What the Monte Carlo Value at Risk Calculator is for
Estimate loss quantiles from a deterministic seeded normal-return simulation.
It makes the prices, cash flows, rates, time periods, weights, and model conventions explicit so you can inspect an entered scenario without hidden live-market assumptions.
Calculation structure
Follow the stated model and units
Simulated return = mean + volatility × standard-normal draw; loss = −portfolio value × return. The tail uses ceil[(1−confidence)×simulations] worst losses.
Visual explanation
See how the inputs become the result
Assets and weightsReturn contribution
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Volatility and dependenceRisk estimate
Simulated return = mean + volatility × standard-normal draw; loss = −portfolio value × return. The tail uses ceil[(1−confidence)×simulations] worst losses.
Read the estimate correctly
Use the result within its boundaries
A 100,000 portfolio, 0% mean, 2% volatility, 95% confidence, 20,000 simulations, and seed 12345 produce a reproducible estimate.
This parametric normal simulation uses the exact discrete tail count shown in the result and can materially understate skew, fat tails, liquidity, gaps, and dependence changes. It is not a maximum-loss estimate.
Quick guide
How to use this calculator
Enter the cash flows, values, rates, timing, or portfolio assumptions named in the fields.
Use one consistent period and currency convention throughout the scenario.
Read the calculator-specific model limits before interpreting the result.
Calculation method
Calculation method
Simulated return = mean + volatility × standard-normal draw; loss = −portfolio value × return. The tail uses ceil[(1−confidence)×simulations] worst losses.
Model, simulation, root, square-root, and compounding outputs are estimates and are visibly marked approximate.
Worked example
Worked example
A 100,000 portfolio, 0% mean, 2% volatility, 95% confidence, 20,000 simulations, and seed 12345 produce a reproducible estimate.
Simulated return = mean + volatility × standard-normal draw; loss = −portfolio value × return. The tail uses ceil[(1−confidence)×simulations] worst losses.
Supported inputs
Precision and limits
Visible input limits
Inputs support up to 12 decimal places and lists support at most 1,200 rows. Iteration and simulation bounds are displayed in their fields.
International scope
No exchange, tax system, reporting standard, currency, fund rule, trading calendar, or market convention is selected automatically.
Decision boundary
Outputs are entered scenarios, not valuations, forecasts, risk limits, executable trades, suitability decisions, or recommendations.
Calculator-specific assumptions
This parametric normal simulation uses the exact discrete tail count shown in the result and can materially understate skew, fat tails, liquidity, gaps, and dependence changes. It is not a maximum-loss estimate.
