Value at Risk estimates the maximum expected loss over a given period at a given confidence level — the parametric method is the fastest way to compute it, assuming returns follow a normal distribution.
How it works
Multiplying portfolio value by the z-score for the chosen confidence level and by the portfolio’s standard deviation gives the estimated VaR — the dollar loss not expected to be exceeded that percentage of the time.
What this does not include
This does not include fat-tail risk — the parametric method’s normal-distribution assumption tends to understate risk during extreme market events, when actual return distributions deviate meaningfully from normal.
How to use this calculator
- Enter portfolio value, confidence level, and daily standard deviation.
A worked example
A $1,000,000 portfolio, 95% confidence level, daily standard deviation of 0.945%: value at risk = $15,545.25 — the estimated maximum one-day loss not expected to be exceeded 95% of the time.
What the variables mean
| Variable | Meaning |
|---|---|
| Portfolio value | Total value of the portfolio |
| Confidence level | Statistical confidence for the risk estimate |
| Daily std dev | Portfolio’s historical daily volatility |
Edge cases worth knowing
VaR describes a threshold, not a worst-case scenario. A 95% VaR means losses exceed this figure about 5% of the time — it deliberately doesn’t capture the full severity of that tail risk, only the boundary.
A negative portfolio value has no meaning, so the calculator declines to show a result for that input.
Frequently asked questions
What does “95% confidence” actually mean here?
It means there’s a statistically expected 5% chance of losing more than the calculated VaR amount on a given day, under the model’s normality assumption — not a guarantee, just a probability estimate.
Why does 99% confidence produce a higher VaR than 95%?
A higher confidence level requires capturing a larger share of the potential loss distribution, which needs a larger z-score multiplier and therefore a bigger estimated loss figure.
Are there better methods than parametric VaR?
Historical simulation and Monte Carlo methods can capture non-normal return behavior more accurately, at the cost of being more computationally intensive than the closed-form parametric approach.
