A1069
Title: An oracle inequality for multivariate dynamic quantile forecasting
Authors: Jordi Llorens-Terrazas - University of Surrey (United Kingdom) [presenting]
Abstract: An oracle inequality is derived for a family of possibly misspecified multivariate conditional autoregressive quantile models. The family includes standard specifications for nonlinear quantile prediction proposed in the literature. The inequality is used to establish that the predictor that minimizes the in-sample average check loss achieves the best out-of-sample performance within its class at a near-optimal rate, even when the model is fully misspecified. An empirical application to backtesting global growth-at-risk shows that a combination of the generalized autoregressive conditionally heteroscedastic model and the vector autoregression for value-at-risk performs best out-of-sample in terms of the check loss.
