Nos derniers articles

This lecture explores how probability theory can quantify uncertainty, chance, and even ignorance. He demonstrates methods to measure the quality of these quantified uncertainties. He also humorously admits a miscalculation during the lecture regarding paired comparisons within the audience.

This report uses UK fire statistics to model insurance claims for a company next year. It estimates the total sum of claims by modeling both the number and size of fires as random variables from statistical distributions. Monte Carlo simulations in R are used to predict the probability distribution of total claim costs.

This paper presents a fundamental #mathematical duality linking utility transforms and #probability distortions, which are vital in #decisionmaking under #risk. It reveals that these concepts are characterized by commutation, allowing for simple axiomatization with just one property. Additionally, rank-dependent utility transforms are further characterized under monotonicity conditions.

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.

"We study an optimal #reinsurance problem under a diffusion #riskmodel for an #insurer who aims to minimize the #probability of lifetime ruin."

This paper explores the #reputationalrisk associated with #esg investments and provides a formal theoretical valuation system for ESG reputation. The authors argue that ESG criteria adoption has multiple positive dimensions and outcomes, but the analysis of the #risks related to #sustainability is uncommon. They model ESG reputational risk using paradigms of #behaviouralfinance, defining it by subjective probabilities framed in a #probability function based on potential trustees' preferences. The paper highlights the need for accurate evaluation of reputational risks related to ESG investments by firms and other institutions, including #insurance companies and #pensionfunds.

This paper examines the Bowley solution in the context of #insurance contracts using the expected utility framework. Specifically, the paper analyzes a sequential game between a #policyholder and an #insurer, in which the policyholder selects the optimal #indemnity function and the insurer adjusts the pricing kernel to maximize expected #netprofit. The paper finds that the optimal safety loading factor increases with the policyholder's #riskaversion level and the #probability of zero loss. However, the paper also shows that the Bowley solution is #pareto dominated, meaning that both parties' interests can be further improved.

This study proposes a new approach to the analysis of #systemicrisk in #financialsystems, which is based on the #probability amount of exogenous shock that can be absorbed by the system before it deteriorates, rather than the size of the impact that exogenous events can exhibit. The authors use a linearized version of DebtRank to estimate the onset of financial distress, and compute localized and uniform exogenous shocks using spectral graph theory. They also extend their analysis to heterogeneous shocks using #montecarlo#simulations. The authors argue that their approach is more general and natural, and provides a standard way to express #failure#risk in financial systems.

"We discuss different properties and representations of default #riskmeasures via monetary risk measures, families of related #tailrisk measures, and Choquet capacities. In a second step, we turn our focus on #defaultrisk measures, which are given as worst-case [#probability of #default] PDs and distorted PDs. The latter are frequently used in order to take into account model risk for the computation of #capitalrequirements through risk-weighted assets (#rwas), as demanded by the Capital Requirement #regulation (#crr). In this context, we discuss the impact of different default risk measures and margins of conservatism on the amount of risk-weighted assets."

This study examines the use of #artificialintelligence (#ai) and #bigdata data analytics by #insurers in #belgium for segmentation purposes to determine #claims#probability for prospective policyholders. The implementation of AI and big data analytics can benefit insurers by increasing the accuracy of #riskassessment. However, pervasive segmentation can have negative implications and potentially harm policyholders if their risk is incorrectly calculated. Existing restrictions in #insurance#regulations fall short of protecting policyholders from inaccuracies in risk assessments, potentially resulting in incorrect #premiums or conditions.