<a id="skfolio-measures-kurtosis"></a>

# skfolio.measures.kurtosis

<a id="skfolio.measures.kurtosis"></a>

### skfolio.measures.kurtosis(returns, sample_weight=None)

Compute the Kurtosis.

The Kurtosis is a measure of the heaviness of the tail of the distribution.
Higher Kurtosis corresponds to greater extremity of deviations (fat tails).

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

  **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,)*
  : Kurtosis.
    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.