Abstract

AbstractFinancial markets are expected to predict macroeconomic conditions as movement in the former depends upon expectations of future performance for the latter. However, existing evidence is mixed. We argue that this arises because the stock return and term structure series typically used in studies, fail to sufficiently capture investor risk preferences. For US data, we use the variance risk premium (VRP) and default yield (DFY) to better capture such a risk measure and demonstrate that these variables exhibit greater evidence of predictive power for key macroeconomic series. In addition to VRP and DFY, we include further variables that may also capture market risk. Given similar dynamics between different risk measures and the potential for multicollinearity in estimation, we consider variable combinations. Using results obtained through predictive regressions, out‐of‐sample forecasting and a probit model designed to capture periods of expansion and contraction, we show that these combination variables can predict future movements in macroeconomic conditions as well as results using individual variables. Of key interest, combinations that include the VRP and DFY are preferred across all macro‐series.

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