Nos derniers articles

" In this paper, we use stochastic algorithms schemes in estimating MSRM [market data based systemic risk measure] and prove that the resulting estimators are consistent and asymptotically normal. We also test numerically the performance of these algorithms on several examples."

"Our paper contributes to the theory of conditional risk measures and conditional certainty equivalents. We adopt a random modular approach which proved to be effective in the study of modular convex analysis and conditional risk measures."

"This paper extends the traditional multi-state models to include epidemic effects."

"... we propose a reverse stress testing framework for dynamic models. Specifically, we consider a compound Poisson process over a finite time horizon and stresses composed of expected values of functions applied to the process at the terminal time. We then define the stressed model as the probability measure under which the process satisfies the constraints and which minimizes the KullbackLeibler divergence to the reference compound Poisson model."

"When developing large-sample statistical inference for quantiles, also known as Values-at-Risk in finance and insurance, the usual approach is to convert the task into sums of random variables. The conversion procedure requires that the underlying cumulative distribution function (cdf) would have a probability density function (pdf), plus some minor additional assumptions on the pdf. In view of this, and in conjunction with the classical continuous-mapping theorem, researchers also tend to impose the same pdf-based assumptions when investigating (functionals of) integrals of the quantiles, which are natural ingredients of many risk measures in finance and insurance. Interestingly, the pdf-based assumptions are not needed when working with integrals of quantiles, and in this paper we explain and illustrate this remarkable phenomenon."

" The aim is to come up with a convex risk functional that incorporates a sefety margin with respect to nonparametric uncertainty and still can be approximated through parametrized models. The particular form of the parametrization allows us to develop a numerical method, based on neural networks, which gives both the value of the risk functional and the optimal perturbation of the reference measure."

"Insurance fraud has been a long-lasting issue in actuarial modeling. Policyholders are prone to hide their true status in their best interest when disclosing their information for insurance pricing purposes. However, from the insurers' point of view, it is either time-consuming or laborious to verify the true status of such risk factors. There is thus a strong incentive to build models accounting for potential misrepresentation, which contributes to a more robust ratemaking system."

"We demonstrate the use of this risk measure for describing the tail risks in financial markets as well as the risks associated with natural hazards (earthquakes, tsunamis, and excessive rainfall)."

"The possibilities of a Keynesian-Knightian synthesis as a way forward are considered by comparing these signposts. It is argued that, although there is some common ground between Knight and Keynes, there are fundamental differences particularly associated with Keynes’s concept of weight of argument."

"... we find the difference in decision-makers’ probability assessments between operational and non-operational risk factors is greater when assessing a proximate rather than a remote target. We contribute to the accounting literature by demonstrating how spatial distance affects probability judgments."