Global energy market connectedness and inflation at risk

After the outbreak of COVID-19 and the Russia-Ukraine war, historically high inflation has become a problem that plagues many countries, leading researchers to model and explore inflation dynamics from a conditional mean-based perspective to a conditional quantile-based perspective and examine the inflation distribution's tail characteristics, also known as inflation-at-risk. Given the rising uncertainty in the global energy market during COVID-19 and large-scale energy supply shocks brought about by the Russia-Ukraine war, this paper first extracts the global energy connectedness index (GECI) based on the MSCI energy index of 29 countries, then incorporates GECI into the quantile regression to characterize the dynamic distribution of inflation. The results show that the predictive effect of GECI on short-, medium-, and long-term inflation dynamics is mainly reflected in upper quantiles of inflation, with heterogeneity across countries. We find that the dynamic distributions of inflation under the impact of major global events are skewed, and there are significant changes in inflation risks. The out-of-sample forecast verifies the validity and forward-looking property of GECI. The results provide practical regulatory tools and decision-making suggestions for policymakers to prevent and respond to high and low inflation risks.