# skfolio.metrics.qlike_loss ### skfolio.metrics.qlike_loss(returns, forecast_variance) QLIKE loss for univariate variance forecasts. $$ \text{QLIKE} = \frac{1}{n} \sum_{t=1}^{n} \left( \log(\sigma_t^2) + \frac{r_t^2}{\sigma_t^2} \right) $$ Lower values are better. For numerical stability, forecast variances are clipped below by a small positive constant before evaluating the score. In financial time series, QLIKE is often used as a comparative score when returns are heavy-tailed and realized variance is only an imperfect proxy for latent volatility. * **Parameters:** **returns** *array-like of shape (n_observations,)* : Realized returns. **forecast_variance** *array-like of shape (n_observations,)* : Forecast variances for the same timestamps, expressed in squared return units. * **Returns:** float : Mean QLIKE loss. Lower values are better; in expectation, the loss is minimized by the true conditional variance forecast. #### SEE ALSO [`portfolio_variance_qlike_loss`](https://skfolio.org/generated/skfolio.metrics.portfolio_variance_qlike_loss.html.md#skfolio.metrics.portfolio_variance_qlike_loss) : Multivariate QLIKE loss projected onto portfolio weights.