Nos derniers articles

The paper examines non-linearities in how geopolitical risk (GPR) shocks affect the economy. Using a VARX model, it finds that large GPR shocks (above 4 standard deviations) significantly increase uncertainty, leading to precautionary saving and reduced consumption, with a more moderate impact on inflation due to conflicting demand and uncertainty effects.

The framework presents a method to quantify #uncertainty propagation in #dynamic #scenarios, focusing on discrete #stochastic processes over a limited time span. These dynamic uncertainty sets encompass various uncertainties like distributional ambiguity, utilizing tools like the Wasserstein distance and $f$-divergences. Dynamic robust #risk #measures, defined as maximum #risks within uncertainty sets, exhibit properties like convexity and coherence based on uncertainty set conditions. $f$-divergence-derived sets yield strong time-consistency, while Wasserstein distance leads to a new non-normalized time-consistency. Recursive representations of one-step conditional robust risk measures underlie strong or non-normalized time-consistency.

"We study a #reinsurer who faces multiple sources of #model #uncertainty. The reinsurer offers contracts to n #insurers whose #claims follow different compound #poisson processes. As the reinsurer is uncertain about the insurers' claim severity distributions and frequencies, they design reinsurance contracts that maximise their expected wealth subject to an #entropy #penalty…."

The study investigates the influence of national culture on the severity of global #bank#misconduct. It finds that cultural traits such as over-confidence and #uncertainty avoidance play a significant role in determining misconduct levels. The research underscores the importance of #regulatory measures and #supervisory independence in countering cultural effects on #financial#malfeasance. These findings hold implications for #regulators, #policymakers, and professionals within the #bankingsector.

The paper discusses #modeling #longevity #risk, focusing on assumptions in #demographic #forecasting to project past data into the future. #stochastic forecasts are crucial to quantify #uncertainty in cohort survival predictions, including process variance and parameter/model errors.

The paper discusses the importance of #riskinformed #decisionmaking -and the use of #riskassessment to support decisions. It highlights the need for a more dynamic approach to #riskmanagement, which takes into account #uncertainty, changes in systems, phenomena, or values that could alter the underlying premises of the initial risk assessment.

We offer a #datadriven theory of #belief formation that explains sudden surges in economic #uncertainty and their consequences. It argues that people, like #bayesian econometricians, estimate a distribution of macroeconomic outcomes but do not know the true distribution. The paper shows how real-time estimation of distributions with non-normal tails can result in large fluctuations in uncertainty, particularly related to tail events or "black swans." Using real-time GDP data, the authors find that revisions in estimated #blackswan #risk explain most of the fluctuations in uncertainty. These findings highlight the importance of #accounting for the effects of uncertainty and non-normality in economic decision-making and #policymaking.

"From a #riskmanagement perspective, it is challenging to #model physical and #transitionrisks given the #uncertainty around #climaterisk drivers, such as changes in #governmentpolicy aimed at reducing #greenhousegasemissions, the pace of technological change, and uncertainty around the transmission channels. A dearth of in-house modeling tools and reliance on #thirdparty vendors also hamper #banks’ ability to properly understand and manage #risks. The most recent #boe climate biennial exploratory scenario (#cbes) noted that “banks varied in their ability to scrutinize and understand the strengths and weakness of third-party models, and adapt them appropriately to the CBES.” As a result, projected #losses for banks varied widely, suggesting a high degree of uncertainty about the magnitude of climate risks as well as a limited ability to accurately reflect such risks in business decisions."

"… we find that the #uncertainty premium is negatively correlated with #riskaversion at all sizes and #probabilities of #risks. This leads to a selection effect: individuals who purchase #insurance are not necessarily the most risk averse. We show that the resulting #misallocation of insurance leads to large #welfare#losses."

This paper explores the #uncertainty around when #data is considered "#personaldata" under #dataprotection#laws. The authors propose that by focusing on the specific #risks to #fundamentalrights that are caused by #dataprocessing, the question whether data falls under the scope of the #gdpr becomes clearer.