Christian Conrad
Karin Loch

anticipating long-term stock market volatility (replication data)

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.

Data and Resources

Suggested Citation

Conrad, Christian; Loch, Karin (2015): Anticipating Long-Term Stock Market Volatility (replication data). Version: 1. Journal of Applied Econometrics. Dataset.