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# skfolio.measures.value_at_risk

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### skfolio.measures.value_at_risk(returns, beta=0.95, sample_weight=None)

Compute the historical value at risk (VaR).
The VaR is the maximum loss at a given confidence level (beta).

* **Parameters:**
  **returns** *ndarray of shape (n_observations,) or (n_observations, n_assets)*
  : Array of return values.

  **beta** *float, default=0.95*
  : The VaR confidence level (return on the worst (1-beta)% observation).

  **sample_weight** *ndarray of shape (n_observations,), optional*
  : Sample weights for each observation. If None, equal weights are assumed.
* **Returns:**
  **value** *float or ndarray of shape (n_assets,)*
  : Value at Risk.
    If `returns` is a 1D-array, the result is a float.
    If `returns` is a 2D-array, the result is a ndarray of shape (n_assets,).

### Notes

NaN handling:
- Unweighted: NaNs are ignored; all-NaN inputs yield NaN.
- Weighted: NaNs propagate.