Quantile forecast combinations in realised volatility prediction
研究通过宏观经济和金融变量增强的分位数自回归模型，检验能否改进已实现波动率的点预测、分位数预测和密度预测，发现完整子集组合方法优于单变量模型和自回归基准。
This paper tests whether it is possible to improve point, quantile and density forecasts of realised volatility by conditioning on a set of predictive variables. We employ quantile autoregressive models augmented with macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarise the information content in the candidate predictors. Our findings suggest that no single variable is able to provide more information for the evolution of the volatility distribution beyond that contained in its own past. The best performing variable is the return on the stock market followed by the inflation rate. Our complete subset approach achieves superior point, quantile and density predictive performance relative to the univariate models and the autoregressive benchmark.