This article addresses the increasing concern of investors regarding corporate wrongdoing impacting a company's stock price, particularly regarding #esgrisk. The article argues that courts should not dismiss ESG securities #fraud cases as mere puffery, but instead focus on whether the corporation and its managers knew of a material #risk of an #esg problem but deceptively obscured that risk in its communications with investors.
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.
This research presents a balance #scorecard tool for assessing #disasterriskreduction and #resilience (#dr3) in the context of #floods, #droughts and #heatwaves. It aims to support the integration and monitoring of #climateadaptation, #sustainability and #riskreduction into development planning in vulnerable communities. This approach contributes to strengthening #governance, resilience and #riskmanagement in disaster-prone areas.
This paper discusses the relationship between standards and private law in the context of #liability #litigation and #tortlaw for damage caused by #ai systems. The paper highlights the importance of #standards in supporting policies and legislation of the #eu, particularly in the #regulation of #artificialintelligence. The paper assesses the role of AI standards in private law and argues that they contribute to defining the duty of care expected from developers and professional operators of AI systems.
This article examines the downfall of #Enron Corporation, which is often seen as the epitome of corporate #fraud. Enron engaged in complex structured hedging transactions to achieve accounting results, which ultimately led to its collapse and the imprisonment of some of its #managers. The article aims to present the facts objectively and asks what advice should have been given to Enron's managers. It suggests that society can overreact to #businessfailure and #regulatory responses can miss the mark, and that corporate managers must take reasonable risks to remain competitive. The article concludes that failure should not automatically be judged as managerial misfeasance.
This paper focuses on predicting #corporate #default #risk using frailty correlated default #models with subjective judgments. The study uses a #bayesian approach with the Particle Markov Chain #montecarlo algorithm to analyze data from #us public non-financial firms between 1980 and 2019. The findings suggest that the volatility and mean reversion of the hidden factor have a significant impact on the default intensities of the firms.
"#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)."
"We study an optimal #reinsurance problem under a diffusion #riskmodel for an #insurer who aims to minimize the #probability of lifetime ruin."
"... the new Climate Risk Division will integrate climate risks into its supervision of regulated entities, support the industry’s growth in managing climate risks, coordinate with international, national, and state regulators, develop internal capacity on climate-related financial risks, support the capacity-building of peer regulators on climate-related supervision, and ensure fair access to financial services for all communities, especially those most impacted by climate change. "
This paper presents an extension of #statistical inference for smoothed quantile estimators from finite domains to infinite domains. A new truncation methodology is proposed for discrete loss distributions with infinite domains. #simulation studies using several distributions commonly used in the #insuranceindustry show the effectiveness of the methodology. The authors also propose a flexible bootstrap-based approach and demonstrate its use in computing the conditional five number summary (C5NS) for tail risk and constructing confidence intervals for each of the five quantiles that make up C5NS. Results using #automobile #accident #data show that the smoothed quantile approach produces more accurate classifications of tail #risk and lower coefficients of variation in the estimation of tail #probabilities compared to the linear interpolation approach.