Monte-Carlo Simulation

Monte Carlo simulations are about producing many random variables based on specific probability distributions. This helps in estimating the probability of various results.

We will give an example to illustrate Monte Carlo Simulation implementation.

Steps Involved in Project Appraisal

Imagine an investor who wants to predict the results of a 70% stock and 30% bond portfolio over 20 years. This is how we set up a Monte Carlo simulation:

Specifying the Simulation:

Step 1: Specify the quantity of interest in terms of underlying variables.

The quantity of interest here could be the final portfolio value after 20 years, denoted as \(V_{iT}\). In this case, this is the final portfolio value at time T resulting from ith simulation trial.

The underlying variable is the return on the portfolio. The starting portfolio value is $100,000, with 70% invested in stocks and 30% in bonds.

Step 2: Specify a time horizon.

Assume we’re interested in yearly returns, so the time horizon is 20 years. Divide the calendar time into sub-periods. In this case, we will assume yearly returns so that the number of sub-periods is \(K = 20\), and the time increment \({\Delta t}\) is, therefore, one year.

Step 3: Specify the method for generating the data used in the simulation.

Here, we need to make distributional assumptions. We might assume that the annual portfolio return follows a normal distribution. Let’s say we estimate an average return \(\mu\) of 7% for stocks, 3% for bonds, a standard deviation \(\sigma\) of 15% for stocks, and 5% for bonds. We can model changes in the portfolio value using the formula below:

$$ \begin{align*}
{\Delta \text{Portfolio value}} &
=0.7\ast (\mu_{\text{stock}}\times \text{Prior portfolio value} \times {\Delta t} \\ & +\sigma_{\text{stock}}\times \text{Prior portfolio value} \times Z_k ) \\ & +0.3\ast (\mu_{\text{bond} }\times \text{Prior portfolio value}\times {\Delta t} \\ & +\sigma_{\text{bond}}\times \text{Prior portfolio value}\times Z_k) \end{align*} $$
Here, \(Z_k\) is a standard normal random variable representing the uncertainty in the portfolio return (risk factor). We can use a computer program to draw 20 random values of \(Z_k\).

Running the Simulation Over a Given Number of Trials:

Step 4: Use the simulated values to produce portfolio values.

This step involves converting the standard normal random numbers \((Z_k)\) generated in step 3 into yearly changes in portfolio value \((\Delta \text{Portfolio value})\) using our model from step 3. This gives us 20 observations of possible changes in portfolio value over the 20-year period. From these observations, we create a sequence of 20 portfolio values, starting with the initial value of $100,000.

Step 5: Calculate the final portfolio value.

The average portfolio value at the end of 20 years \((V_{iT})\) is calculated by summing up the portfolio values at the end of each year and dividing by 20. We then calculate the present value \((V_{i0})\) of this average value by discounting it to the present using an appropriate interest rate. The subscript \(i\) in \(V_{iT}\) and \(V_{i0}\) indicates that these values are from the ith simulation trial. This completes one simulation trial.

Step 6: Repeat steps 4 and 5 over the required number of trials.

Finally, we repeat steps 4 and 5 multiple times, say, 1,000 times. We then calculate summary statistics, such as the mean, median, and percentiles of the distribution of \(V_{i0}\) values. These summary statistics provide a range of potential outcomes for the portfolio value after 20 years, helping the investor understand the risks and rewards of the investment strategy.

Major Applications of Monte Carlo Simulations

• It can also be used to value complex securities such as American or European options.

Limitations of Monte Carlo Simulations

• It only provides us with statistical estimates of results, not exact figures.
• It is fairly complex and can only be carried out using specially designed software that may be expensive.
• The complexity of the process may cause errors, leading to wrong results that can be potentially misleading.

Question

Which of the following is a correct statement about the use of Monte Carlo simulations in finance and investment?

1. They provide exact valuations of call options.
2. They estimate a portfolio’s potential returns by simulating its performance.
3. They assess how changes in assumptions, such as interest rates or market volatility, affect a financial model.

Solution

The correct answer is C.

Monte Carlo simulations can assess how changes in assumptions, such as interest rates or market volatility, affect a financial model. This allows analysts to understand the impact of these changes on the model’s results.

A is incorrect because Monte Carlo simulations do not provide exact valuations of call options. Instead, they can estimate the value of these options by simulating their potential outcomes.

B is incorrect because while Monte Carlo simulations can estimate a portfolio’s potential returns, they do not simply simulate its performance. Instead, they use probability distributions to model the uncertainty in the portfolio’s returns.