"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 paper employs #computational #linguistics to introduce a novel text-based measure of firm-level #cyberrisk exposure based on quarterly earnings conference calls of listed firms. Our quarterly measures are available for more than 13,000 firms from 85 countries over 2002-2021. ... The geography of cyber risk exposure is well approximated by a gravity model extended with cross-border portfolio flows. Back-of-the-envelope calculations suggest that the global #cost of cyber risk is over $200 billion per year."
The authors use mid-quantile regression to deal with ordinal #riskassessments and compare their approach to current alternatives for #cyberrisk ranking and graded responses. They test their #model on both simulated and real data and discuss its applications to #threatlintelligence.
"... we find that the #bayesian approach outperforms the classical [#counterparty #risk #model] in identifying whether a model is correctly specified, which is the principal aim of any backtesting framework."
A #regulatory #reform that imposes greater regulatory #capital #costs for #insurers to provide property coverage in catastrophe-prone areas results in price increases, though the magnitude of the increases is restrained due to #insurance pricing #regulation. The increase in price is commensurate to 12-30% of the increase in regulatory capital costs due to catastrophes, and the increase in price is larger for areas with higher hurricane risks, suggesting that consumers in risky areas bear the cost of #climatechange.
Traditional #statistical and #algorithm-based methods used to analyze #bigdata often overlook small but significant evidence. #bayesian #statistics, driven by #conditional #probability, offer a solution to this challenge. The review identifies two main applications of Bayesian statistics in #finance: prediction in financial markets and credit risk models. The findings aim to provide valuable insights for researchers aiming to incorporate Bayesian methods and address the sample size issue effectively in #financial #research.
"The #eu proposal for the #artificialintelligenceact (#aia) defines four #risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of #ai systems (#ais), the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. Our suggestion is to apply the four categories to the risk #scenarios of each AIs, rather than solely to its field of application."
This paper claims to contribute to the understanding of #peertopeer, #decentralized distributed #insurance as a viable alternative to traditional insurance models, offering potential solutions to address market consolidation and enhance #financialinclusion through #risksharing. Further exploration and empirical studies are necessary to validate the viability and long-term implications of this emerging paradigm in the #insuranceindustry.
"Among investor categories, #insurers and households appear to be the most exposed to #climaterisks through the intermediary of #investmentfunds. However, insurers seem to be aware of this #risk and tend to invest in funds with low exposure to brown assets."
"In the rapidly evolving world of #ai technology, creating a robust #regulatoryframework that balances the benefits of AI #chatbots [like #chatgpt] with the prevention of their misuse is crucial."