Abstract:
This paper examines the e ect 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 us-
ing 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 in-
corporating 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 sig-
ni cant for predicting S&P volatility, while PPI and M2 volatility are signi cant for
the VIX. For the GARCH regressions, terms of trade, employment, and capacity uti-
lization volatility are statistically signi cant. 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.
