An interesting finding about the ability of geopolitical risk to forecast aggregate equity return volatility out-of-sample

This study quantifies the relative predictive power afforded by the increasingly popular newspaper-based geopolitical risk (GPR) indices suggested in Caldara and Iacoviello (2018) with regards to forecasting aggregate equity return volatility out-of-sample. The central contribution of this short study to the mainstream equity return volatility predictability literature is to concisely demonstrate that when used as a regressor in the predictive model, the one-month lagged value of the logarithm of the GPR index of interest does not improve out-of-sample point forecast accuracy relative to the benchmark nor competitors employing well-known economic variables, such as the dividend yield, book-to-market ratio, default yield spread, the rate of inflation or the percentage change in the U.S. industrial production index. The same conclusion holds when the geopolitical risk indices are combined with these economic variables via simple point forecast combination schemes. However, the geopolitical risk indices are very useful in explaining the relative out-of-sample forecast performance of models employing certain economic variables and the benchmark. In fact, when the geopolitical risk indices are used as the ``monitoring variable'' under dynamic point forecast selection strategies, such as the one suggested in Zhu and Timmermann (2021), we are able to obtain sizable point forecast accuracy gains relative to the benchmark for certain economic variables.