Monte Carlo simulation relies on a group of algorithms that replicates the behavior of a complex system, or probabilistic phenomena, using inferential statistics.
The Monte Carlo method simulates the physical processes that are typically time-consuming, or too expensive to setup and run for a large number times. Monte Carlo simulation is used in many different disciplines from particle physics to biochemistry and engineering.
The Monte Carlo simulation method, first used by scientists working on the atom bomb in 1940, is applied to risk quantitative analysis and decision making problems. Monte Carlo simulation is especially useful in those situations where we need to make an estimate and uncertain decisions such as weather forecast predictions.
Characteristics of Monte Carlo simulations include having an output that must generate random samples while its input distribution must be known.
Users of Monte Carlo simulation methodology claim there are considerable advantages, such as:
- Easy to implement
- Provides statistical sampling for numerical experiments using the computer
- Provides approximate solution to mathematical problems
- Can be used for both stochastic and deterministic problems
Today, Monte Carlo analysis is at the heart of most financial planning software programs that aim to test the feasibility of financial plans. This is due to Monte Carlo simulations have a number of advantages over their historical simulations counterparts based on the analysis in Bengen’s work and the Trinity Study.
Additionally, Monte Carlo simulation allows for a wider variety of scenarios than the rather limited historical data can provide.
In fact, it is not uncommon to see a Monte Carlo simulation study based on 10,000 or more simulated paths for financial market returns. This provides an opportunity to observe a wider variety of return sequences that support a deeper perspective about possible retirement outcomes.
Want to learn more? Tonex offers Monte Carlo Simulation Training, a 2-day course that introduces participants to Monte Carlo simulation, a technique used to understand the impact of risk and uncertainty in engineering projects, project management, cost, and other forecasting models.
Monte Carlo Simulation Training course introduces fundamental issues in simulation-based analysis and Monte Carlo-based computing. Participants will learn about rigorous analysis and interpretation and an objective treatment of various approaches.
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