Ruin probabilities with dependence on the number of claims within a fixed time window

We analyse the ruin probabilities for a renewal insurance risk process with inter-arrival time distributions depending on the claims that arrived within a fixed (past) time window. This dependence could be explained through a regenerative structure. The main inspiration of the model comes from the Bonus-Malus feature. We discuss first asymptotic results of ruin probabilities for different regimes of claim distributions. For numerical results, we recognise an embedded Markov additive process. Via an appropriate change of measure, ruin probabilities could be computed to a closed form formulae. Additionally, we present simulated results via the importance sampling method, which further permit an in-depth analysis of a few concrete cases

Bonus-malus in insurance portfolios

This thesis constitutes a research work on Bonus-Malus (BM) systems in insurance portfolios, featuring designing pricing strategies and examining associated solvency risks. The first piece of work proposed two different pricing models via the Bayesian approach. Results imply adverse attitudes towards policyholders having a history of many small claims, when the modelling for claim severities takes different forms. On the other hand, the rest of the work dedicated to embedding a BM structure under a risk analysis framework, where the focus lies in measuring the underlying ruin probabilities. It was necessary to initially investigate a discrete model where such probability could be obtained through recursions. As for a continuous model, BM feature was reflected by a Bayesian estimator for premium adjustment. Such construction normally brings in a dependence structure to the risk model thus violating classical assumptions. One way was to inspect how different it is from a classical risk model. Then through some conditional arguments one could find accordingly a solution based on results in literature. From another perspective, it has been found that for a No Claim Discount (NCD) or a Bonus system, an alteration in premium rates could be transformed equivalently to an interchange of distribution between inter-claim times. Then some Markov properties were able to be diagnosed under higher dimensions, which leads to a further possibility of computations. Results can be found in the form of simulations

Pricing foreseeable and unforeseeable risks in insurance portfolios

In this manuscript we propose a method for pricing insurance products that cover not only traditional risks, but also unforeseen ones. By considering the Poisson process parameter to be a mixed random variable, we capture the heterogeneity of foreseeable and unforeseeable risks. To illustrate, we estimate the weights for the two risk streams for a real dataset from a Portuguese insurer. To calculate the premium, we set the frequency and severity as distributions that belong to the linear exponential family. Under a Bayesian setup , we show that when working with a finite mixture of conjugate priors, the premium can be estimated by a mixture of posterior means, with updated parameters, depending on claim histories. We emphasise the riskiness of the unforeseeable trend, by choosing heavy-tailed distributions. After estimating distribution parameters involved using the Expectation-Maximization algorithm, we found that Bayesian premiums derived are more reactive to claim trends than traditional ones

Risk models with premiums adjusted to claims number

Classical compound Poisson risk models consider the premium rate to be constant. By adjusting the premium rate to the claims history, one can emulate a Bonus–Malus system within the ruin theory context. One way to implement such adjustment is by considering the Poisson parameter to be a continuous random variable and use credibility theory arguments to adjust the premium rate a posteriori. Depending on the defectiveness of this random variable, respectively referred to as ‘unforeseeable’ (defective) versus ‘historical’ (non-defective) risks, one obtains different relations between the ruin probability with constant versus adjusted premium rate. A combination of these two kinds of risks also leads to a relation between the two ruin probabilities, when the a posteriori estimator of the number of claims is carefully chosen. Examples for specific claim sizes are presented throughout the paper

Estimation of foreseeable and unforeseeable risks in motor insurance

This project works with the risk model developed by [6] and quests modelling, estimating and pricing insurance for risks brought in by innovative technologies, or other emerging or latent risks. The model considers two different risk streams that arise together, however not clearly separated or observed. Specifically, we consider a risk surplus process where premia are adjusted according to past claim frequencies, like in a Bonus-Malus (BM) system, when we consider a classical or historical risk stream and an unforeseeable risk one. These are unknown risks which can be of high uncertainty that, when pricing insurance (ratemaking and experience rating), suggest a sensitive premium adjustment strategy. It is not clear for the actuary to observe which claim comes from one or the other stream. When modelling such risks it is crucial to estimate the behaviour of such claims, occurrence and their severity. Premium calculation must fairly reflect the nature of these two kinds of risk streams. We start proposing a model, separating claim counts and severities, then propose a premium calculation method, and finally a parameter estimation procedure. In the modelling we assume a Bayesian approach as used in credibility theory, a credibility approach for premium calculation and the use of the Expectation-Maximization (EM) algorithm in the estimation procedureinfo:eu-repo/semantics/publishedVersio

Estimation of foreseeable and unforeseeable risks in motor insurance

This project works with the risk model developed by Li et al. (2015) and quests modelling, estimating and pricing insurance for risks brought in by innovative technologies, or other emerging or latent risks. The model considers two different risk streams that arise together, however not clearly separated or observed. Specifically, we consider a risk surplus process where premia are adjusted according to past claim frequencies, like in a Bonus-Malus (BM) system, when we consider a classical or historical risk stream and an unforeseeable risk one. These are unknown risks which can be of high uncertainty that, when pricing insurance (ratemaking and experience rating), suggest a sensitive premium adjustment strategy. It is not clear for the actuary to observe which claim comes from one or the other stream. When modelling such risks it is crucial to estimate the behaviour of such claims, occurrence and their severity. Premium calculation must fairly reflect the nature of these two kinds of risk streams. We start proposing a model, separating claim counts and severities, then propose a premium calculation method, and finally a parameter estimation procedure. In the modelling we assume a Bayesian approach as used in credibility theory, a credibility approach for premium calculation and the use of the Expectation-Maximization (EM) algorithm in the estimation procedure

Estimation of foreseeable and unforeseeable risks in motor insurance

This project works with the risk model developed by Li et al. (2015) and quests modelling, estimating and pricing insurance for risks brought in by innovative technologies, or other emerging or latent risks. The model considers two different risk streams that arise together, however not clearly separated or observed. Specifically, we consider a risk surplus process where premia are adjusted according to past claim frequencies, like in a Bonus-Malus (BM) system, when we consider a classical or historical risk stream and an unforeseeable risk one. These are unknown risks which can be of high uncertainty that, when pricing insurance (ratemaking and experience rating), suggest a sensitive premium adjustment strategy. It is not clear for the actuary to observe which claim comes from one or the other stream. When modelling such risks it is crucial to estimate the behaviour of such claims, occurrence and their severity. Premium calculation must fairly reflect the nature of these two kinds of risk streams. We start proposing a model, separating claim counts and severities, then propose a premium calculation method, and finally a parameter estimation procedure. In the modelling we assume a Bayesian approach as used in credibility theory, a credibility approach for premium calculation and the use of the Expectation-Maximization (EM) algorithm in the estimation procedure

Methamphetamine exposure drives cell cycle exit and aberrant differentiation in rat hippocampal-derived neurospheres

Introduction: Methamphetamine (METH) abuse by pregnant drug addicts causes toxic effects on fetal neurodevelopment; however, the mechanism underlying such effect of METH is poorly understood.Methods: In the present study, we applied three-dimensional (3D) neurospheres derived from the embryonic rat hippocampal tissue to investigate the effect of METH on neurodevelopment. Through the combination of whole genome transcriptional analyses, the involved cell signalings were identified and investigated.Results: We found that METH treatment for 24 h significantly and concentration-dependently reduced the size of neurospheres. Analyses of genome-wide transcriptomic profiles found that those down-regulated differentially expressed genes (DEGs) upon METH exposure were remarkably enriched in the cell cycle progression. By measuring the cell cycle and the expression of cell cycle-related checkpoint proteins, we found that METH exposure significantly elevated the percentage of G0/G1 phase and decreased the levels of the proteins involved in the G1/S transition, indicating G0/G1 cell cycle arrest. Furthermore, during the early neurodevelopment stage of neurospheres, METH caused aberrant cell differentiation both in the neurons and astrocytes, and attenuated migration ability of neurospheres accompanied by increased oxidative stress and apoptosis.Conclusion: Our findings reveal that METH induces an aberrant cell cycle arrest and neuronal differentiation, impairing the coordination of migration and differentiation of neurospheres

A Comprehensive Analysis of the CaMK2A Gene and Susceptibility to Alzheimer’s Disease in the Han Chinese Population

There is ample evidence suggesting that calcium/calmodulin-dependent protein kinase II alpha (CaMK2A) may play an important role in the pathophysiology of Alzheimer’s disease (AD). This genetic study aimed to investigate whether CaMK2A confers susceptibility to the development of AD in the Han Chinese population. A total of seven single nucleotide polymorphisms (SNPs) within CaMK2A were screened in two independent cohorts from southwestern China (333 AD patients and 334 controls) and eastern China (382 AD patients and 426 controls) to discern the potential association between this gene and AD. In addition, a cross-platform normalized expression resource was used to investigate whether CaMK2A is differentially expressed in the brain between individuals with AD and the controls. In addition, expression quantitative trait loci (eQTL) analysis was used to explore the differences in CaMK2A expression in the brain among different genotypes. The cross-platform normalized data showed significant differences in CaMK2A expression in the hippocampus, entorhinal cortex and temporal cortex between the AD patients and the control subjects (|log FC| > 0.1, P < 0.05); however, only the differences in the hippocampus and temporal cortex remained after the multiple comparisons correction [false discovery rate (FDR)-corrected, P < 0.05]. The frequency of the rs4958445 genotype was significantly different between the AD subjects and the controls from southwestern China (P = 0.013, P = 0.034 after FDR correction). When the two samples were combined, rs4958445 still showed a significant association with AD (P = 0.044). Haplotype analysis indicated that the T-A-C-A-T-C-C and T-G-C-A-T-C-C haplotypes in the southwestern cohort and the T-G-C-G-C-T-C haplotype in the eastern cohort, consisting of rs10051644, rs6869634, rs3797617, rs3756577, rs4958445, rs10515639 and rs6881743, showed a significant association with AD (P = 0.037, P = 0.026 and P = 0.045, respectively). Furthermore, the brain eQTL analysis revealed a significant association between the rs4958445 polymorphism and CaMK2A expression in the inferior olivary nucleus (P = 0.029). Our results suggest an important role for CaMK2A in the pathophysiology of AD in the Han Chinese population, especially the southwestern population