• Received : 2016.11.11
  • Accepted : 2016.11.30
  • Published : 2016.12.25


We develop an efficient numerical method for pricing the Derivative Linked Securities (DLS). The payoff structure of the hybrid DLS consists with a standard 2-Star step-down type ELS and the range accrual product which depends on the number of days in the coupon period that the index stay within the pre-determined range. We assume that the 2-dimensional Geometric Brownian Motion (GBM) as the model of two equities and a no-arbitrage interest model (One-factor Hull and White interest rate model) as a model for the interest rate. In this study, we employ the Monte Carlo simulation method with the Compute Unified Device Architecture (CUDA) parallel computing as the General Purpose computing on Graphic Processing Unit (GPGPU) technology for fast and efficient numerical valuation of DLS. Comparing the Monte Carlo method with single CPU computation or MPI implementation, the result of Monte Carlo simulation with CUDA parallel computing produces higher performance.


Supported by : National Research Foundation of Korea(NRF)


  1. D. Fei, A study on the efficiency of CUDA in calculation of SOPM, Master Thesis, Kyungpook National University (2009),
  2. J.L. Fernandez, Static and dynamic SABR stochastic volatility models: Calibration and option pricing using GPUs, Mathematics and Compuers in Simulation 94, (2013), 55-75.
  3. KDB Daewoo Securities, CD-EQUITY DUET 387th DLS Instruction (2011).
  4. D. Brigo and F.Mercurio, Interest rate models theory and practice with smile, inflation and credit, 2nd edition, London: Springer (2006).
  5. J.C. Hull and A. White, Pricing interest rate derivative securities, Review of Financial Studies (1990).
  6. J.C. Hull and A.White, Numerical procedures for implementing term structure models I: single factor models, Journal of Derivatives (1994).
  7. T. Liu, X.G. Xu, and C.D. Carothers, Comparison of two accelerators for Monte Carlo radiation transport calculations, Nvidia Tesla M2090 GPU and Intel Xeon Phi 5110p coprocessor, A case study for X ray CT imaging dose cacluation, Joint International Conference on Supercomputing in Nuclear Applications, Monte Carlo 82, (2015), 230-239.
  8. NVIDIA, CUDA C Programming Guide, (2016).
  9. NVIDIA, CUDA CURAND Library,, (2016).
  10. NVIDIA, NVIDIA CUDA Compute Unified Device Architecture,, (2007).
  11. T. Williams, Parallel Processing Platform Opens Bridge to High Performance Embedded Systems,, (2014).
  12. Korea Investment & Securities, IMYOU 2IN1 ELS Instruction (2013).
  13. J. A. Anderson, E. Jankowski, Th.L. Grubb, M. Engel, and S.C. Glotzer, Massively parallel Monte Carlo for many particle simulations on GPUs, Journal of Computational Physics 254, (2013), 27-38.
  14. NVIDIA, Introducing NVIDIA TESLA GPUs for Computational Finance,, (2014).