Insurance Contracting with Adverse Selection and Moral Hazard

Date : Tags : , , , , , ,
"Our model yields richer separating Nash equilibria than pure moral hazard and pure adverse selection models, although separating Nash equilibria may not exist in some cases. It also retains some properties, for example, no full insurance and the positive correlation between insurance coverage and risk type, in those benchmark models. Our study on comparative statics indicates that, under some conditions and with some exceptions, the optimal indemnity and premium decrease with disutility from effort, increase with potential loss, and decrease with the initial wealth of the insured."

Risk Aggregation, Tail Risk, Correlation: Capital Allocation Efficiency and Regulator...

"... model uncertainty is a vital component of the current challenges in risk measurement, and therefore the regulator should design risk measures encouraging well-understood prudent decisions over (less understood) risky ones. From this perspective robust regulation should be a desirable goal. To achieve such an objective, simple – but not simpler – rules are needed."

Quantifying Uncertainty and Sensitivity in Climate Risk Assessments

"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 uncertainties by varying a number of input factors based on a discrete, scientifically justified set of options."

A Dirichlet Process Mixture Regression Model for the Analysis of Competing Risk Events

"We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex. Such functionals are called distortion riskmetrics, which include many statistical measures of risk and variability used in portfolio optimization and insurance."

Risk sharing, measuring variability, and distortion riskmetrics

Date : Tags :
"We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex. Such functionals are called distortion riskmetrics, which include many statistical measures of risk and variability used in portfolio optimization and insurance."

Machine Learning methods in climate finance: a systematic review

Date : Tags : , , , ,
"Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts."

Is Accumulation Risk In Cyber Systematically Underestimated?

"The purpose of this article is to highlight the importance of taking a holistic approach to cyber. In particular, we argue that actuarial modelling should not be viewed stand-alone, but rather as an integral part of an interconnected value chain with other processes such as cyber-risk assessment and cyber-claims settlement."