Tax Genius · India
Monte Carlo Portfolio Analysis
A practical methodology for using Monte Carlo simulation in portfolio research while making distribution, dependence, horizon and parameter assumptions visible.
Research framework published and architecture-reviewed:
Research framework
Simulation expands the set of paths a portfolio can be tested against, but the result is only as credible as the assumptions generating those paths.
Why simulate
Historical paths provide only one realized sequence. Simulation can test many alternative paths, helping researchers examine sequence risk, drawdown distributions and the sensitivity of outcomes to assumptions.
Assumptions first
The most important part of a Monte Carlo result is not the chart. It is the specification of returns, volatility, dependence, rebalancing, horizon and any non-normal behaviour built into the generator.
Sensitivity
Mission Saptanga treats parameter sensitivity as a primary output. If a conclusion changes materially under small reasonable assumption changes, that instability is part of the research result.
Decision role
Simulation complements historical stress and scenario analysis. It does not replace judgment about structural breaks, liquidity or investor-specific constraints.
Methodology
- State return-distribution and dependence assumptions explicitly.
- Separate parameter estimation from simulation output.
- Use scenario ranges rather than a single simulated forecast.
- Compare simulated outcomes with historical and stress evidence.
Evidence requirements
- Documented return and covariance inputs.
- Simulation horizon and rebalance assumptions.
- Number of paths and random-seed/reproducibility policy where relevant.
- Sensitivity analysis for key parameters.
Limitations
- Simulation does not create information that is absent from the model assumptions.
- Tail events can be understated by inappropriate distribution choices.
- Probability ranges are conditional on the model and should not be presented as certainty.