Value at Risk Calculator

Introduction

Value at Risk answers a specific question: over a given horizon, what’s the most you can expect to lose on all but the worst handful of outcomes? Portfolio managers, risk officers, and individual investors use it to size positions and set stop-losses. This calculator produces VaR two ways, one that trusts the actual shape of your return data, and one that assumes a normal distribution, so you can see whether the two agree. A third figure, conditional VaR, estimates what happens when VaR is breached. It’s built for anyone with a return series in hand, from a brokerage statement or spreadsheet. Everything runs in your browser; the returns you paste in are never sent anywhere, and the tool stays free.

How to Use the Value at Risk Calculator

1. Choose the currency your portfolio is denominated in the symbol applies to every money field.

2. Enter the current portfolio value.

3. Select a confidence level: 90%, 95%, 99%, or 99.9%.

4. Set the time horizon in days, anywhere from 1 to 250.

5. Choose the frequency of your return data – daily, weekly, or monthly.

6. Enter each historical return as a percentage, using minus signs for losses.

7. Add rows as needed or remove outliers you don’t want to include. The tool requires at least five observations.

8. Press a quick-example button: S&P 500, tech stock, bond fund, crypto, steady portfolio, or crisis scenario to load a realistic dataset.

9. Read the VaR figure at the top, then compare historical, parametric, and CVaR estimates in the cards below.

How the Value at Risk Calculator Formula Works

Three VaR methods run in parallel from the same return series.

Formula: Historical VaR = Portfolio × |Percentile Return × √Horizon| ÷ 100

Formula: Parametric VaR = Portfolio × |Mean × Horizon − z × σ × √Horizon| ÷ 100

Formula: CVaR = Portfolio × |Mean of tail returns × √Horizon| ÷ 100

Where z is the z-score for your confidence level (1.28 for 90%, 1.64 for 95%, 2.33 for 99%, 3.09 for 99.9%), and σ is the sample standard deviation of your period returns.

The historical method sorts your returns from worst to best and finds the return at the (1 − confidence) percentile using linear interpolation between the two nearest observations. That return is then scaled to your horizon by multiplying by the square root of the number of periods, the standard square-root-of-time rule that assumes returns are independent across periods.

The parametric method assumes returns follow a normal distribution. It takes the mean and standard deviation, then computes the loss at the chosen z-score, adjusting for the drift term (mean × horizon).

CVaR, also called expected shortfall, averages every return that falls at or below the historical VaR threshold. It answers a different question: given that VaR is breached, what’s the average loss in that tail?

The tool requires at least five return observations, and refuses to remove rows past that minimum. More data produces more reliable estimates, one hundred or more observations is the professional standard.

Worked Example

Suppose you hold a $200,000 equity portfolio and have thirty daily returns on hand, with a worst single-day return of −3.2%, a mean of +0.15%, and a sample standard deviation of 1.2%. You select 95% confidence over a one-day horizon.

Historical VaR: The 5th percentile of the sorted returns lands near −1.8% after interpolation. Scaling to one day leaves it unchanged, so VaR = 200,000 × 1.8 ÷ 100 = $3,600.

Parametric VaR: Loss = 0.15 × 1 − 1.645 × 1.2 × 1 = −1.824%. VaR = 200,000 × 1.824 ÷ 100 = $3,648.

CVaR: Averaging the returns below −1.8% (say two observations at −2.1% and −3.2%) gives −2.65%. VaR = 200,000 × 2.65 ÷ 100 = $5,300.

The two VaR methods land within 1.3% of each other, which suggests the returns are reasonably well-behaved. The CVaR tells a starker story, when losses exceed the VaR threshold, they average $5,300, roughly 1.5× the VaR figure. That gap is the tail risk VaR alone doesn’t capture. On a 95% confidence daily measure, you’d expect about 12.6 breaches per year (5% of 252 trading days), which sets a realistic expectation for how often the number will be exceeded.