ABSTRACT
Pandemic waves are worldwide disasters that can create long-term disruptions in critical industries. Airline transportation is a crucial industry for the US economy. We empirically study how vital industries such as airlines adapt in response to massive disasters like COVID-19. This paper investigates the changes in the network of the US domestic flights caused by the start of the COVID-19 epidemic. Using a novel dataset, we examine the epidemic-induced network adaptations in the US airline industry and quantify the strength of the flight network's response to the epidemic network activation. We find that the overall disruption in the flight network is large in size. When considering a natural multilayer structure of the flight network represented by airlines, we find that the COVID-19 epidemic changes the multilayer structure, and some layers are more resilient than others. © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.
ABSTRACT
We consider a network-based framework for studying causal relationships in financial markets and demonstrate this approach by applying it to the entire U.S. stock market. Directed networks (referred to as "causal market graphs") are constructed based on publicly available stock prices time series data during 2001-2020, using Granger causality as a measure of pairwise causal relationships between all stocks. We consider the dynamics of structural properties of the constructed network snapshots, group stocks into network-based clusters, as well as identify the most "influential" market sectors via the PageRank algorithm. Interestingly, we observed drastic changes in the considered network characteristics in the years that corresponded to significant global-scale events, most notably, the financial crisis of 2008 and the COVID-19 pandemic of 2020.