Abstract
AbstractWe use daily data for the period 5 January 2000 to 31 October 2018 to analyse the impact of structural oil supply, oil demand and financial market risk shocks on the level, slope and curvature factors derived from the term structure of interest rates of the U.S. Treasury securities covering maturities of 1–30 years. Linear causality tests detect no evidence of predictability of these shocks on the three latent factors. However, statistical tests performed on the linear model provide evidence of structural breaks and nonlinearity, and hence indicate that the Granger causality test results are based on a misspecified framework, and cannot be relied upon. Given this, we use a nonparametric causality in‐quantiles test to reconsider the predictive ability of the three shocks on the three latent factors, with this model being robust to misspecification due to regime changes and nonlinearity, as it is a data‐driven approach. Moreover, this framework allows us to model the entire conditional distribution of the level, slope and curvature factors, and hence can accommodate, via the lower quantiles, the zero lower bound situation seen in our sample period. Using this robust model, we find overwhelming evidence of causality from the two oil shocks and the risk shock for the entire conditional distribution of the three factors, suggesting the predictability of the entire U.S. term structure based on information contained in oil and financial market innovations. Our results have important implications for academics, investors and policymakers.
Highlights
The existing literature on the impact and oil market price, returns, volatility, and shocks on the moments of equity market of the United States, is huge, to say the least (see, for example, Balcilar, Gupta, and Miller (2015); Balcilar, Gupta, and Wohar (2017) or Gupta and Wohar (2017) for detailed reviews in this regard)
The strongest evidence of predictability at and around the median, which corresponds to the normal state of the yield factors, is in line with the findings of Ioannidis and Ka (2018), who, based on a pre-global financial crisis sub-sample found that oil market disturbances cause relatively stronger impacts on interest rates, compared to when the rates are extremely low under the zero lower bound (ZLB) situation, which in our case is characterized by the lower quantiles of the conditional distributions of Lt, St and Ct
Against the backdrop of sparse literature on the impact of oil shocks on the government bond market of the United States, we analyse the impact of oil supply, oil demand and financial market risk shocks, derived from a structural vector autoregressive (SVAR), on the entire term structure of interest rates, by obtaining three latent factors, level, slope and curvature
Summary
The research about the impact of oil shocks on the term structure of U.S government bond yields is of great research value from the perspectives of both investors and policymakers Against this backdrop, this article contributes to this sparse literature by investigating the impacts of oil and risk shocks on the term structure of interest rates in the U.S Treasury market. To the best of our knowledge, this is the first article to study the predictability of disentangled oil demand, oil supply and financial market risk shocks at a daily frequency on the entire conditional distribution of the level, slope and curvature factors characterizing the complete term structure of interest rates of the United States.
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