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

Chu, Amanda M. Y.; Chan, Lupe S. H.; So, Mike K. P.

doi:10.1002/sta4.408

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…The paper 45 documented the extraordinarily high financial network connectedness due to the impacts of the COVID-19 pandemic. In addition, the papers 46 , 47 presented evidence that the pandemic network connectedness can lead the financial network connectedness. The above research documented the possible contagion or transmission of the pandemic risk to financial risk.…”

Section: Discussionmentioning

confidence: 99%

“…Initially, network analysis was applied predominantly in the field of social sciences 36 , but it has now been extended to other fields of study: for example, in the field of medicine to understand the transmission of infectious diseases 37 , the relations between genetics and human diseases 38 , and drug-target interactions 39 and in the finance field to assess systemic risk and contagion effects in financial markets 40 . Due to the global pandemic, an increasing number of studies have also made use of network analysis to predict and visualise pandemic risk 41 – 44 and its influence on financial market connectedness 45 – 47 .…”

Section: Introductionmentioning

confidence: 99%

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Assessing systemic risk in financial markets using dynamic topic networks

Mak

Chu

2022

Sci Rep

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing systemic risk in financial markets using dynamic topic networks

Mak

Chu

2022

Sci Rep

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“…SAOMs have been used to model the evolution of friendship (Boda et al, 2020), collaborative networks (Cao et al, 2017), or financial networks (Chu et al, 2021). In social sciences, the nodes in the graph are actors while the edges describe a relation between actors, e.g.…”

Section: Stochastic Actor Oriented Models (Saom)mentioning

confidence: 99%

Unlocking social network analysis methods for studying human mobility

Wiedemann

Henry

Raubal

2022

AGILE GIScience Ser.

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Abstract. Planning and operations in urban spaces are strongly affected by human mobility behavior. A better understanding of individual mobility is key to improve transportation systems and to guide the allocation of public space. Previous studies have discovered statistical laws of travel distances, but the topology of movement between places has received little attention. We propose to employ network modelling methods to analyze the effect of spatial and context attributes on individual movement patterns. The perspective of mobility as a network allows to explicitly regard dyadic dependencies of sequential location visits. Here, we consider two methods developed for social networks and provide a formulation of mobility networks to justify their applicability. First, we use the Multiple Regression Quadratic Assignment Procedure to test hypotheses on the influence of location attributes on mobility behavior. Secondly, Stochastic Actor-Oriented Models are applied to model the evolution of mobility networks over time. As a proof-of-concept study, we transform data from one GNSS-based and one check-in based dataset into mobility networks and present results from both methods. We find relations that appear for a majority of samples and thus seem inherent to mobility networks. The differences between individuals and the available datasets are further quantified and discussed. We conclude that the transfer of network modeling methods is an interesting opportunity to study network-related phenomena in geographic information science.

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“…Rolling-window methods have been applied to financial risk management in previous studies. For example, [15] uses a rolling-window method to learn a time series of undirected networks for a stochastic actor-oriented model; [16] proposes a textual modeling method using a rolling-window scheme in a regression model, to predict a market volatility proxy based on the GARCH residuals; [17] studies the effect of the COVID-19 pandemic on financial market connectedness and systemic risk using rolling-window Granger-causality tests; and [18] uses a rolling-window method and a combined method with the topic modeling and network analysis to assess systemic risk in financial markets. Network analysis has been applied to risk assessment [19], pandemic risk management [20][21][22] and financial risk management [13,18,23,24].…”

Section: Introductionmentioning

confidence: 99%

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

Chan

Chu

2023

PLoS ONE

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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.

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

Cited by 9 publications

References 30 publications

Assessing systemic risk in financial markets using dynamic topic networks

Assessing systemic risk in financial markets using dynamic topic networks

Unlocking social network analysis methods for studying human mobility

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

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