Monte Carlo Analysis

What Is Monte Carlo Simulation

Monte Carlo analysis is a statistical tool that imagines thousands of possible futures for your portfolio. Starting from historical returns, volatility, and correlations, the model generates 10,000 alternative scenarios to answer questions such as:

• How much could my portfolio be worth in 1, 5, or 10 years?
• What is the probability of reaching (or missing) a specific goal?
• How relevant are day-to-day swings compared with the long term?

The simulation does not guarantee an outcome. It delivers a probability map that helps you understand the range of plausible results and how likely they are.

How the Scenarios Are Built

1. Starting data – Current portfolio value, historical returns and volatility for each holding, and their correlations.
2. Random simulations – For every one of the next 120 months (10 years) the model draws plausible returns that respect averages, risk, and correlations.
3. Repetition – The process repeats 10,000 times, creating just as many potential paths for your portfolio.
4. Statistics – From the simulated paths we extract distributions, probabilities, and summary indicators (percentiles, expected return, probability of loss, and more).

With or Without Rebalancing

You can decide whether the simulation assumes a periodic rebalancing back to the initial weights (T0) or lets the weights drift naturally. Rebalancing narrows the spread of results because it restores the original mix; without it, winners grow heavier and losers lighter, so outcomes become more extreme.

Absolute or Relative View

• Absolute view: results are expressed in the portfolio currency (e.g., EUR). Choose it when you have a monetary target.
• Relative view: shows percentage changes versus today. Ideal when comparing strategies or understanding potential gains and losses in percentage terms.

How to Read the Charts

1. Scenario Curves

The first chart displays a selection of the 10,000 simulated paths. Each line shows how the portfolio value might evolve over time.

Look for:

• The cloud of lines, which illustrates uncertainty: the further in time, the wider the spread.
• The central fan, representing the most likely outcomes, while the outer lines are optimistic or pessimistic extremes.
• In the relative view you can instantly gauge potential percentage increases or drawdowns.

2. Ten-Year Outcome Histogram

The second chart summarises the scenarios by looking at the portfolio value exactly 10 years from now. Each bar shows the probability of ending up in a given range.

How to interpret it:

• The tallest bar marks the most frequent result across simulations.
• The right tail highlights very positive scenarios, while the left tail shows the worst cases.
• Moving the cursor (when available) reveals the probability of hitting a chosen target.

Practical Tips

• Define your goal upfront: knowing your target value makes the distribution easier to interpret.
• Compare multiple setups: toggle rebalancing on and off to see how much risk changes.
• Watch the percentiles: the 5th, 50th, and 95th percentiles describe bad, median, and great scenarios.
• Use the relative view for quick comparisons with other portfolios or strategies.
• Remember this is a probabilistic estimate: real markets can deviate, especially during shocks.

Why It Helps Non-Experts Too

• Complex notions (volatility, distributions, correlations) are turned into intuitive visuals.
• Plain-language labels and guided explanations walk you through each step.
• You can explore scenarios with no formulas: the system does the heavy lifting and serves readable probabilities.

With Monte Carlo simulation you obtain a realistic—though never certain—picture of how your portfolio could evolve, helping you take more informed decisions about investments, goals, and the level of risk you are willing to tolerate.