Stochastic actor-oriented modelling of the impact of COVID-19 on financial network evolution

Dynamic Causality Analysis of COVID-19 Pandemic Risk and Oil Market Changes

Crude oil draws attention in recent research as its demand may indicate world economic growth trend in the post-COVID-19 era. In this paper, we study the dynamic lead–lag relationship between the COVID-19 pandemic and crude oil future prices. We perform rolling-sample tests to evidence whether two pandemic risk scores derived from network analysis, including a preparedness risk score and a severity risk score, Granger-cause changes in oil future prices. In our empirical analysis, we observe 49% to 60% of days in 2020 to 2021 during which the pandemic scores significantly affected oil futures. We also find an asymmetric lead–lag relationship, indicating that there is a tendency for oil futures to move significantly when the pandemic is less severe but not when it is more severe. This study adopts preparedness risk score and severity risk score as proxy variables to measure the impact of the COVID-19 pandemic risk on oil market. The asymmetric lead–lag behavior between pandemic risk and oil future prices provides insights on oil demand and consumption during the COVID-19 pandemic.

Assessing systemic risk in financial markets using dynamic topic networks

Systemic risk in financial markets refers to the breakdown of a financial system due to global events, catastrophes, or extreme incidents, leading to huge financial instability and losses. This study proposes a dynamic topic network (DTN) approach that combines topic modelling and network analysis to assess systemic risk in financial markets. We make use of Latent Dirichlet Allocation (LDA) to semantically analyse news articles, and the extracted topics then serve as input to construct topic similarity networks over time. Our results indicate how connected the topics are so that we can correlate any abnormal behaviours with volatility in the financial markets. With the 2015–2016 stock market selloff and COVID-19 as use cases, our results also suggest that the proposed DTN approach can provide an indication of (a) abnormal movement in the Dow Jones Industrial Average and (b) when the market would gradually begin to recover from such an event. From a practical risk management point of view, this analysis can be carried out on a daily basis when new data come in so that we can make use of the calculated metrics to predict real-time systemic risk in financial markets.