DOI QR코드

DOI QR Code

FAST PRICING OF FOUR ASSET EQUITY-LINKED SECURITIES USING BROWNIAN BRIDGE

  • YOO, CHANGWOO (DEPARTMENT OF FINANCIAL ENGINEERING, KOREA UNIVERSITY) ;
  • CHOI, YONGHO (DEPARTMENT OF MATHEMATICS AND BIG DATA, DAEGU UNIVERSITY) ;
  • KIM, SANGKWON (DEPARTMENT OF MATHEMATICS, KOREA UNIVERSITY) ;
  • KWAK, SOOBIN (DEPARTMENT OF MATHEMATICS, KOREA UNIVERSITY) ;
  • HWANG, YOUNGJIN (DEPARTMENT OF MATHEMATICS, KOREA UNIVERSITY) ;
  • KIM, JUNSEOK (DEPARTMENT OF MATHEMATICS, KOREA UNIVERSITY)
  • Received : 2021.04.22
  • Accepted : 2021.08.12
  • Published : 2021.09.25

Abstract

In this study, we present a fast option pricing method for four asset equity-linked securities (ELS) using Brownian bridge. The proposed method is based on Monte Carlo simulation (MCS) and a Brownian bridge approach. Currently, three asset ELS is the most popular ELS among multi-asset ELSs. However, four asset ELS emerged as an alternative to three asset ELS under low interest rate environment to give higher coupon rate to investors. We describe in detail the computational solution algorithm for the four underlying asset step-down ELS. The numerical tests confirm the accuracy and speed of the method.

Keywords

Acknowledgement

The authors appreciate the reviewers for their constructive comments, which have improved the quality of this paper. The corresponding author (J.S. Kim) was supported by the Brain Korea 21 FOUR from the Ministry of Education of Korea.

References

  1. M. C. Fu and J. Q. Hu, Sensitivity analysis for Monte Carlo simulation of option pricing, Prob. Eng. Inf. Sci., 9(3) (1995), 417-446. https://doi.org/10.1017/S0269964800003958
  2. D. Duffie and P. Glynn, Efficient Monte Carlo simulation of security prices, Ann. Appl. Probab., 5(4) (1995), 897-905. https://doi.org/10.1214/aoap/1177004598
  3. N. K. Chidambaran, Genetic programming with Monte Carlo simulation for option pricing, Proc. Winter Simul. Conf., 1 (2003), 285-292.
  4. L. Ballotta and I. Kyriakou, Monte Carlo simulation of the CGMY process and option pricing, J. Fut. Mark., 34(12) (2014), 1095-1121. https://doi.org/10.1002/fut.21647
  5. L. A. AbbasTurki, S. Vialle, B. Lapeyre, and P. Mercier, Pricing derivatives on graphics processing units using Monte Carlo simulation, Concurr. Comput. Pract. Exper., 26(9) (2014), 1679-1697. https://doi.org/10.1002/cpe.2862
  6. S. Galanti and A. Jung, Low-discrepancy sequences: Monte Carlo simulation of option prices, J. deriv., 5(1) (1997), 63-83. https://doi.org/10.3905/jod.1997.407985
  7. D. Grant, G. Vora, and D. Weeks, Path-dependent options: Extending the Monte Carlo simulation approach, Manag. Sci., 43(11) (1997), 1589-1602. https://doi.org/10.1287/mnsc.43.11.1589
  8. S. Rakhmayil, I. Shiller, and R. K. Thulasiram, Different Estimators Of The Underlying Asse4 s Volatility And Option Pricing Errors: Parallel Monte-Carlo Simulation, WIT Trans. Modelling Simul., 38 (2004), 121-131.
  9. G. M. Jabbour and Y. K. Liu, Option pricing and Monte Carlo simulations, J. Bus. Eco. Res., 3(9) (2005), 1-6.
  10. R. Martinkut-Kaulien, J. Stankeviien, and S. inyt, Option pricing using Monte Carlo simulation, J. Sec. Sust. Iss., 2(4) (2013), 65-79. https://doi.org/10.9770/jssi.2013.2.4(7)
  11. Q. Zhu, G. Loeper, W. Chen, and N. Langren, Markovian approximation of the rough Bergomi model for Monte Carlo option pricing, Math., 9(5) (2021), 528:1-21.
  12. H. Jang, S. Kim, J. Han, S. Lee, J. Ban, H. Han, C. Lee, D. Jeong, and J. Kim, Fast Monte Carlo simulation for pricing Equity-Linked Securities, Comput. Econ., 56 (2020), 865-882. https://doi.org/10.1007/s10614-019-09947-2
  13. P. Glasserman, Monte Carlo methods in financial engineering, Springer Science & Business Media, 53 (2013).
  14. R. Pemantle and M. D. Penrose, On path integrals for the high-dimensional Brownian bridge, J. Comput. Appl. Math., 3 (1992), 381-390.
  15. S. E. Shreve, Stochastic Calculus for Finance II: Continuous-Time Models. Springer Science & Business Media, 11 (2004).