Skip to main content
Log in

Fast Monte Carlo Simulation for Pricing Equity-Linked Securities

  • Published:
Computational Economics Aims and scope Submit manuscript

Abstract

In this paper, we present a fast Monte Carlo simulation (MCS) algorithm for pricing equity-linked securities (ELS). The ELS is one of the most popular and complex financial derivatives in South Korea. We consider a step-down ELS with a knock-in barrier. This derivative has several intermediate and final automatic redemptions when the underlying asset satisfies certain conditions. If these conditions are not satisfied until the expiry date, then it will be checked whether the stock path hits the knock-in barrier. The payoff is given depending on whether the path hits the knock-in barrier. In the proposed algorithm, we first generate a stock path for redemption dates only. If the generated stock path does not satisfy the early redemption conditions and is not below the knock-in barrier at the redemption dates, then we regenerate a daily path using Brownian bridge. We present numerical algorithms for one-, two-, and three-asset step-down ELS. The computational results demonstrate the efficiency and accuracy of the proposed fast MCS algorithm. The proposed fast MCS approach is more than 20 times faster than the conventional standard MCS.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Baldi, P., Caramellino, L., & Iovino, M. G. (1999). Pricing general barrier options: A numerical approach using sharp large deviation. Mathematical Finance, 9(4), 293–321.

    Article  Google Scholar 

  • Boyle, P. P. (1977). Options: A Monte Carlo approach. European Journal of Operational Research, 4(3), 323–338.

    Google Scholar 

  • Boyle, P., Broadie, M., & Glasserman, P. (1997). Monte Carlo methods for security pricing. Journal of Economic Dynamics and Control, 21(8), 1267–1321.

    Article  Google Scholar 

  • Deng, G., Mallett, J., & McCann, C. (2011). Modeling autocallable structured products. Journal of Derivatives & Hedge Funds, 17(4), 326–340.

    Article  Google Scholar 

  • Fabozzi, F. J., Paletta, T., & Tunaru, R. (2017). An improved least squares Monte Carlo valuation method based on heteroscedasticity. European Journal of Operational Research, 263(2), 698–706.

    Article  Google Scholar 

  • Ghafarian, B., Hanafizadeh, P., & Qahi, A. H. M. (2018). Applying Greek letters to robust option price modeling by binomial-tree. Physica A: Statistical Mechanics and Its Applications, 503, 632–639.

    Article  Google Scholar 

  • Glasserman, P. (2013). Monte Carlo methods in financial engineering (Vol. 53). Berlin: Springer.

    Google Scholar 

  • Higham, D. J. (2004). Black–Scholes option valuation for scientific computing students. Computing in Science & Engineering, 6, 72–79.

    Article  Google Scholar 

  • Jeong, D., Yoo, M., & Kim, J. (2018). Finite difference method for the Black–Scholes equation without boundary conditions. Computational Economics, 51(4), 961–972.

    Article  Google Scholar 

  • Jo, J., & Kim, Y. (2013). Comparison of numerical schemes on multi-dimensional Black–Scholes equations. Bulletin of the Korean Mathematical Society, 50(6), 2035–2051.

    Article  Google Scholar 

  • Kalantari, R., & Shahmorad, S. (2019). A stable and convergent finite difference method for fractional Black–Scholes model of American put option pricing. Computational Economics, 53(1), 191–205.

    Article  Google Scholar 

  • Leitao, Á., Grzelak, L. A., & Oosterlee, C. W. (2017). On a one time-step Monte Carlo simulation approach of the SABR model: Application to European options. Applied Mathematics and Computation, 293, 461–479.

    Article  Google Scholar 

  • Ma, J., Zhou, Z., & Cui, Z. (2017). Hybrid Laplace transform and finite difference methods for pricing American options under complex models. Computers and Mathematics with Applications, 74(3), 369–384.

    Article  Google Scholar 

  • Pemantle, R., & Mathew, P. (1992). On path integrals for the high-dimensional Brownian bridge. Journal of Computational and Applied Mathematics, 3, 381–390.

    Article  Google Scholar 

  • Ruf, J., & Scherer, M. (2011). Pricing corporate bonds in an arbitrary jump-diffusion model based on an improved Brownian-bridge algorithm. Journal of Computational Finance, 14(3), 127–145.

    Article  Google Scholar 

  • Shiraya, K., & Takahashi, A. (2017). A general control variate method for multi-dimensional SDEs: An application to multi-asset options under local stochastic volatility with jumps models in finance. European Journal of Operational Research, 258(1), 358–371.

    Article  Google Scholar 

  • Shreve, S. E. (2004). Stochastic calculus for finance II: Continuous-time models. New York: Springer.

    Book  Google Scholar 

  • Tsai, I. (2017). The source of global stock market risk: A viewpoint of economic policy uncertainty. Economic Modelling, 60, 122–131.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the reviewers for their constructive and helpful comments on the revision of this article. The author (D. Jeong) was supported by 2018 Research Grant (PoINT) from Kangwon National University. The corresponding author (J.S. Kim) was supported by the Brain Korea 21 Plus (BK 21) from the Ministry of Education of Korea.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Junseok Kim.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

In this appendix, we provide a MATLAB source code for two asset ELS pricing.

figure d
figure e

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jang, H., Kim, S., Han, J. et al. Fast Monte Carlo Simulation for Pricing Equity-Linked Securities. Comput Econ 56, 865–882 (2020). https://doi.org/10.1007/s10614-019-09947-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10614-019-09947-2

Keywords

Navigation