This work considered strong convergence of the Euler-Maruyama (EM) method for a stochastic volatility jump-diffusion model (SVJD model, for short). In this model, the underlying asset price follows a jump-diffusion geometric Brownian motion with stochastic volatility, and the volatility process obeys a mean-reverting square root process with Poisson jumps. As preliminary results, the existence and uniqueness of nonnegative solutions for the SVJD model was shown by means of Tanaka's formula and the comparison theorem. Also, some moment properties of the solution to the SVJD model were given. In view of unavailability of an explicit solution for the SVJD model, we used the EM method to approximate the exact solution and proved strong convergence of the EM approximation in the $ L^2 $ sense. In addition, the EM approximation for the SVJD model was applied to approximately compute expected payoffs of a European option and a barrier option. Finally, simulations were presented to verify the theoretical analysis.
Citation: Weiwei Shen, Yan Zhang. Strong convergence of the Euler-Maruyama method for the stochastic volatility jump-diffusion model and financial applications[J]. AIMS Mathematics, 2025, 10(5): 12032-12054. doi: 10.3934/math.2025545
This work considered strong convergence of the Euler-Maruyama (EM) method for a stochastic volatility jump-diffusion model (SVJD model, for short). In this model, the underlying asset price follows a jump-diffusion geometric Brownian motion with stochastic volatility, and the volatility process obeys a mean-reverting square root process with Poisson jumps. As preliminary results, the existence and uniqueness of nonnegative solutions for the SVJD model was shown by means of Tanaka's formula and the comparison theorem. Also, some moment properties of the solution to the SVJD model were given. In view of unavailability of an explicit solution for the SVJD model, we used the EM method to approximate the exact solution and proved strong convergence of the EM approximation in the $ L^2 $ sense. In addition, the EM approximation for the SVJD model was applied to approximately compute expected payoffs of a European option and a barrier option. Finally, simulations were presented to verify the theoretical analysis.
| [1] |
A. Alfonsi, Strong order one convergence of a drift implicit Euler scheme: Application to the CIR process, Statist. Probab. Lett., 83 (2013), 602–607. https://doi.org/10.1016/j.spl.2012.10.034 doi: 10.1016/j.spl.2012.10.034
|
| [2] |
D.S. Bates, Jumps and stochastic volatility: Exchange rate processes implicit in Deutsche Mark options, Rev. Financ. Stud., 9 (1996), 69–107. https://doi.org/10.1093/rfs/9.1.69 doi: 10.1093/rfs/9.1.69
|
| [3] |
F. Black, M. Scholes, The pricing of options and corporate liabilities, J. Polit. Econ., 81 (1973), 637–654. https://doi.org/10.1086/260062 doi: 10.1086/260062
|
| [4] |
P. P. Boyle, Options: A Monte Carlo approach, J. Financ Econ., 4 (1977), 323–338. https://doi.org/10.1016/0304-405X(77)90005-8 doi: 10.1016/0304-405X(77)90005-8
|
| [5] |
M. Broadie, Ö. Kaya, Exact simulation of stochastic volatility and other affine jump diffusion processes, Oper. Res., 54 (2006), 217–231. https://doi.org/10.1287/opre.1050.0247 doi: 10.1287/opre.1050.0247
|
| [6] |
M. Fukasawa, T. Takabatake, R. Westphal, Consistent estimation for fractional stochastic volatility model under high-frequency asymptotics, Math. Finance, 32 (2022), 1086–132. https://doi.org/10.1111/mafi.12354 doi: 10.1111/mafi.12354
|
| [7] |
A. Gardoń, The order of approximations for solutions of Itô-type stochastic differential equations with jump, Stoch. Anal. Appl., 22 (2004), 679–699. https://doi.org/10.1081/SAP-120030451 doi: 10.1081/SAP-120030451
|
| [8] |
S. L. Heston, A closed-form solution for options with stochastic volatility with applications to bond and currency options, Rev. Financ. Stud., 6 (1993), 327–343. https://doi.org/10.1093/rfs/6.2.327 doi: 10.1093/rfs/6.2.327
|
| [9] |
D. J. Higham, P. E. Kloeden, Numerical methods for nonlinear stochastic differential equations with jumps, Numer. Math., 101 (2005), 101–119. https://doi.org/10.1007/s00211-005-0611-8 doi: 10.1007/s00211-005-0611-8
|
| [10] |
D. J. Higham, X. Mao, Convergence of Monte Carlo simulations involving the mean-reverting square root process, J. Comput Financ., 8 (2005), 35–61. https://doi.org/10.21314/JCF.2005.136 doi: 10.21314/JCF.2005.136
|
| [11] | D. J. Higham, P. E. Kloeden, Convergence and stability of implicit methods for jump-diffusion systems, Int. J. Numer. Anal. Model., 3 (2006), 125–140. https://doi.org/2006-IJNAM-893 |
| [12] |
M. Hutzenthaler, A. Jentzen, P. E. Kloeden, Strong convergence of an explicit numerical method for SDEs with non-globally Lipschitz continuous coefficients, Ann. Appl. Probab., 22 (2012), 1611–1641. https://doi.org/10.1214/11-AAP803 doi: 10.1214/11-AAP803
|
| [13] | P. E. Kloeden, E. Platen, Numerical solution of stochastic differential equations, Berlin: Spring-Verlag, 1999. |
| [14] | X. Mao, Stability of stochastic differential equations with respect to semimartingales, New York: John Wiley & Sons, 1991. |
| [15] |
X. Mao, A. Truman, C. Yuan, Euler-Maruyama approximations in mean-reverting stochastic volatility model under regime-switching, Int. J. Stoch. Anal., 1 (2006), 080967. https://doi.org/10.1155/JAMSA/2006/80967 doi: 10.1155/JAMSA/2006/80967
|
| [16] |
X. Mao, 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), 14–28. https://doi.org/10.1016/j.cam.2012.08.015 doi: 10.1016/j.cam.2012.08.015
|
| [17] |
R. C. Merton, Option pricing when underlying stock returns are discontinuous, J. Financ Econ., 3 (1976), 125–144. https://doi.org/10.1016/0304-405X(76)90022-2 doi: 10.1016/0304-405X(76)90022-2
|
| [18] | R. Situ, Theory of stochastic differential equations with jumps and applications, New York: Springer, 2005. |
| [19] |
Y. Tian, H. Zhang, European option pricing under stochastic volatility jump-diffusion models with transaction cost, Comput. Math. Appl., 79 (2020), 2722–2741. https://doi.org/10.1016/j.camwa.2019.12.001 doi: 10.1016/j.camwa.2019.12.001
|
| [20] |
X. Wang, W. Xiao, J. Yu, Modeling and forecasting realized volatility with the fractional Ornstein CUhlenbeck process, J. Econom., 232 (2023), 389–415. https://doi.org/10.1016/j.jeconom.2021.08.001 doi: 10.1016/j.jeconom.2021.08.001
|
| [21] |
F. Wu, X. Mao, K. Chen, Strong convergence of Monte Carlo simulations of the mean-reverting square root process with jump, Appl. Math. Comput., 206 (2008), 494–505. https://doi.org/10.1016/j.amc.2008.09.040 doi: 10.1016/j.amc.2008.09.040
|
| [22] |
F. Wu, X. Mao, K. Chen, The Cox-Ingersoll-Ross model with delay and strong convergence of its Euler-Maruyama approximate solutions, Appl. Numer. Math., 59 (2009), 2641–2658. https://doi.org/10.1016/j.apnum.2009.03.004 doi: 10.1016/j.apnum.2009.03.004
|
| [23] | T. Yamada, S. Watanabe, On the uniqueness of solutions of stochastic differential equations, Kyoto J. Math., 11 (1971), 155–167. |
| [24] |
X. Yang, X. Wang, A transformed jump-adapted backward Euler method for jumpextended CIR and CEV models, Numer. Algor., 74 (2017), 39–57. https://doi.org/10.1007/s11075-016-0137-4 doi: 10.1007/s11075-016-0137-4
|