A moving-window bayesian network model for assessing systemic risk in financial markets.

Systemic risk refers to the uncertainty that arises due to the breakdown of a financial system. The concept of "too connected to fail" suggests that network connectedness plays an important role in measuring systemic risk. In this paper, we first recover a time series of Bayesian networks for stock returns, which allow the direction of links among stock returns to be formed with Markov properties in directed graphs. We rank the stocks in the time series of Bayesian networks based on the topological orders of the stocks in the learned Bayesian networks and develop an order distance, a new measure with which to assess the changes in the topological orders of the stocks. In an empirical study using stock data from the Hang Seng Index in Hong Kong and the Dow Jones Industrial Average, we use the order distance to predict the extreme absolute return, which is a proxy of extreme market risks, or a signal of systemic risks, using the LASSO regression model. Our results indicate that the network statistics of the time series of Bayesian networks and the order distance substantially improve the predictability of extreme absolute returns and provide insights into the assessment of systemic risk.

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

The coronavirus disease 2019 (COVID‐19) pandemic has led to tremendous loss of human life and has severe social and economic impacts worldwide. The spread of the disease has also caused dramatic uncertainty in financial markets, especially in the early stages of the pandemic. In this paper, we adopt the stochastic actor‐oriented model (SAOM) to model dynamic/longitudinal financial networks with the covariates constructed from the network statistics of COVID‐19 dynamic pandemic networks. Our findings provide evidence that the transmission risk of the COVID‐19, measured in the transformed pandemic risk scores, is a main explanatory factor of financial network connectedness from March to May 2020. The pandemic statistics and transformed pandemic risk scores can give early signs of the intense connectedness of the financial markets in mid‐March 2020. We can make use of the SAOM approach to predict possible financial contagion using pandemic network statistics and transformed pandemic risk scores of the COVID‐19 and other pandemics.

Financial Network Connectedness and Systemic Risk During the COVID-19 Pandemic

The COVID-19 pandemic causes a huge number of infections. The outbreak of COVID-19 has not only caused substantial healthcare impacts, but also affected the world economy and financial markets. In this paper, we study the effect of the COVID-19 pandemic on financial market connectedness and systemic risk. Specifically, we test dynamically whether the network density of pandemic networks constructed by the number of COVID-19 confirmed cases is a leading indicator of the financial network density and portfolio risk. Using rolling-window Granger-causality tests, we find strong evidence that the pandemic network density leads the financial network density and portfolio risk from February to April 2020. The findings suggest that the COVID-19 pandemic may exert significant impact on the systemic risk in financial markets.