Solvency II requires that firms with Internal Models derive the Solvency Capital Requirement directly from the probability distribution forecast generated by the Internal Model. A number of UK insurance undertakings do this via an aggregation model consisting of proxy models and a copula. Since 2016 there have been a number of industry surveys on the application of these models, with the 2019 Prudential Regulation Authority (“PRA”) led industry wide thematic review identifying a number of areas of enhancement. This concluded that there was currently no uniform best practice. While there have been many competing priorities for insurers since 2019, the Working Party expects that firms will have either already made changes to their proxy modelling approach in light of the PRA survey, or will have plans to do so in the coming years. This paper takes the PRA feedback into account and explores potential approaches to calibration and validation, taking into consideration the different heavy models used within the industry and relative materiality of business lines.

For graphs $G$ and $H$, the Ramsey number $r(G,H)$ is the smallest positive integer $N$ such that any red/blue edge colouring of the complete graph $K_N$ contains either a red $G$ or a blue $H$. A book $B_n$ is a graph consisting of $n$ triangles all sharing a common edge.

Recently, Conlon, Fox, and Wigderson conjectured that for any $0\lt \alpha \lt 1$, the random lower bound $r(B_{\lceil \alpha n\rceil },B_n)\ge (\sqrt{\alpha }+1)^2n+o(n)$ is not tight. In other words, there exists some constant $\beta \gt (\sqrt{\alpha }+1)^2$ such that $r(B_{\lceil \alpha n\rceil },B_n)\ge \beta n$ for all sufficiently large $n$. This conjecture holds for every $\alpha \lt 1/6$ by a result of Nikiforov and Rousseau from 2005, which says that in this range $r(B_{\lceil \alpha n\rceil },B_n)=2n+3$ for all sufficiently large $n$.

We disprove the conjecture of Conlon, Fox, and Wigderson. Indeed, we show that the random lower bound is asymptotically tight for every $1/4\leq \alpha \leq 1$. Moreover, we show that for any $1/6\leq \alpha \le 1/4$ and large $n$, $r(B_{\lceil \alpha n\rceil }, B_n)\le \left (\frac 32+3\alpha \right ) n+o(n)$, where the inequality is asymptotically tight when $\alpha =1/6$ or $1/4$. We also give a lower bound of $r(B_{\lceil \alpha n\rceil }, B_n)$ for $1/6\le \alpha \lt \frac{52-16\sqrt{3}}{121}\approx 0.2007$, showing that the random lower bound is not tight, i.e., the conjecture of Conlon, Fox, and Wigderson holds in this interval.

A system experiences random shocks over time, with two critical levels, d1 and d2, where $d_{1} \lt d_{2}$. k consecutive shocks with magnitudes between d1 and d2 partially damaging the system, causing it to transition to a lower, partially working state. Shocks with magnitudes above d2 have a catastrophic effect, resulting in complete failure. This theoretical framework gives rise to a multi-state system characterized by an indeterminate quantity of states. When the time between successive shocks follows a phase-type distribution, a detailed analysis of the system’s dynamic reliability properties such as the lifetime of the system, the time it spends in perfect functioning, as well as the total time it spends in partially working states are discussed.

We explore one systematic review and meta-analysis of both observational and randomized studies examining COVID-19 vaccines in 5- to 11-year-olds, which reported substantial benefits associated with vaccinating this age group. We discuss the limitations of the individual studies that were used to estimate vaccination benefits. The review included five observational studies that evaluated vaccine effectiveness (VE) against COVID-19 severe disease or hospitalization. All five studies failed to adequately assess differences in underlying health between vaccination groups. In terms of vaccination harms, looking only at the randomized studies, a significantly higher odds of adverse events was identified among the vaccinated compared with the unvaccinated. Observational studies are at risk of overestimating the effectiveness of vaccines against severe disease if healthy vaccinee bias is present. Falsification endpoints can provide valuable information about underlying healthy vaccinee bias. Studies that have not adequately ruled out bias due to better health among the vaccinated or more vaccinated should be viewed as unreliable for estimating the VE of COVID-19 vaccination against severe disease and mortality. Existing systematic reviews that include observational studies of the COVID-19 vaccine in children may have overstated or falsely inferred vaccine benefits due to unidentified or undisclosed healthy vaccinee bias.

Nowadays public policymakers are offered with opportunities to take data-driven evidence-based decisions by analyzing the very large volumes of policy-related data that are generated through different channels (e.g., e-services, mobile apps, social media). Machine learning (ML) and artificial intelligence (AI) tehcnologies ease and automate the analysis of large policy-related datasets, which helps policymakers to realize a shift toward data-driven decisions. Nevertheless, the deployment and use of AI tools for public policy development is also associated with significant technical, political, and operation challenges. For instance, AI-based policy development solutions must be transparent and explainable to policymakers, while at the same time adhering to the mandates of emerging regulations such as the AI Act of the European Union. This paper introduces some of the main technical, operational, regulatory compliance challenges of AI-based policymaking. Accordingly, it introduces technological solutions for overcoming them, including: (i) a reference architecture for AI-based policy development, (ii) a virtualized cloud-based tool for the specification and implementation of ML-based data-driven policies, (iii) a ML framework that enables the development of transparent and explainable ML models for policymaking, and (iv) a set of guidelines for using the introduced technical solutions to achieve regulatory compliance. The paper ends up illustrating the validation and use of the introduced solutions in real-life public policymaking cases for various local governments.

We develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance prior best matching the spatio-temporal dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA) on synthetic case studies to validate the ability of the GA to recover APC structure and on real-life national-level datasets from the Human Mortality Database. Our machine learning-based analysis provides novel insight into the presence/absence of Cohort effects in different populations and into the relative smoothness of mortality surfaces along the Age and Year dimensions. Our modeling work is done with the PyTorch libraries in Python and provides an in-depth investigation of employing GA to aid in compositional kernel search for GP surrogates.

Cointegrating polynomial regressions (CPRs) include deterministic variables, integrated variables, and their powers as explanatory variables. Based on a novel kernel-weighted limit result and a novel functional central limit theorem, this paper shows that the fully modified ordinary least squares (FM-OLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57, 99–125) is robust to being used in CPRs. Being used in CPRs refers to a widespread empirical practice that treats the integrated variables and their powers, incorrectly, as a vector of integrated variables and uses textbook FM-OLS. Robustness means that this “formal” FM-OLS practice leads to a zero mean Gaussian mixture limiting distribution that coincides with the limiting distribution of the Wagner and Hong (2016, Econometric Theory 32, 1289–1315) application of the FM estimation principle to the CPR case. The only restriction for this result to hold is that all integrated variables to power one are included as regressors. Even though simulation results indicate performance advantages of the Wagner and Hong (2016, Econometric Theory 32, 1289–1315) estimator, partly even in large samples, the results of the paper give an asymptotic foundation to “formal” FM-OLS and thus enlarge the usability of the Phillips and Hansen (1990, Review of Economic Studies 57, 99–125) estimator implemented in many software packages.

We report an outbreak of confirmed Mycoplasma pneumoniae community-acquired pneumonia (CAP) in Nord Franche-Comté Hospital, France, from 14 November 2023 to 31 January 2024. All 13 inpatients (11 adults with a mean age of 45.5 years and 2 children) were diagnosed with positive serology and/or positive reverse transcription polymerase chain reaction (RT-PCR) on respiratory specimens. All patients were immunocompetent and required oxygen support with a mean duration of oxygen support of 6.2 days. Two patients were transferred to the intensive care unit (ICU) but were not mechanically ventilated. Patients were treated with macrolides (n = 12, 92.3%) with recovery in all cases. No significant epidemiological link was reported in these patients.

While driver telematics has gained attention for risk classification in auto insurance, scarcity of observations with telematics features has been problematic, which could be owing to either privacy concerns or favorable selection compared to the data points with traditional features.

To handle this issue, we apply a data integration technique based on calibration weights for usage-based insurance with multiple sources of data. It is shown that the proposed framework can efficiently integrate traditional data and telematics data and can also deal with possible favorable selection issues related to telematics data availability. Our findings are supported by a simulation study and empirical analysis in a synthetic telematics dataset.

We consider estimation of the spot volatility in a stochastic boundary model with one-sided microstructure noise for high-frequency limit order prices. Based on discrete, noisy observations of an Itô semimartingale with jumps and general stochastic volatility, we present a simple and explicit estimator using local order statistics. We establish consistency and stable central limit theorems as asymptotic properties. The asymptotic analysis builds upon an expansion of tail probabilities for the order statistics based on a generalized arcsine law. In order to use the involved distribution of local order statistics for a bias correction, an efficient numerical algorithm is developed. We demonstrate the finite-sample performance of the estimation in a Monte Carlo simulation.

We present novel cross-sectional and longitudinal claim count models for vehicle insurance built upon the combinedd actuarial neural network (CANN) framework proposed by Wüthrich and Merz. The CANN approach combines a classical actuarial model, such as a generalized linear model, with a neural network. This blending of models results in a two-component model comprising a classical regression model and a neural network part. The CANN model leverages the strengths of both components, providing a solid foundation and interpretability from the classical model while harnessing the flexibility and capacity to capture intricate relationships and interactions offered by the neural network. In our proposed models, we use well-known log-linear claim count regression models for the classical regression part and a multilayer perceptron (MLP) for the neural network part. The MLP part is used to process telematics car driving data given as a vector characterizing the driving behavior of each insured driver. In addition to the Poisson and negative binomial distributions for cross-sectional data, we propose a procedure for training our CANN model with a multivariate negative binomial specification. By doing so, we introduce a longitudinal model that accounts for the dependence between contracts from the same insured. Our results reveal that the CANN models exhibit superior performance compared to log-linear models that rely on manually engineered telematics features.

This paper introduces gemact, a Python package for actuarial modeling based on the collective risk model. The library supports applications to risk costing and risk transfer, loss aggregation, and loss reserving. We add new probability distributions to those available in scipy, including the (a, b, 0) and (a, b, 1) discrete distributions, copulas of the Archimedean family, the Gaussian, the Student t and the Fundamental copulas. We provide an implementation of the AEP algorithm for calculating the cumulative distribution function of the sum of dependent, nonnegative random variables, given their dependency structure specified with a copula. The theoretical framework is introduced at the beginning of each section to give the reader with a sufficient understanding of the underlying actuarial models.

We establish the exponential nonuniform Berry–Esseen bound for the maximum likelihood estimator of unknown drift parameter in an ultraspherical Jacobi process using the change of measure method and precise asymptotic analysis techniques. As applications, the optimal uniform Berry–Esseen bound and optimal Cramér-type moderate deviation for the corresponding maximum likelihood estimator are obtained.

We show that an estimate by de la Peña, Ibragimov, and Jordan for ${\mathbb{E}}(X-c)^+$, with c a constant and X a random variable of which the mean, the variance, and $\mathbb{P}(X \leqslant c)$ are known, implies an estimate by Scarf on the infimum of ${\mathbb{E}}(X \wedge c)$ over the set of positive random variables X with fixed mean and variance. This also shows, as a consequence, that the former estimate implies an estimate by Lo on European option prices.