Nos derniers articles

Examining the Great Depression, we use novel methods and data to show that despite 9,000 #bank closures, #risk increased instead of leaving the system. Healthier #banks acquired risk through mergers, with each acquisition raising the acquiring bank's risk by 25%. #financialcrises don't rapidly eliminate risk; merger policies affect #systemicrisk.

"We examine the impact of the U.S. withdrawal from the #parisagreement on the relationship between #climaterisk and #systemicrisk of #us #globalbanking. We find that after 2017, investors stopped pricing climate risk into U.S. systemic risk directly, consistent with domestic investors expecting climate risk #deregulation. However, climate risk still indirectly impacts the U.S. systemic risk through the internal capital markets of U.S. #global #banks operating abroad."

This article discusses the need for high-level frameworks to guide the #regulation of #artificialintelligence (#ai) technologies. It adapts a #fintechinnovation Trilemma framework to argue that regulators can prioritize only two of three aims when considering AI oversight: promoting #innovation, mitigating #systemicrisk, and providing clear #regulatoryrequirements.

"#systemicrisk measures have been shown to be predictive of #financialcrises and declines in real activity. Thus, forecasting them is of major importance in #finance and #economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (#mes)."

This study proposes a new approach to the analysis of #systemicrisk in #financialsystems, which is based on the #probability amount of exogenous shock that can be absorbed by the system before it deteriorates, rather than the size of the impact that exogenous events can exhibit. The authors use a linearized version of DebtRank to estimate the onset of financial distress, and compute localized and uniform exogenous shocks using spectral graph theory. They also extend their analysis to heterogeneous shocks using #montecarlo#simulations. The authors argue that their approach is more general and natural, and provides a standard way to express #failure#risk in financial systems.

"In this paper we propose efficient #bayesian Hamiltonian #montecarlo method for estimation of #systemicrisk#measures , LRMES, SRISK and ΔCoVaR, and apply it for thirty global systemically important banks and for eighteen largest #us#financialinstitutions over the period of 2000-2020. The systemic risk measures are computed based on the Dynamic Conditional Correlations model with generalized asymmetric #volatility. A policymaker may choose to rank the firms using some quantile of their systemic risk distributions such as 90, 95, or 99% depending on #risk preferences with higher quantiles being more conservative."

This paper proposes a #credit#portfolio approach for evaluating #systemicrisk and attributing it across #financialinstitutions. The proposed model can be estimated from high-frequency credit default swap (#cds) data and captures risks from publicly traded #banks, privately held institutions, and coöperative banks. The approach overcomes limitations of earlier studies by accounting for correlated losses between institutions and also offers a modeling extension to account for #fattails and #skewness of #assetreturns. The model is applied to a universe of banks in #europe, highlighting discrepancies between the #capitaldequacy of the largest contributors to systemic risk and less systemically important banks.

The paper discusses the risks posed by #artificialintelligence (#ai) systems, from biased lending algorithms to chatbots that spew violent #hatespeech. The author argues that policymakers have a responsibility to consider broader, longer-term #risks from #aitechnology, such as #systemicrisk and the potential for misuse. While #regulatory proposals like the #eu #aiact and the #whitehouse AI Bill of Rights focus on immediate risks, they do not fully address the need for #algorithmicpreparedness. It proposes a roadmap for algorithmic preparedness, which includes five forward-looking principles to guide the development of regulations that confront the prospect of algorithmic black swans and mitigate the harms they pose to society. This approach is particularly important for general purpose systems like #chatgpt, which can be used for a wide range of applications, including ones that may have unintended consequences. The article emphasizes the need for #governance and #regulation to ensure that #aisystems are developed and used in ways that minimize risk and maximize benefit, and it references the #nist AI #riskmanagement Framework as a potential tool for achieving this goal.

Proposes a set of novel modeling mechanisms to regulate the size of banks' macroprudential capital buffers by using market-based estimates of systemic risk combined with a structural framework for credit risk assessment. It applies the model to the European banking sector and finds differences with the capital buffers currently assigned by national regulators, which have substantial implications for systemic risk in the EEA.

"Insights from scenario analysis may help inform the use of ‘hard’ macroprudential tools to foster the robustness and resilience of the banking system against climate-induced shocks. Against the backdrop of the ongoing reform of the EU’s macroprudential framework, the paper explores how the macroprudential toolkit could be adjusted to the reality of climate-related financial risks."