65 résultats pour « banks »

Emerging climate litigation impacts on the banking industry

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The paper examines climate litigation's growing impact on banks, noting limited current effects but a projected increase. Key risks include reputational damage and influences on risk management and investment decisions. Banks are urged to address climate litigation risks proactively to enhance resilience, with future research suggested on mitigation strategies.

Distrust Spillover on Banks: The Impact of Financial Advisory Misconduct

Local communities exposed to #fraudulent #investmentadvisory firms tend to withdraw deposits from their affiliated #banks, even though the banks are not involved in the #misconduct. The #reputationalrisk is more significant when banks share names with fraudulent advisory firms or are located in areas with high social norms. The author establishes causality by exploring a quasi-natural experiment in which #fraud is likely exogenously revealed.

The Informational Impact of Prudential Regulations

"#banks take costly actions (such as higher #capitalization, #liquidity holding, and advanced #riskmanagement) to avoid financial distress and #bankruns ... We show that #prudential #regulations have an informational impact: sufficiently tight regulations can eliminate inefficient separating equilibria in banks’ signaling game, thereby changing the information available to creditors and their incentives to run."

Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations

This paper examines the use of #machinelearning methods in the context of #banks' #capitalrequirements, specifically the internal Ratings Based (#irb) approach. The authors discuss the advantages and risks of using machine learning in this domain, and provide recommendations related to #risk parameter estimations, #regulatory capital, the trade-off between performance and interpretability, international #banking competition, and #governance, #operationalrisk, and training.

Application of Deep Reinforcement Learning in Asset Liability Management

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This paper introduces the application of Deep Reinforcement Learning (#drl) in #alm, addressing limitations of traditional methods reliant on human judgement. The findings highlight the potential of DRL to enhance #riskmanagement outcomes for #insurers, #banks, #pensionfunds, and #assetmanagers, providing improved adaptability to changing market conditions.

Climate Risk Contagion of U.S. Banks

"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."