Crypto VaR Calculator

Calculator Inputs

Portfolio value (USD)

Your total crypto exposure, in dollars.

Confidence level (%)

Common choices: 95, 97.5, 99.

Holding period (days)

How long you expect to hold risk.

Method

Choose a model for tail losses.

Input type

Returns can be decimals or percent.

Horizon handling

Rolling uses actual multi-day windows.

Simulation trials

Used for Monte Carlo only.

Simulation distribution

Student t can model heavier tails.

t degrees of freedom

Used when Student t is selected.

Random seed

Same seed gives repeatable simulations.

Data series

For returns: use decimals (0.012) or percent (1.2). For prices: paste a clean historical series like 42000, 41850, 43210...

See example table

Example Data Table

Day	Price (USD)	Log return	Return (%)
1	42,000	—	—
2	41,850	-0.00358	-0.36%
3	43,210	0.03197	3.20%
4	42,640	-0.01328	-1.33%
5	44,120	0.03410	3.41%
6	43,500	-0.01417	-1.42%
7	45,090	0.03586	3.59%

Tip: You can paste either prices or returns. The calculator converts prices to log returns automatically.

Formula Used

Return: r_t = ln(P_t / P_t-1) for price inputs.
Historical VaR: VaR = −Q_p(r) × V, where Q_p is the p-th quantile and V is portfolio value.
Parametric VaR (Normal): VaR = −(μ + σ·z_p) × V, where z_p is the normal quantile.
Expected Shortfall: ES is the average return in the worst tail, then converted to loss.
Horizon scaling: multi-day VaR ≈ VaR_1d × √h (optional rolling-horizon alternative).

All outputs are shown as positive loss estimates. Models simplify reality, so use conservative assumptions and stress tests.

How to Use This Calculator

Set your total portfolio value in USD.
Pick a confidence level and holding period.
Choose a method: historical, parametric, or Monte Carlo.
Paste either daily prices or daily returns into the box.
Press Calculate VaR to see results above the form.
Download CSV for documentation, or PDF for sharing.

For portfolios, use the combined portfolio returns series if available.

VaR relevance in volatile crypto markets

Crypto markets can move sharply within hours, so risk limits need a quantified downside boundary. Value at Risk estimates the loss level that should not be exceeded over a chosen horizon at a stated confidence. A 99% one‑day VaR of 4% means losses worse than 4% are expected about 1 day in 100, under the chosen model. Report VaR in percent and dollars for sizing decisions. Combine this with stress scenarios and drawdowns to avoid relying on a single statistic during sudden market panics or cascades.

Model selection and interpretation tradeoffs

Historical VaR reads the left‑tail percentile from observed returns, making it intuitive, but it assumes the lookback window is representative. Parametric VaR uses mean and volatility with a normal quantile; it is fast yet can understate heavy tails. Monte Carlo VaR simulates many horizon outcomes and can apply Student‑t shocks to reflect fatter tails. Compare results to understand model risk and sensitivity.

Horizon design and scaling discipline

Multi‑day risk depends on how returns behave through time. The square‑root method scales one‑day volatility by √h, which fits independent returns. Crypto often shows volatility clustering, so rolling horizon returns can be more realistic by summing actual multi‑day windows from the dataset. For longer horizons, re-estimate inputs after regime shifts and test multiple lookback lengths to reduce bias.

Expected Shortfall for tail awareness

VaR is a threshold; Expected Shortfall (CVaR) describes the average loss once the threshold is breached. Two portfolios may share the same VaR while having very different tail severity, and ES exposes that difference. If VaR is 4% but ES is 7%, losses in the worst tail are materially larger than the cutoff suggests. ES is useful for leverage and liquidity risk.

Practical controls for ongoing governance

Turn metrics into controls by enforcing data hygiene and validation. Use cleaned price history, aligned timestamps, and portfolio returns net of fees when available. More observations improve stability; 250+ daily points is preferable. Track VaR and ES against risk limits and cash buffers, then backtest exceptions by counting how often realized losses exceed VaR. Frequent breaches signal updated assumptions, heavier tails, or a new volatility regime.

FAQs

What does a 99% VaR number actually mean?

It is a model-based loss threshold for the selected horizon. Under the model, losses should exceed that level about 1% of the time.

Should I paste prices or returns?

Either works. Prices are converted to log returns automatically. If you paste returns, decimals or percentage values are accepted.

Why can parametric VaR be too optimistic for crypto?

Normal assumptions often miss heavy tails and sudden gaps. That can understate rare but severe drawdowns, especially during liquidity stress.

When should I use rolling horizon returns?

Use rolling mode when you want multi-day risk based on actual windows in your dataset, instead of assuming independence and square-root scaling.

What is the difference between VaR and Expected Shortfall?

VaR is the cutoff loss. Expected Shortfall is the average loss beyond that cutoff, providing a clearer view of tail severity.

How can I validate the calculator results?

Backtest by counting exceptions: how often realized losses exceed VaR. Too many breaches suggests new volatility, short lookbacks, or a mismatched model.