Portfolio Simulation - Part II
Now we will use the simulation function you built to evaluate 10-year returns.
Your stock-heavy portfolio has an initial investment of $10,000, an expected return of 7% and a volatility of 30%. You want to get a 95% confidence interval of what your investment will be worth in 10 years. We will simulate multiple samples of 10-year returns and calculate the confidence intervals on the distribution of returns.
By the end of this exercise, you will have run a complete portfolio simulation.
The function portfolio_return()
from the previous exercise is already initialized in the environment.
This exercise is part of the course
Statistical Simulation in Python
Exercise instructions
- Initialize
sims
to 1,000. - Enter the appropriate values for the
portfolio_return()
function parameters. - Calculate the 95% confidence interval lower (
lower_ci
) and upper limits (upper_ci
).
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Run 1,000 iterations and store the results
sims, rets = 1000, []
for i in range(sims):
rets.append(portfolio_return(yrs = 10, avg_return = 0.07,
volatility = 0.3, principal = 10000))
# Calculate the 95% CI
lower_ci = ____
upper_ci = ____
print("95% CI of Returns: Lower = {}, Upper = {}".format(lower_ci, upper_ci))