chapter five
5 Monte Carlo Simulations
This chapter covers
- Why simulations are an important tool for financial planning
- Simulating returns in Python
- Understanding the difference between arithmetic and geometric averages and simple vs. continuously compounded returns
- Estimating the probability of running out of money in retirement
- Implementing bootstrapping as an alternative to simulating returns
- Incorporating additional risks in financial planning such as inflation risk and longevity risk
Monte Carlo simulations have numerous applications in wealth management and financial planning. In this chapter, we will focus on one particular problem that is particularly well-suited for Monte Carlo analysis: whether you will run out of money in retirement.
In Monte Carlo simulations, random scenarios are generated and analyzed. Most people focus on the randomness of stock and bond returns, but Monte Carlo simulations can incorporate anything random, like inflation, health care expenses, life expectancy, or even future tax rates.