The Benefits of Bagging for Forecast Models of Realized Volatility
研究了袋装法能否提升已实现波动率时间序列模型的预测精度，基于1995至2005年道琼斯工业平均指数中23只股票的数据，发现袋装法对对数线性模型和非线性模型均有改进，尤其对前者效果更佳。
This article shows that bagging can improve the forecast accuracy of time series models for realized volatility. We consider 23 stocks from the Dow Jones Industrial Average over the sample period 1995 to 2005 and employ two different forecast models, a log-linear specification in the spirit of the heterogeneous autoregressive model and a nonlinear specification with logistic transitions. Both forecast model types benefit from bagging, in particular in the 1990s part of our sample. The log-linear specification shows larger improvements than the nonlinear model. Bagging the log-linear model yields the highest forecast accuracy on our sample.