The Agent Rationality in the Doom Loop of Sovereign Debt: An Agent-Based Model Simulation of Systemic Risk Emergence Process

2018 ◽  
pp. 197-210
Author(s):  
Paulo Sérgio Rosa ◽  
Célia G. Ralha ◽  
Ivan Ricardo Gartner
Keyword(s):  
Systemic Risk ◽  
Sovereign Debt ◽  
Model Simulation ◽  
Agent Based Model ◽  
Agent Based

scholarly journals Conceptual Agent Based Model Simulation for the Port Nautical Services

2019 ◽  
Vol 52 (3) ◽  
pp. 19-24
Author(s):  
I.Y. Davydenko ◽  
R.W. Fransen
Keyword(s):  
Model Simulation ◽  
Agent Based Model ◽  
Agent Based

MACRO- AND MICROPRUDENTIAL POLICIES: SWEET AND LOWDOWN IN A CREDIT NETWORK AGENT-BASED MODEL

2019 ◽  
pp. 1-20
Author(s):  
Ermanno Catullo ◽  
Federico Giri ◽  
Mauro Gallegati
Keyword(s):  
Systemic Risk ◽  
Financial Stability ◽  
Agent Based Model ◽  
Agent Based ◽  
Macroprudential Policies ◽  
The Stability ◽  
The Individual ◽  
The Cost ◽  
Individual Choices ◽  
Credit Network

The paper presents an agent-based model reproducing a stylized credit network that evolves endogenously through the individual choices of firms and banks. We introduce in this framework a financial stability authority in order to test the effects of different prudential policy measures designed to improve the resilience of the economic system. Simulations show that a combination of micro- and macroprudential policies reduces systemic risk but at the cost of increasing banks’ capital volatility. Moreover, the agent-based methodology allows us to implement an alternative meso regulatory framework that takes into consideration the connections between firms and banks. This policy targets only the more connected banks, increasing their capital requirement in order to reduce the diffusion of local shocks. Our results support the idea that the mesoprudential policy is able to reduce systemic risk without affecting the stability of banks’ capital structure.

scholarly journals An Agent-Based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

SOVEREIGN DEFAULT CONTAGION AND MONETARY POLICY IN AN AGENT-BASED MODEL

2020 ◽  
pp. 2050010
Author(s):  
JOÃO SILVESTRE
Keyword(s):  
Monetary Policy ◽  
Sovereign Debt ◽  
Debt Crisis ◽  
Sovereign Default ◽  
Quantitative Easing ◽  
Agent Based Model ◽  
Agent Based ◽  
European Central ◽  
Default Contagion ◽  
Sovereign Defaults

Sovereign default contagion was one of the most debated topics during the Eurozone sovereign debt crisis. Despite all the improvements in the financial situation since 2010, namely after European Central Bank quantitative easing policies, the nature of the problem and the policy prescriptions are still under dispute today. Using an agent-based model, we simulate sovereign default contagion for different monetary policy options in a world where governments have random incomes, different heterogeneous borrowing behaviors and risk aversion levels and where countries can enter into ex-ante agreements to protect themselves against default. Our simulations showed that default contagion can be a very fast and “destructive” process, and that monetary policy can have a very important role in preventing sovereign defaults through zero interest rate and quantitative easing policies.

scholarly journals An Agent-based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Vol 8 (7) ◽  
pp. 313 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

The commute of residents in a big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make the fine description of urban residents’ travel behaviors possible, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, we simulated the travel behaviors of commuters: the spatial context of the model is set up using the existing urban road network and by dividing the area into space units. Then, using the mobile phone call detail records of a month, statistics of residents’ travel during the four time slots in working day mornings are acquired and then used to generate the Origin-Destination matrix of travels at different time slots, and the data are imported into the model for simulation. Under the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can induce backward the causes of traffic congestion using the simulation results and the Origin-Destination matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

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