COVID-19 and stock returns: Evidence from the Markov switching dependence approach

Bouteska, Ahmed; Sharif, Taimur; Abedin, Mohammad Zoynul

doi:10.1016/j.ribaf.2023.101882

Cited by 30 publications

(8 citation statements)

References 85 publications

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“…They assume that the dependence structure between financial assets is constant regardless of market conditions. In response to this limitation, Markov regime-switching copula models have been developed [16][17][18]54]. These models are highlighted for their ability to eliminate the need to temporally determine the point of change in the dependence structure, as emphasized by Boubaker and Sghaier [55].…”

Section: Literature Reviewmentioning

confidence: 99%

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Hu,

Zhu,

Zhang

et al. 2024

PLoS ONE

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The linkages between the US and China, the world’s two major agricultural powers, have brought great uncertainty to the global food markets. Inspired by these, this paper examines the extreme risk spillovers between US and Chinese agricultural futures markets during significant crises. We use a copula-conditional value at risk (CoVaR) model with Markov-switching regimes to capture the tail dependence in their pair markets. The study covers the period from January 2006 to December 2022 and identifies two distinct dependence regimes (stable and crisis periods). Moreover, we find significant and asymmetric upside/downside extreme risk spillovers between the US and Chinese markets, which are highly volatile in crises. Additionally, the impact of international capital flows (the financial channel) on risk spillovers is particularly pronounced during the global financial crisis. During the period of the COVID-19 pandemic and the Russia-Ukraine 2022 war, the impact of supply chain disruptions (the non-financial channel) is highlighted. Our findings provide a theoretical reference for monitoring the co-movements in agricultural futures markets and practical insights for managing investment portfolios and enhancing food market stability during crises.

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Section: Literature Reviewmentioning

confidence: 99%

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Hu,

Zhu,

Zhang

et al. 2024

PLoS ONE

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“…Due to the time‐varying nature of financial variables, especially exhibiting significant regime‐switching behavior before and after some stream events, like COVID‐19, and so forth. The nonlinear dynamics analysis framework, the Markov switching technique, is always used in describing the character of financial variables (Bouteska et al, 2023; Just & Echaust, 2020; Liu & Lee, 2021). Besides, some non‐parametric techniques, like recurrence quantification analysis and cross‐recurrence plot analysis, are also applied in financial research.…”

Section: Literature Reviewmentioning

confidence: 99%

The dynamics of crude oil future prices on China's energy markets: Quantile‐on‐quantile and casualty‐in‐quantiles approaches

Meng,

Mo,

Nie

2023

Journal of Futures Markets

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This study employs the quantile‐on‐quantile method, casualty‐in‐quantiles method, and rolling window regression to investigate the impact of international crude oil future prices on the stock prices of both traditional and new energy sectors in China. The empirical results reveal that the effect of oil future prices on the energy stock market in China varies across quantiles and is easily affected by extreme events. Specifically, the impact of oil future prices on the new energy stock market is significant and volatile, while it is less volatile and displays a negative correlation with the traditional energy market. Furthermore, a concentrated positive correlation is observed in the middle and low quantile stages of the energy stock market. A significant Granger causality exists between oil future prices and the energy stock market in different quantiles. Those findings can provide useful guidance for policymakers, investors, and consumers.

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“…Tuffour et al ( 2021 ) indicated that soft loans, guarantee support, and statutory payment interventions seem to support small enterprises in revamping their operations. Undoubtedly, the COVID-19-related government interventions affected economic activity and investor sentiment, which were reflected in financial markets (Aharon & Siev, 2021 ; Ahmed et al, 2023 ; Ashraf, 2020 ; Chai et al, 2022 ; He et al, 2020b ; Topcu & Gulal, 2020 ). On the one hand, the interventions have generally been linked to strong negative financial market reactions (Aharon & Siev, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

et al. 2023

Self Cite

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Evaluating and understanding the financial impacts of COVID-19 has emerged as an urgent research agenda. Nevertheless, the impacts of government interventions on stock markets remain poorly understood. This study explores, for the first time, the impact of COVID-19 related government intervention policies on different stock market sectors using explainable machine learning-based prediction models. The empirical findings suggest that the LightGBM model provides excellent prediction accuracy while preserving computationally efficient and easy explainability of the model. We also find that COVID-19 government interventions are better predictors of stock market volatility than stock market returns. We further show that the observed effects of government intervention on the volatility and returns of ten stock market sectors are heterogeneous and asymmetrical. Our findings have important implications for policymakers and investors in terms of promoting balance and sustaining prosperity across industry sectors through government interventions.

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COVID-19 and stock returns: Evidence from the Markov switching dependence approach

Cited by 30 publications

References 85 publications

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

Extreme risk spillovers between US and Chinese agricultural futures markets in crises: A dependence-switching copula-CoVaR model

The dynamics of crude oil future prices on China's energy markets: Quantile‐on‐quantile and casualty‐in‐quantiles approaches

An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors

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