidias. Construction of positivity preserving numerical schemes for some multidimensional stochastic differential equations. Discrete & Continuous Dynamical Systems - B, 2015, 20 (1) : 153-160. doi: 10.3934/dcdsb.2015.20.153
[14]

Chaman Kumar, Sotirios Sabanis. On tamed milstein schemes of SDEs driven by Lévy noise. Discrete & Continuous Dynamical Systems - B, 2017, 22 (2) : 421-463. doi: 10.3934/dcdsb.2017020
[15]

Xinlong Feng, Huailing Song, Tao Tang, Jiang Yang. Nonlinear stability of the implicit-explicit methods for the Allen-Cahn equation. Inverse Problems & Imaging, 2013, 7 (3) : 679-695. doi: 10.3934/ipi.2013.7.679
[16]

Yanqing Wang. A semidiscrete Galerkin scheme for backward stochastic parabolic differential equations. Mathematical Control & Related Fields, 2016, 6 (3) : 489-515. doi: 10.3934/mcrf.2016013
[17]

Weidong Zhao, Jinlei Wang, Shige Peng. Error estimates of the $\theta$-scheme for backward stochastic differential equations. Discrete & Continuous Dynamical Systems - B, 2009, 12 (4) : 905-924. doi: 10.3934/dcdsb.2009.12.905
[18]

Weidong Zhao, Yang Li, Guannan Zhang. A generalized $\theta$-scheme for solving backward stochastic differential equations. Discrete & Continuous Dynamical Systems - B, 2012, 17 (5) : 1585-1603. doi: 10.3934/dcdsb.2012.17.1585
[19]

Charles-Edouard Bréhier, Ludovic Goudenège. Analysis of some splitting schemes for the stochastic Allen-Cahn equation. Discrete & Continuous Dynamical Systems - B, 2019, 24 (8) : 4169-4190. doi: 10.3934/dcdsb.2019077
[20]

Francisco Guillén-González, Mouhamadou Samsidy Goudiaby. Stability and convergence at infinite time of several fully discrete schemes for a Ginzburg-Landau model for nematic liquid crystal flows. Discrete & Continuous Dynamical Systems - A, 2012, 32 (12) : 4229-4246. doi: 10.3934/dcds.2012.32.4229

2018 Impact Factor: 1.008

Tools

Metrics

  • PDF downloads (21)
  • HTML views (0)
  • Cited by (15)

Other articles
by authors

[Back to Top]

Copyright © 2019 American Institute of Mathematical Sciences