  A novel forecasting combination and weighted quantile based tail risk
forecasting framework is proposed, aiming to reduce the impact of modelling
uncertainty in financial tail risk forecasting. The proposed approach is based
on a two-step estimation procedure. The first step involves the combination of
Value-at-Risk (VaR) forecasts at a grid of different quantile levels. A range
of parametric and semi-parametric models is selected as the model universe
which is incorporated in the forecasting combination procedure. The quantile
forecasting combination weights are estimated by optimizing the quantile loss.
In the second step, the Expected Shortfall (ES) is computed as a weighted
average of combined quantiles. The quantiles weighting structure used to
generate the ES forecast is determined by minimizing a strictly consistent
joint VaR and ES loss function of the Fissler-Ziegel class. The proposed
framework is applied to six stock market indices and its forecasting
performance is compared to each individual model in the model universe and a
simple average approach. The forecasting results based on a number of
evaluations support the proposed framework.
