'Deja Vol' Revisited: Survey Forecasts of Macroeconomic Variables Predict Volatility in the Cross-Section of Industry Portfolios

Abstract

We investigate the question of whether macroeconomic variables contain information about future stock volatility beyond that contained in past volatility. We show that forecasts of GDP growth from the Federal Reserve's Survey of Professional Forecasters predict volatility in a cross-section of 49 industry portfolios. The expectation of higher growth rates is associated with lower stock volatility. Our results are in line with both counter-cyclical volatility in dividend news as well as in expected returns. Inflation forecasts predict higher or lower stock volatility depending on the state of the economy and the stance of monetary policy. Forecasts of higher unemployment rates are good news for stocks during expansions and go along with lower stock volatility. Our results hold in-as well as out-of-sample and pass various robustness checks.

Modelling Volatility Cycles: the (MF)^2 GARCH Model

“…Again, we found strong evidence for predictability and co-movement in forecast errors. For further evidence on the counter-cyclical behavior of the volatility forecast errors of US industry portfolios seeConrad and Glas (2020).…”