Abstract:
The purpose of this paper is to investigate whether the current account balance can help in
forecasting the quarterly S&P500-based equity premium out-of-sample. We consider an
out-of-sample period of 1970:Q3 to 2014:Q4, with a corresponding in-sample period of
1947:Q2 to 1970:Q2. We employ a quantile predictive regression model. The quantilebased
approach is more informative relative to any linear model, as it investigates the
ability of the current account to forecast the entire conditional distribution of the equity
premium, rather than being restricted just to the conditional-mean. In addition, we employ a
recursive estimation of both the conditional-mean and quantile predictive regression models
over the out-of-sample period which allows for time-varying parameters in the forecast
evaluation part of the sample for both these models. Our results indicate that unlike as
suggested by the linear (mean-based) predictive regression model, the quantile regression
model shows that the (changes in the) real current account balance contains significant outof-
sample information especially when the stock market is performing poorly (below the
quantile value of 0.3), but not when the market is in normal to bullish modes (quantile
value above 0.3). This result seems to be intuitive in the sense that, when the markets are
performing average to well, that is performing around the median and above of the
conditional distribution of the equity premium, the excess returns is inherently a randomwalk
and hence, no information, from a predictor (changes in the real current account
balance) is necessary.
