Abstract:
We use daily data for the period 25th November 1985 to 10th March 2020 to analyze the impact of newspapers-based measures of geopolitical risks (GPRs) on United States (US) Treasury securities by considering the level, slope and curvature factors derived from the term structure of interest rates of maturities covering 1 to 30 years. No evidence of predictability of the overall GPRs (or for threats and acts) is detected using linear causality tests. However, evidence of structural breaks and nonlinearity is provided by statistical tests performed on the linear model, which indicates that the Granger causality cannot be relied upon, as they are based on a misspecified framework. As a result, we use a data-driven approach, specifically a nonparametric causality-in-quantiles test, which is robust to misspecification due to regime changes and nonlinearity, to reconsider the predictive ability of the overall and decomposed GPRs on the three latent factors. Moreover, the zero lower bound situation, visible in our sample period, is captured by the lower quantiles, as this framework allows us to capture the entire conditional distribution of the three factors. Using this robust model, we find overwhelming evidence of causality from the GPRs, with relatively stronger effects from threats than acts, for the entire conditional distribution of the three factors, with higher impacts on medium- and long-run maturities, i.e., curvature and level factors, suggesting the predictability of the entire US term structure based on information contained in GPRs. Our results have important implications for academics, investors and policymakers.
