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.

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.