Measure probable portfolio losses across chosen horizons and confidence levels. Compare three robust estimation approaches. Make risk limits clearer with evidence, structure, and speed.
This sample shows how daily returns can feed the historical estimate.
| Date | Portfolio Value | Daily Return % |
|---|---|---|
| 2026-04-01 | 1,000,000.00 | 0.45% |
| 2026-04-02 | 995,500.00 | -0.45% |
| 2026-04-03 | 1,003,422.00 | 0.80% |
| 2026-04-04 | 994,392.00 | -0.90% |
| 2026-04-05 | 996,381.00 | 0.20% |
| 2026-04-06 | 989,406.00 | -0.70% |
| 2026-04-07 | 993,364.00 | 0.40% |
| 2026-04-08 | 984,424.00 | -0.90% |
| 2026-04-09 | 991,315.00 | 0.70% |
| 2026-04-10 | 987,349.00 | -0.40% |
Value at Risk estimates the loss threshold that should not be exceeded at a chosen confidence level over a defined holding period.
Horizon Mean = μ × t
Horizon Volatility = σ × √t
VaR = Portfolio Value × max(0, z × Horizon Volatility − Horizon Mean)
Here, μ is the daily mean return, σ is the daily volatility, t is the holding period, and z is the standard normal cutoff for the confidence level.
Historical VaR = Portfolio Value × max(0, −Percentile(Returns, Tail Probability))
This method uses actual return observations. The lower tail percentile is read directly from the sample after scaling returns to the selected horizon.
Simulated Return = Horizon Mean + Horizon Volatility × Random Normal Shock
Monte Carlo VaR = Portfolio Value × max(0, −Percentile(Simulated Returns, Tail Probability))
This approach creates many possible future returns and measures the cutoff loss from the simulated distribution.
Expected Shortfall averages losses beyond the VaR cutoff. It helps when you want a deeper view of severe tail risk rather than one threshold point.
Value at Risk is widely used in market risk analysis because it gives one clear loss threshold linked to a confidence level and a time horizon. That makes it useful for limit setting, dashboard reporting, position reviews, and capital planning discussions. A single number cannot explain every risk, but it can help teams compare strategies on a common scale.
This calculator gives three common approaches in one place. The parametric method is fast and practical when return distributions are roughly stable and close to normal. The historical method is easy to explain because it relies on observed returns. The Monte Carlo method adds flexibility by generating many possible outcomes from the chosen assumptions.
The result table also shows expected shortfall. That matters because VaR only marks the cutoff point. Two portfolios can have the same VaR but very different tail severity. Expected shortfall tells you what the average loss looks like once that bad threshold has already been crossed.
For horizon scaling, the tool uses square root of time for volatility and for historical scaling. This is common in practice, though it is still an approximation. Real markets can show skewness, kurtosis, jumps, and changing correlations. Because of that, VaR should support judgment, not replace it. Stress testing, scenario analysis, and concentration checks remain important.
It is an estimated loss threshold over a chosen period and confidence level. A 95% one day VaR means losses should exceed that value only 5% of the time.
Each method sees risk differently. Parametric is model based, historical uses actual returns, and Monte Carlo simulates many possible paths from your assumptions.
There is no universal answer. Many teams use 95% for routine monitoring and 99% for stricter control or regulatory style review.
Expected shortfall shows the average loss beyond the VaR line. It gives more information about tail severity when bad outcomes already occur.
Yes, but inputs must stay consistent. If returns are weekly, then mean, volatility, and horizon assumptions should also use weekly units.
No. This version is built for a single aggregated portfolio return stream. Multi-asset covariance modeling would need added portfolio structure inputs.
Historical VaR uses observed data directly. Parametric VaR assumes a distribution shape. Different assumptions naturally produce different tail estimates.
No. VaR is useful, but it should be paired with stress tests, scenario analysis, liquidity review, and concentration checks for stronger decisions.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.