Oil-Price Uncertainty and International Stock Returns: Dissecting Quantile-Based Predictability and Spillover Effects Using More than a Century of Data

Abstract

We investigate whether oil-price uncertainty helps forecast the international stock returns of ten advanced and emerging countries. We consider an out-of-sample period of August 1925 to September 2021, with an in-sample period between August 1920 and July 1925, and employ a quantile-predictive-regression approach, which is more informative relative to a linear model, as it investigates the ability of oil-price uncertainty to forecast the entire conditional distribution of stock returns Based on a recursive estimation scheme, we draw the following main conclusions: the quantile-predictive-regression approach using oil-price uncertainty as a predictor statistically outperforms the corresponding quantile-based constant-mean model for all ten countries at certain quantiles (capturing normal, bear, and bull markets), and over specific forecast horizons, compared to forecastability being detected for eight countries under the linear predictive model. Importantly, we detect forecasting gains in many more horizons (at particular quantiles) compared to the linear case. In addition, an oil-price uncertainty-based state-contingent spillover analysis reveals that the ten equity markets are connected more tightly at the upper regime, suggesting that heightened oil-market volatility erodes the benefits from diversification across equity markets.

Keywords

• international stock markets
• oil price uncertainty
• forecasting
• quantile regression