• Corpus ID: 231786485

Artificial intelligence applied to bailout decisions in financial systemic risk management

@inproceedings{Petrone2021ArtificialIA,
  title={Artificial intelligence applied to bailout decisions in financial systemic risk management},
  author={Daniele Petrone and Neofytos Rodosthenous and Vito Latora},
  year={2021}
}
We describe the bailout of banks by governments as a Markov Decision Process (MDP) where the actions are equity investments. The underlying dynamics is derived from the network of financial institutions linked by mutual exposures, and the negative rewards are associated to the banks’ default. Each node represents a bank and is associated to a probability of default per unit time (PD) that depends on its capital and is increased by the default of neighbouring nodes. Governments can control the… 
View PDF on arXiv

Figures and Tables from this paper

References

SHOWING 1-10 OF 26 REFERENCES
An Artificial Intelligence Approach to Regulating Systemic Risk
TLDR
It is found that under various stress testing scenarios collateralization reduces the costs of resolving a financial system, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations.
A dynamic approach merging network theory and credit risk techniques to assess systemic risk in financial networks
TLDR
The PD model is introduced, a dynamic model that combines credit risk techniques with a contagion mechanism on the network of exposures among banks to quantify systemic risk and reveals the emergence of a strong contagion regime where lower default correlation between banks corresponds to higher losses.
Network models of financial systemic risk: a review
The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible…
A Framework for Assessing the Systemic Risk of Major Financial Institutions
In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against…
Estimating Bilateral Exposures in the German Interbank Market: Is There a Danger of Contagion?
TLDR
It is found that the financial safety net considerably reduces – but does not eliminate – the danger of contagion, and the failure of a single bank could lead to the breakdown of up to 15 % of the banking system in terms of assets.
DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk
TLDR
DebtRank, a novel measure of systemic impact inspired by feedback-centrality, is introduced, finding that a group of 22 institutions, which received most of the funds, form a strongly connected graph where each of the nodes becomes systemically important at the peak of the crisis.
Measuring Systemic Risk: A Risk Management Approach
TLDR
This paper proposes a new method to measure and monitor the systemic risk in a banking system using a sample of international banks from 1988 until 1999 and estimates correlations between bank asset portfolios and compute different measures of systemic risk.
Reducing and sharing the burden of bank failures
This report demonstrates that the contingent liabilities associated with efforts to limit the adverse externalities stemming from failures in the European banking sector are substantially decreasing…
Simulation methods to assess the danger of contagion in interbank markets
Default Recovery Rates in Credit Risk Modelling: A Review of the Literature and Empirical Evidence
Evidence from many countries in recent years suggests that collateral values and recovery rates (RRs) on corporate defaults can be volatile and, moreover, that they tend to go down just when the…
...
...