Anticipating Long-Term Stock Market Volatility
使用两成分GARCH-MIDAS模型，研究发现期限利差、新屋开工率、企业利润和失业率等宏观变量能有效预测美国股市的长期波动性，其中期限利差和新屋开工率是领先指标。
We investigate the relationship between long-term US stock market risks and the macroeconomic environment using a two-component GARCH-MIDAS model. Our results show that macroeconomic variables are important determinants of the secular component of stock market volatility. Among the various macro variables in our dataset the term spread, housing starts, corporate profits and the unemployment rate have the highest predictive ability for long-term stock market volatility. While the term spread and housing starts are leading variables with respect to stock market volatility, for industrial production and the unemployment rate expectations data from the Survey of Professional Forecasters regarding the future development are most informative. Copyright © 2014 John Wiley & Sons, Ltd.