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Title: Combination of conditional quantile forecasts: An application to value at risk modeling Authors:  Guilherme Valle Moura - Universidade Federal de Santa Catarina (Brazil)
Andre Portela Santos - Universidade Federal de Santa Catarina (Brazil) [presenting]
Joao Frois Caldeira - Universidade Federal do Rio Grande do Sul (Brazil)
Abstract: An effective and computationally fast approach is introduced to combine conditional quantile forecasts. The approach uses the information of the relevant loss function for the quantile problem in order to define forecast combination weights in a dynamic fashion. Two important advantages of the proposed method are that i) does not require numerical optimization of the combination weights, which facilitates implementation when a large cross section of individual forecasts is considered and ii) the aggressiveness in the allocation across alternative forecasts and the trimming of worse forecasts can be easily calibrated with a single parameter. An empirical exercise based on a data set with 50 assets shows that combinations of portfolio VaR forecasts are accurate and outperform the 16 individual models in many instances. The results hold for both long and short portfolio positions as well as in high volatile subsamples.