The dynamics of cross‐boundary fire—Financial contagion between the oil and stock markets

Abstract

Motivated by the complex dynamics between the oil and stock markets, this study develops a dynamic Markov regime switching‐copula‐extreme value theory model to quantitatively investigate financial contagion and its characteristics between these two markets. The proposed model is applied to daily returns on crude oil prices and the stock markets in the United States and China over six major extreme downside risk events. We find that financial contagion is shorter, stronger, and more susceptible to extreme downside shocks in the United States than in China. In addition, the COVID‐19 crisis shows the largest financial contagion compared with previous crises.

Pandemic-driven financial contagion and investor behavior: Evidence from the COVID-19

“…There is a large amount of and still growing body of literature on the copula function due to its flexibility in describing various patterns of dependence structure such as non-linearly, asymmetry, dynamic, and tail dependence ( Abakah et al, 2021 , Chabi-Yo et al, 2018 , Christoffersen et al, 2012 , Hüttner et al, 2020 , Sahamkhadam et al, 2022 , Supper et al, 2020 , Wang and Dyer, 2012 , Wang, Yuan, Wang, 2021 ). A copula captures the dependence structure of a multivariate distribution and is defined as a multivariate distribution function with standard uniform margins.…”

“…There are a variety of copulas in the literature and each copula captures a different dependence structure and dependence degree. Therefore, an appropriate copula function should be selected depending on the nature of financial contagion, which suggests the exploration of extreme dependence (especially in the lower tail) between two markets rather than the widely used correlation in the literature ( Wang, Yuan, Wang, 2021 , Ye et al, 2017 ). Therefore, we are merely interested in the lower tail dependence and looking for the copula that captures the lower tail dependence feature.…”

“…For instance, some methods such as the vector autoregression approach and GARCH model are based on linear assumptions and ignore the non-linear dependence that is usually observed between financial markets ( Wang, Yuan, Li et al, 2021 ). Although some other methods such as the quantile regression approach and DCCA method could capture some non-linear dependence, they are not designed to model the entire dynamic tail dependence that is more appropriate for financial contagion ( Wang, Yuan, Wang, 2021 , Ye et al, 2017 ). To overcome these challenges, copula models have been proposed to describe the complex dynamics including non-linear and dynamic tail dependence, and have been widely used to study financial contagion ( Aristeidis and Elias, 2018 , Jayech, 2016 , Wang, Yuan, Li et al, 2021 , Wang, Yuan, Wang, 2021 ).…”

“…However, they cannot adequately approximate the tail behavior of the marginal distribution, yet the tail behavior is essential in measuring financial contagin. To address this issue, in addition to the GARCH model, Wang, Yuan, Wang (2021) model the tail behavior of the marginal distribution with EVT and construct a dynamic copula-EVT model to detect the existence of financial contagion. The dynamic copula-EVT model incorporates both the tail behavior and the complex tail dependence structure between financial markets.…”

“…The research related to the financial contagion driven by pandemic events (such as SARS in 2003, H1N1 in 2009, Ebola in 2014, and ZIKA in 2016) is limited until recently, when there has been a surge of interest in studying it after the outbreak of the COVID-19 ( Akhtaruzzaman et al, 2021 , Aslam et al, 2020 , Duan et al, 2021 , Guo et al, 2021 , Liao et al, 2021 ). Wang, Yuan, Wang (2021) investigate the financial contagion between oil and stock markets during the COVID-19 and show that the magnitude of financial contagion exceeds that during the 2008 financial crisis. However, little attention is dedicated to the influence factors of the pandemic-driven financial contagion, as it is of great practical importance for risk managers to make effective policies to mitigate risk from the pandemic shock.…”