Finance Train LogoFinance Train
Learning LibraryTemplatesBlog
Data Science Bundle
Finance TrainFinance Train
Learning LibraryTemplatesBlog
Data Science Bundle
Lesson 19 of 20

Lesson Downloads

Option Pricing Using Monte Carlo Simulation

Monte Carlo Simulation can be used to price various financial instruments such as derivatives.

In this article, we will learn how to calculate the price of an option using the Monte Carlo Simulation.

Even though the option value can be easily calculated using the Black-Scholes Option pricing formula, we can make use of the Monte Carlo Simulation technique to achieve the same results.

Let us calculate the price of a call option. Assume that the underlying stock price (S) is 195, the exercise price(X) is 200, risk free rate (rf) is 5%, volatility (s) is 30%, and the time to expiry (t) is 0.25.

Step 1

The role of Monte Carlo simulation is to generate several future value of the stock based on which we can calculate the future value of the call option. The changes in the stock prices can be calculated using the following formula:

ΔS=SrfΔt+Sσεt\Delta S=Sr_{f}\Delta t +S\sigma \varepsilon \sqrt{t}ΔS=Srf​Δt+Sσεt​

In this equation, ε represents the random number generated from a standard normal probability distribution. In our example, we will calculate this number using the Rand() function in excel. A standard normal variable can be approximated using the Excel formula “= Rand() + Rand() + Rand() + Rand() + Rand() + Rand() + Rand() + Rand() + Rand() + Rand() + Rand() + Rand() - 6.0”.

For the purpose of this example, we will generate 1000 random paths. In reality, the higher the number of trials, the more accurate will be the result.

If the random number is 0.576548, ΔS will be 19.29978.

Step 2

Once we have ΔS, we can calculate the future value of the stock price (S + ΔS). We need to do this for each path.

The stock value at expiry will be 195 + 19.29978 = 214.29978

Step 3

The option value at expiry will be given by the formula =MAX (0,S-X)

= MAX(214.29978 – 200) = 14.29978

The above three steps will be repeated 1000 times to get 1000 option values.

Step 4

We will take the average of these 1000 option values. In our example, this value comes to approx. 9.671. Please note that this value will change every time the spreadsheet is recalculated, so you may never get the same answer.

Step 5

The option value will be discounted to the present value by multiplying it with exp(-r*t)

In our example, this value comes to 9.95.

The result can be compared to the results from a Black-Scholes calculator. You will notice that the results will get closer as you increase your number of trials.

Previous Lesson

Continuously Compounded Rate of Return

Next Lesson

Historical Simulation Vs Monte Carlo Simulation

Back to ebook

Common Probability Distributions

20 lessons

Lessons

1
What is a Probability Distribution
2
Discrete Vs. Continuous Random Variable
3
Cumulative Distribution Function
4
Discrete Uniform Random Variable
5
Bernoulli and Binomial Distribution
6
Stock Price Movement Using a Binomial Tree
7
Tracking Error and Tracking Risk
8
Continuous Uniform Distribution
9
Normal Distribution
10
Univariate Vs. Multivariate Distribution
11
Confidence Intervals for a Normal Distribution
12
Standard Normal Distribution
13
Calculating Probabilities Using Standard Normal Distribution
14
Shortfall Risk
15
Safety-first Ratio
16
Lognormal Distribution and Stock Prices
17
Discretely Compounded Rate of Return
18
Continuously Compounded Rate of Return
19
Option Pricing Using Monte Carlo Simulation
20
Historical Simulation Vs Monte Carlo Simulation

Quizzes

Common Probablity Distributions
Finance Train

Learn data science and AI skills for finance through practical courses and tutorials.

Learn

  • Learning Library
  • Course Directory
  • Blog

Resources

  • Templates & Downloads
  • Tools
  • Tables
  • Calculators

Company

  • About
  • Contact
  • Privacy
  • Terms

© 2026 Finance Train. All rights reserved.