Fundamental Volatility’s Effect on Asset Volatility

This paper examines the effect of macroeconomic variable volatility on implied and realized asset price level volatilities in the U.S. using monthly data from 1986 2008. Two approaches are taken: An autoregressive distributed lag model using rolling standard deviations and a GARCH model. The S&P 500’s volatility is used as a proxy for historical (actual) volatility and the VIX is used as a proxy for implied volatility. For the distributed lag model, each linear regression tests granger causality (using Newey-West robust standard errors) of a single macroeconomic variable by incorporating lagged values (as determined by comparing Bayesian Information Criteria of both the constructed macroeconomic variable and the dependent asset volatility variable). Capacity utilization, PPI, and employment volatility are found to be significant for predicting S&P volatility, while PPI and M2 volatility are significant for the VIX. For the GARCH regressions, terms of trade, employment, and capacity utilization volatility are statistically significant. Forecasts are then constructed using those variables shown to be granger casual, but a two-sided t-test rejects the null hypothesis that forecast errors are zero in every case. ∗The author is grateful to Professor Lori Leachman, his thesis advisor and first economics professor, without whose support and guidance this learning experience would not have been possible. Thanks also to Professor Kent Kimbrough for great ideas, an open office door, and countless iterations of suggestions, Professor Rossi for extensive help with the models, Professor Rasiel for help with the VIX, Professor Bollerslev, and participants in Professor Kimbrough’s seminar at Duke for helpful comments.