skfolio.measures
.cvar#
- skfolio.measures.cvar(returns, beta=0.95, sample_weight=None)[source]#
Compute the historical CVaR (conditional value at risk).
The CVaR (or Tail VaR) represents the mean shortfall at a specified confidence level (beta).
- Parameters:
- returnsndarray of shape (n_observations,) or (n_observations, n_assets)
Array of return values.
- betafloat, default=0.95
The CVaR confidence level (expected VaR on the worst (1-beta)% observations).
- sample_weightndarray of shape (n_observations,), optional
Sample weights for each observation. If None, equal weights are assumed.
- Returns:
- valuefloat or ndarray of shape (n_assets,)
CVaR. If
returns
is a 1D-array, the result is a float. Ifreturns
is a 2D-array, the result is a ndarray of shape (n_assets,).