"... 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."
"We believe our paper adds to the important body of cybersecurity literature that explores the roles of government and business, particularly corporate directors, in the governance of data security."
"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 unacceptable risks are those that are deemed to contravene Union values, and they are therefore considered as “prohibited AI practices” by Article 5 AIA. The proposed prohibition covers four categories: 1) AI systems deploying subliminal techniques, 2) AI practices exploiting vulnerabilities, 3) social scoring systems, and 4) “real-time” remote biometric identification systems. "
"... supply chain network features add significant detection power to predicting enterprise cyber risk, relative to merely using enterprise-only attributes. Particularly, compared to a base model that relies only on internal enterprise features... Given that each cyber data breach is a low probability high impact risk event, these improvements in the prediction power have significant value."
"We propose a new conceptual framework grouping government interventions into three dimensions: regulation of risky activity, public investment in risk reduction, and co-insurance."
"As businesses improved their resilience, cybercriminals adapted and ransoms escalated, calling insurability into question. Yet there remains little appetite for imposing restrictive conditionality in this highly competitive market. Instead, insurers have turned to governments to contain criminal threats and cushion catastrophic losses."
"We find that sustainable funds are less exposed to transition risk and perform better than the overall fund sector in the low-carbon transition, validating their choice as green investments."
" 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."
"We present a framework for constructing multivariate risk measures that is inspired from univariate Optimized Certainty Equivalent (OCE) risk measures. We show that this new class of risk measures verifies the desirable properties such as convexity, monotonocity and cash invariance. We also address numerical aspects of their computations using stochastic algorithms instead of using Monte Carlo or Fourier methods that do not provide any error of the estimation."