Uncertainty Propagation and Dynamic Robust Risk Measures

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 […]

Does National Culture Influence Malfeasance in Banks Around the World?

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 […]

Demographic Issues in Longevity Risk Analysis

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. Lire

Extending and Improving Current Frameworks for Risk Management and Decision-Making: A New Approach for Incorporating Dynamic Aspects of Risk and Uncertainty

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. Lire

Understanding Uncertainty Shocks and the Role of Black Swans

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 […]

Uncertainty and Welfare in Insurance Markets

“… 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.” Lire

Refining Data Protection: Anonymisation and Scope of GDPR

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 of whether data falls under the scope of the #gdpr becomes clearer. Lire

Dealing with Uncertainty in Cyberspace

There are five different common reactions to dealing with, or taming, this #uncertainty in #cyberspace: (1) using #riskmanagement to control uncertainty; (2) recovering from uncertainty through #resilience; (3) mitigating uncertainty through the use of #laws and #regulations; (4) suspending uncertainty by engaging in trust; and (5) ignoring uncertainty through inaction. Lire

Quantifying Uncertainty and Sensitivity in Climate Risk Assessments: Varying Hazard, Exposure and Vulnerability Modelling Choices

“We present a novel approach to quantify the uncertainty and sensitivity of risk estimates, using the CLIMADA open-source climate risk assessment platform. This work builds upon a recently developed extension of CLIMADA, which uses statistical modelling techniques to better quantify climate model ensemble uncertainty. Here, we further analyse the propagation of hazard, exposure and vulnerability […]

Bayesian Model Selection and Prior Calibration for Structural Models in Economic Experiments: Some Guidance for the Practitioner

“Bayesian estimates from experimental data can be influenced by highly diffuse or “uninformative” priors. This paper discusses how practitioners can use their own expertise to critique and select a prior that (i) incorporates our knowledge as experts in the field, and (ii) achieves favorable sampling properties. I demonstrate these techniques using data from eleven experiments […]

Cyber Risk: Hyperconnectivity and the Political Economy of Uncertainty

“This paper explores the notion of ‘cyber risk’, asking how we might understand it through a sociotechnical lens. It pays specific attention to how we can theorise cyber risk as an assemblage of sociotechnical ‘riskscapes’, in which our understanding of risk goes beyond organisational imperatives of ‘risk management’ and into treating cyber risk as a set of productive knowledges and practices within a […]

Fat Tails, Tipping Points and Asymmetric Time Horizons: Dealing With Systemic Climate-Related Uncertainty in the Prudential Regime

“Even pioneering forward-looking stress tests cannot feasibly capture all possible tail risks. We propose supplementing the existing capital requirements regime by giving it a stronger precautionary and macroprudential focus, paying particular attention to the prevention of environmental tipping points to avoid systemic and catastrophic impacts on the financial system and macroeconomy.”    Lire

Regulatory Complexity, Uncertainty, and Systemic Risk: are Regulators Hehogs or Foxes?

“Rebalancing regulation towards simplicity may produce Pareto-improving solutions, and encourage better decision making by authorities and regulated entities. However, addressing systemic risk in a complex financial system should not entail the replacement of overly complex rules with overly simple or less stringent regulations.” Lire

Catastrophic Uncertainty and Regulatory Impact Analysis

“Cost-benefit analysis embodies techniques for the analysis of possible harmful outcomes when the probability of those outcomes can be quantified with reasonable confidence. But when those probabilities cannot be quantified (“deep uncertainty”), the analytic path is more difficult. The problem is especially acute when potentially catastrophic outcomes are involved, because ignoring or marginalizing them could […]

Information, Uncertainty and Espionage

“Decision theory, both orthodox and behavioural, depicts decision rather narrowly as a prioritisation task undertaken within a delineated problem space where the probabilities “sum to one”. From such a perspective, certain perennial challenges in intelligence and counterintelligence appear resolvable when in fact they are not, at least not when approached from the usual direction.” Lire