# Digital barrier options pricing: an improved Monte Carlo algorithm

- 6.4k Downloads
- 1 Citations

## Abstract

A new Monte Carlo method is presented to compute the prices of digital barrier options on stocks. The main idea of the new approach is to use an exceedance probability and uniformly distributed random numbers in order to efficiently estimate the first hitting time of barriers. It is numerically shown that the answer of this method is closer to the exact value and the first hitting time error of the modified Monte Carlo method decreases much faster than of the standard Monte Carlo methods.

## Keywords

Digital option Double barrier Monte Carlo simulation Uniform distribution## Introduction

Derivative securities have witnessed incredible innovation over the past years. In particular, path-dependent options are successful, and most of them comprise barrier options to reduce the cost of hedging [4, 8, 22]. For these derivatives, exact valuation expressions are seldom available, thus one resorts to simulations multiple times. In this manuscript a new Monte Carlo method is proposed in order to efficiently compute the prices of digital barrier options based on an exceedance probability.

*i*-th binary or nothing and then obtain the pricing formulae. In addition, Ballestra [3] considered the problem of pricing vanilla and digital options under the Black–Scholes model, and showed that, if the payoff functions are dealt with properly, then errors close to the machine precision are obtained in only some hundredths of a second.

Barrier options are similar to vanilla options except that the option is knocked out or in, if the underlying asset price hits the barrier price *B*, before expiration date. Since 1967, barrier options have been traded in the OTC market and nowadays are the most popular class of exotic options. A step further along the option evolution path is where we combine barrier and binary options to obtain binary barrier options and binary double barrier options. Accordingly, it is quite important to develop accurate and efficient methods to evaluate barrier digital option prices in financial derivative markets.

Most research done to date have focused on option pricing with various methods, for example, Mehrdoust [17] has proposed an efficient algorithm for pricing arithmetic Asian options based on the AV and the MCV procedures, and Jerbi et al. [13], have calculated the conditional expectation using the Malliavin approach and shown that with this formula, the American option under J-process can be performed using the Monte Carlo simulation. In addition, Zhang et al. [23], have presented the total least squares quasi-Monte Carlo approach for valuing American barrier options, and Jasra and Del Moral provided a review and development of sequential Monte Carlo (SMC) methods for option pricing [12], and in Kim et al. [15], have considered Heston’s stochastic volatility model and derive exact analytic expressions for the prices of fixed strike and floating-strike geometric Asian options with continuously sampled averages.

The Monte Carlo method is very popular and robust numerical method, since it is not only easily extended to multiple underlying assets but also is stochastic and amenable to coding. On the other hand, one of main drawbacks of the Monte Carlo method is slow convergence. The statistical error of the Monte Carlo method is of order \(O(\frac{1}{\sqrt{M}})\) with *M* simulations. In particular, for continuously monitored barrier options, the hitting time error is of order \(O(\frac{1}{\sqrt{N}})\) with *N* time steps, see [7], while the European vanilla options have no time discretization error. In this study, to efficiently reduce the hitting time error near the barrier price, inspired by [16], at each finite time step, we suggest the use of a uniformly distributed random variable and a conditional exceedance probability to correctly check whether the continuous underlying asset price hits the barrier or not. Numerical results show that the new Monte Carlo method converges much faster than the standard Monte Carlo method [18]. This idea of using exceedance probability for stopped diffusion is well known in the physics community [11, 16].

The outline of the paper is as follows: in “Digital options” section, we introduce digital options and their pricing formulas and we estimate it by using standard Monte Carlo. In “Modified Monte Carlo algorithm” section, we propose the new Monte Carlo method based on the idea of using uniformly distributed random variable and the conditional exceedance probability. In “Digital barrier options” section, we present numerical results for digital barrier options with one underlying assets and compare the accuracy and efficiency between the standard and the new Monte Carlo methods. In “Double-barrier digital options” section, we present numerical results for pricing double barrier digital options and see the efficiency of the new Monte Carlo method. Finally, we summarize our conclusions and give some direction for future work.

## Digital options

The purpose of this section is to introduce two main types of digital options and express their pricing formula.

### Cash-or-nothing options

*x*at expiration if the option is in-the-money. The payoff from a call is 0 if \(S_{\text {T}}\le K\) and

*x*if \(S_{\text {T}}>K,\) and the payoff from a put is 0 if \(S_{\text {T}}\ge K\) and

*x*if \(S_{\text {T}}<K,\) where \(S_{\text {T}}\) and

*K*are stock price at maturity and strike price, respectively. Valuation of cash-or-nothing call and put options can be made using the formula described by Rubinstein and Reiner [21]:

*S*is the price of the underlying asset,

*r*is a risk-free interest rate, \(\sigma\) is a volatility,

*T*is the exercise date and \(N(\cdot )\) denotes the cumulative function for the standard normal distribution. For example, the value of a cash-or-nothing put option with 9 months to expiration, futures price 100, strike price 80, cash payout 10, risk-free interest rate 6 % per year, and the volatility 35 % per year is \(p=10e^{-0.06\times 0.75}N(-0.5846)=2.6710.\) The simulation of standard Monte Carlo that conducted on Matlab for this example has the answer 2.23.

### Asset-or-nothing options

## Modified Monte Carlo algorithm

*Q*, by a sample average of

*M*simulations

*T*] into

*N*uniform subinterval \(0 = t_0 < t_1 <\cdots < t_N = T.\) Then compute \(S_{n+1} := S_{\text {t}_{n+1}}\) at each time step for \(n = 0,...,N-1\) by

*B*. The idea is to use an exceedance probability at each time step. Let \(p_n\) denotes the probability that a diffusion process

*X*exits of domain

*D*at \(t\in [t_n, t_{n+1}]\) by given values \(X_n\) and \(X_{n+1}.\) In one dimensional half interval case, \(D=(-\infty , B)\) for a constant

*B*, the probability \(p_n\) has a simple expression using the law of Brownian bridge, see [14]. So,

*R*is a prescribed cash rebate. In this case, as an approximation of the first hitting time \(\tau,\) we may choose the midpoint \(\widetilde{\tau }=(t_n+t_{n+1})/2.\)

## Digital barrier options

- 1.
Cash-or-nothing barrier options. These payout either a prespecified cash amount or nothing, depending on whether the asset price has hit the barrier or not.

- 2.
Asset-or-nothing barrier options. These payout the value of the asset or nothing, depending on whether the asset price has hit the barrier or not.

### *Example 1*

## Double-barrier digital options

*x*at maturity if the asset price touches the lower

*L*or upper

*U*barriers before expiration. The option pays off zero if the barriers are not hit during the lifetime of the option. Similarly, a knock-out pays out a predefined cash amount

*x*at maturity if the lower or upper barriers are not hit during the lifetime of the option. If the underlying asset price touches any of barriers during the option’s life, the option vanishes. Using the Fourier sine series, we can show that the risk natural value of double barrier cash or nothing knock-out is:

### *Example 2*

Table 1 gives examples of values for knock-out double-barrier binary options for different choices of barriers and volatilities and the value of them simulation with \(M=10,000\) using the new Monte Carlo in Matlab. Also, Fig. 4 shows comparison between the exact value and the new Monte Carlo values on this example with \(~\sigma =0.1,\) and Fig. 5 displays comparison between the standard MC and the improve MC errors.

Comparison of numerical approximations using the improve MC for Example 2

Double-barrier binary option parameters \(S=100,\,T=0.25,\,r=0.05,\,x=10\) | |||||
---|---|---|---|---|---|

| | \(\sigma =0.1\) | \(\sigma =0.2\) | ||

Exact | New MC | Exact | New MC | ||

80 | 120 | 9.873 | 9.864 | 8.977 | 8.898 |

85 | 115 | 9.815 | 9.770 | 7.268 | 7.250 |

90 | 110 | 8.977 | 8.825 | 3.685 | 3.622 |

95 | 105 | 3.667 | 3.598 | 0.091 | 0.081 |

## Conclusion

In this paper, we have proposed a new efficient Monte Carlo approach for estimate values of the digital barrier and double barrier options, to correctly compute the first hitting time of the barrier price by the underlying asset. The approximate error of the new method converges much faster than the standard Monte Carlo method. Future work will be devoted to extend this idea to more general diffusion problems, and theoretically study the rate of convergence of the approximate errors, and also pricing digital barrier options by other methods such as SMC and comparing results.

## Notes

### Acknowledgments

The authors are grateful to the referees for their careful reading, insightful comments and helpful suggestions which have led to improvement of the paper.

## References

- 1.Appolloni, E., Ligori, A.: Efficient tree methods for pricing digital barrier options (2014). arxiv.org/pdf/1401.2900
- 2.Baldi, P.: Exact asymptotics for the probability of exit from a domain and applications to simulation. Ann. Probab.
**23**, 1644–1670 (1995)MathSciNetCrossRefMATHGoogle Scholar - 3.Ballestra, L.V.: Repeated spatial extrapolation: an extraordinarily efficient approach for option pricing. J. Comput. Appl. Math.
**256**, 83–91 (2014)MathSciNetCrossRefMATHGoogle Scholar - 4.Bingham, N., Kiesel, R.: Risk-Neutral Valuation: Pricing and Hedging of Financial Derivatives. Springer, New York (2004)CrossRefMATHGoogle Scholar
- 5.Cortes, J.C., Jodar, L., Villafuerte, L.: Numerical solution of random differential equations: a mean square approach. Math. Comput. Model.
**45**, 757–765 (2007)MathSciNetCrossRefMATHGoogle Scholar - 6.Cox, J.C., Rubinstein, M.: Options Markets. Prentice Hall, New Jersey (1985)Google Scholar
- 7.Gobet, E.: Weak approximation of killed diffusion using Euler schemes. Stoch. Process. Appl.
**87**, 167–197 (2000)MathSciNetCrossRefMATHGoogle Scholar - 8.Haug, E.G.: Option Pricing Formulas. McGraw-Hill Companies, New York (2007)Google Scholar
- 9.Hui, C.H.: One-touch double barrier binary option values. Appl. Financ. Econ.
**6**, 343–346 (1996)CrossRefGoogle Scholar - 10.Hyong-Chol, O., Dong-Hyok, K., Jong-Jun, J., Song-Hun, R.: Integrals of higher binary options and defaultable bonds with discrete default information. Electron. J. Math. Anal. Appl.
**2**, 190–214 (2014)MathSciNetGoogle Scholar - 11.Jansons, K.M., Lythe, G.D.: Efficient numerical solution of stochastic differential equations using exponential time stepping. J. Stat. Phys.
**100**, 1097–1109 (2000)MathSciNetCrossRefMATHGoogle Scholar - 12.Jasra, A., Del Moral, P.: Sequential Monte Carlo methods for option pricing. Stoch. Anal. Appl.
**29**, 292–316 (2011)MathSciNetCrossRefMATHGoogle Scholar - 13.Jerbi, Y., Kharrat, M.: Conditional expectation determination based on the J-process using Malliavin calculus applied to pricing American options. J. Stat. Comput. Simul.
**84**, 2465–2473 (2014)MathSciNetCrossRefGoogle Scholar - 14.Karatzas, I., Shreve, S.E.: Brownian Motion and Stochastic Calculus. Springer, New York (1991)MATHGoogle Scholar
- 15.Kim, B., Wee, I.S.: Pricing of geometric Asian options under Heston’s stochastic volatility model. Quant. Fin.
**14**, 1795-1809 (2014)MathSciNetCrossRefMATHGoogle Scholar - 16.Mannella, R.: Absorbing boundaries and optimal stopping in a stochastic differential equation. Phys. Lett. A
**254**, 257–262 (1999)MathSciNetCrossRefMATHGoogle Scholar - 17.Mehrdoust, F.: A new hybrid Monte Carlo simulation for Asian options pricing. J. Stat. Comput. Simul.
**85**, 507–516 (2015)MathSciNetCrossRefGoogle Scholar - 18.Moon, K.: Efficient Monte Carlo algorithm for pricing barrier options. Comm. Korean Math. Soc.
**23**, 285–294 (2008)MathSciNetCrossRefMATHGoogle Scholar - 19.Nouri, K., Ranjbar, H.: Mean square convergence of the numerical solution of random differential equations. Mediter. J. Math.
**12**, 1123–1140 (2015)MathSciNetCrossRefMATHGoogle Scholar - 20.Palan, S.: Digital options and efficiency in experimental asset markets. J. Econ. Behav. Organ.
**75**, 506–522 (2010)CrossRefGoogle Scholar - 21.Rubinstein, M., Reiner, E.: Unscrambling the binary code. Risk Mag.
**4**, 75–83 (1991)Google Scholar - 22.Wilmott, P.: Derivatives: The Theory and Practice of Financial Engineering. Wiley, New York (1998)Google Scholar
- 23.Zhang, L., Zhang, W., Xu, W., Shi, X.: A modified least-squares simulation approach to value American barrier options. Comput. Econ.
**44**, 489–506 (2014)CrossRefGoogle Scholar

## Copyright information

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.