Tail-risk spillovers and interconnectedness in international logistics markets : a QVAR approach

This research explores the interdependence within the international logistics sector among 17 nations, utilizing a quantile-based technique to assess the transmission of returns. By analyzing daily data from DataStream spanning from 1 June 2016, to 12 August 2024, we apply the Quantile Vector Autoregression framework to examine the synchronous behavior of variables, considering the magnitude of shocks. Our findings reveal varying degrees of linkage at the lower, median, and upper quantiles of the conditional distribution. The results show that extreme events, such as the COVID-19 pandemic and the Russia-Ukraine war, significantly amplified spillovers across logistics markets, while the impact of the Israel-Hamas conflict was more regionally contained. Regional clustering and geographical proximity play a crucial role, with stronger interconnections observed among neighboring countries, such as the US and Canada, and Germany and France. The US stands out as a dominant transmitter of shocks, while countries in Asia and Oceania tend to be net receivers, highlighting their vulnerability to external disruptions. These results underscore the need for quantile-based risk assessments in regulatory frameworks and risk management strategies to better manage asymmetric risk transmissions during global crises. The current study makes significant theoretical contributions to understanding interdependence and risk transmission in the global logistics industry, particularly under extreme market conditions. By employing the Quantile Vector Autoregression (QVAR) approach, we move beyond traditional mean-based analyses, which often overlook the critical behavior of markets during crises. Our focus on tail-risk spillovers highlights how extreme positive and negative shocks exert a much stronger influence on market dynamics than median shocks, advancing the literature on market interdependencies by introducing a more nuanced perspective on risk transmission during periods of stress and stability.