Title: Quantile forecast combinations
Authors: Ekaterini Panopoulou - University of Essex (United Kingdom) [presenting]
Loukia Meligkotsidou - University of Athens (Greece)
Ioannis Vrontos - Athens University of Economics and Business (Greece)
Spyridon Vrontos - University of Essex (United Kingdom)
Abstract: Whether it is possible to improve point, quantile and density forecasts via quantile forecast combinations is tested. The models we employ are quantile autoregressive and mean regression models augmented with a plethora of macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarize the information content in the candidate predictors. We also develop a recursive algorithm that selects, in real time, the best complete subset for each predictive regression quantile. We provide two forecasting applications; one related to stock market return forecasting and the second on realised volatility forecasting. We show that our approach delivers statistically and economically significant out-of-sample forecasts relative to both the historical average/autoregressive benchmark and the complete subset regression approach.