Fast Monte Carlo Simulation for Pricing Equity-Linked Securities

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.

[1]  Robin Pemantle,et al.  On path integrals for the high-dimensional Brownian bridge , 1992 .

[2]  S. Shreve Stochastic Calculus for Finance II: Continuous-Time Models , 2010 .

[3]  Desmond J. Higham,et al.  Black-Scholes option valuation for scientific computing students , 2004 .

[4]  Paolo Baldi,et al.  Pricing General Barrier Options: A Numerical Approach Using Sharp Large Deviations , 1999 .

[5]  Paul Glasserman,et al.  Monte Carlo Methods in Financial Engineering , 2003 .

[6]  Zhiqiang Zhou,et al.  Hybrid Laplace transform and finite difference methods for pricing American options under complex models , 2017, Comput. Math. Appl..

[7]  I-Chun Tsai,et al.  The source of global stock market risk: A viewpoint of economic policy uncertainty , 2017 .

[8]  W OosterleeCornelis,et al.  On a one time-step Monte Carlo simulation approach of the SABR model , 2017 .

[9]  P. Glasserman,et al.  Monte Carlo methods for security pricing , 1997 .

[10]  Desmond J. Higham,et al.  BLACK – SCHOLES FOR SCIENTIFIC COMPUTING STUDENTS , 2022 .

[11]  Yongsik Kim,et al.  COMPARISON OF NUMERICAL SCHEMES ON MULTI-DIMENSIONAL BLACK-SCHOLES EQUATIONS , 2013 .

[12]  Geng Deng,et al.  Modeling autocallable structured products , 2011 .

[13]  Junseok Kim,et al.  Finite Difference Method for the Black–Scholes Equation Without Boundary Conditions , 2018 .

[14]  Cornelis W. Oosterlee,et al.  On a one time-step Monte Carlo simulation approach of the SABR model: Application to European options , 2017, Appl. Math. Comput..

[15]  Frank J. Fabozzi,et al.  An improved least squares Monte Carlo valuation method based on heteroscedasticity , 2017, Eur. J. Oper. Res..

[16]  Akihiko Takahashi,et al.  A general control variate method for multi-dimensional SDEs: An application to multi-asset options under local stochastic volatility with jumps models in finance , 2017, Eur. J. Oper. Res..

[17]  P. Boyle Options: A Monte Carlo approach , 1977 .

[18]  Matthias Scherer,et al.  Pricing corporate bonds in an arbitrary jump-diffusion model based on an improved Brownian-bridge algorithm , 2011 .

[19]  Payam Hanafizadeh,et al.  Applying Greek letters to robust option price modeling by binomial-tree , 2018, Physica A: Statistical Mechanics and its Applications.

[20]  S. Shahmorad,et al.  A Stable and Convergent Finite Difference Method for Fractional Black–Scholes Model of American Put Option Pricing , 2019 .