This study provides semi-explicit formulas for inf-convolution and optimal allocations, considering homogeneous, conditional, and absolutely continuous beliefs. The research also explores inf-convolution between Lambda value at risk and other risk measures, discussing optimal allocations and alternative Lambda value at risk definitions.
“ In this paper, we propose an efficient important sampling method for distortion risk measures in such models that reduces the computational cost through machine learning. We demonstrate the applicability and efficiency of the Monte Carlo method in numerical experiments on various distortion risk measures and models.”An Integrated App”
Climate risk modeling provides quantitative data on potential risks at various scales. However, integrating qualitative data and local knowledge can enhance and validate these assessments.
This study explores a Bayesian approach to Pay-As-You-Drive (PAYD) insurance, using Naive Bayes classifiers and Bayesian Networks for risk assessment. It achieved 87.5% accuracy in predicting risk and improved interpretability over traditional models, optimizing pricing strategies and promoting affordable coverage by dismissing geographic grouping in insurance pricing.
“... commitments to ESG might be viewed as signalling a particular approach to risk management rather than an ideologically-driven willingness to sacrifice profitability.”
The paper examines the EU AI Act's impact on banking supervision, highlighting the ECB's role. It discusses legal frameworks, obligations for high-risk AI systems, AI governance, and the balance between innovation and prudential requirements. Strategic policy recommendations are provided to enhance oversight and financial system integrity.
“... insurers’ asset allocation and product pricing decisions are more connected than previously thought.”
This study analyzes the financial impact of Corporate Social Irresponsibility (CSI) events on European banks using a dataset of 11,832 reputational shocks from 2007-2023. Results show significant negative stock returns and increased volatility following CSI media coverage, with proactive ESG engagement mitigating these effects.
The theory of regulatory compliance has enabled the development of differential monitoring, emphasizing tailored, impactful regulations over uniform approaches, proving crucial in risk assessment and key indicators.
This paper defines vector-valued risk measures using axioms and shows they ignore dependence structures of input random vectors, unlike set-valued risk measures. Convex vector-valued risk measures are unsuitable for capital allocation in various financial applications, including systemic risk measures. The results also generalize to conditional settings.