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The latest country-specific regulatory guidance for assessing effectiveness of risk minimization measures (RMM) strategies was identified across five continents-Africa (Egypt, South Africa), Asia (Australia, China, Japan, South Korea, Singapore), Europe (EU-27, United Kingdom), North America (Unites States, Canada) and South America (Brazil)-and compared to the Reporting recommendation Intended for pharmaceutical Risk Minimization Evaluation Studies (RIMES) checklist, developed to assess the quality of effectiveness evaluations and endorsed by the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). RIMES checklist items address study hypothesis, participants, measures, statistical analysis and results. European Medical Agency (EMA) and Food and Drug Administration (FDA) guidance only partially aligned with RIMES, primarily for measures and results. In the absence of country-specific guidance, most countries recommended following EMA or FDA guidelines; Japan and South Africa mentioned the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH E2E) guideline; Brazil and China had no guidance/recommendations. Worldwide, there was a lack of RMM-specific guidance and, when guidance existed, they were not harmonized, and alignment with the RIMES checklist was limited.

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This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.

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We contribute to the literature on statistical robustness of risk measures by computing the index of qualitative robustness for risk measures based on utility functions. This problem is intimately related to finding the natural domain of finiteness and continuity of such risk measures.

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As the cyber insurance market is expanding and cyber insurance policies continue to mature, the potential of including pre-incident and post-incident services into cyber policies is being recognised by insurers and insurance buyers. This work addresses the question of how such services should be priced from the insurer's viewpoint, i.e. under which conditions it is rational for a profit-maximising, risk-neutral or risk-averse insurer to share the costs of providing risk mitigation services. The interaction between insurance buyer and seller is modelled as a Stackelberg game, where both parties use distortion risk measures to model their individual risk aversion. After linking the notions of pre-incident and post-incident services to the concepts of self-protection and self-insurance, we show that when pricing a single contract, the insurer would always shift the full cost of self-protection services to the insured; however, this does not generally hold for the pricing of self-insurance services or when taking a portfolio viewpoint. We illustrate the latter statement using toy examples of risks with dependence mechanisms representative in the cyber context. Supplementary Information: The online version contains supplementary material available at 10.1057/s41288-023-00289-7.

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When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson's Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion consistent with the grouping conclusion by removing the confounder's influence to eliminate the paradox. The ECIT uses relative risk difference Pd = max(0, (R - 1)/R) (R denotes the risk ratio) as the probability of causation. In contrast, Philosopher Fitelson uses confirmation measure D (posterior probability minus prior probability) to measure the strength of causation. Fitelson concludes that from the perspective of Bayesian confirmation, we should directly accept the overall conclusion without considering the paradox. The author proposed a Bayesian confirmation measure b* similar to Pd before. To overcome the contradiction between the ECIT and Bayesian confirmation, the author uses the semantic information method with the minimum cross-entropy criterion to deduce causal confirmation measure Cc = (R - 1)/max(R, 1). Cc is like Pd but has normalizing property (between -1 and 1) and cause symmetry. It especially fits cases where a cause restrains an outcome, such as the COVID-19 vaccine controlling the infection. Some examples (about kidney stone treatments and COVID-19) reveal that Pd and Cc are more reasonable than D; Cc is more useful than Pd.

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We estimate the distribution of COVID-19 mortality (measured as daily deaths) from the start of the pandemic until July 31st, 2022, for six European countries and the USA. We use the Pareto, the stretched exponential, the log-normal and the log-logistic distributions as well as mixtures of the log-normal and log-logistic distributions. The main results are that the Pareto does not describe well the data and that mixture distributions tend to offer a very good fit to the data. We also compute Value-at-Risk measures as well as mortality probabilities with our estimates. We also discuss the implications of our results and findings from the point of view of public health planning and modelling.

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We introduce here a new distribution called the power-modified Kies-exponential (PMKE) distribution and derive some of its mathematical properties. Its hazard function can be bathtub-shaped, increasing, or decreasing. Its parameters are estimated by seven classical methods. Further, Bayesian estimation, under square error, general entropy, and Linex loss functions are adopted to estimate the parameters. Simulation results are provided to investigate the behavior of these estimators. The estimation methods are sorted, based on partial and overall ranks, to determine the best estimation approach for the model parameters. The proposed distribution can be used to model a real-life turbocharger dataset, as compared with 24 extensions of the exponential distribution.

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This paper addresses asymmetric flexible two-parameter exponential model called the weighted exponential (WDEx) distribution. Some of its basic mathematical features are evaluated. Its hazard rate accommodates upside-down bathtub, decreasing, decreasing-constant, increasing, and increasing-constant shapes. Five actuarial indicators are studied. We utilize nine classical and Bayesian approaches of estimation for estimating the WDEx parameters. We provide a detailed simulation study to explore and assess the asymptotic behaviors of these estimators. Two approximation methods called the Markov chain Mont Carlo and Tierney and Kadane are applied to obtain the Bayesian estimates. The efficiency and applicability of the WDEx distribution are explored by modeling a lifetime data set from insurance field, showing that the WDEx distribution provides a superior fit over its competing exponential models such as the beta-exponential, Harris extend-exponential, Marshall-Olkin exponential, Marshall-Olkin alpha-power exponential, gamma Weibull, and exponentiated-Weibull distributions.

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In this paper we consider a portfolio selection problem defined for irregularly spaced observations. We use the Independent Component Analysis for the identification of the dependence structure and continuous-time GARCH models for the marginals. We discuss both estimation and simulation of market prices in a context where the time grid of price quotations differs across assets. We present an empirical analysis of the proposed approach using two high-frequency datasets that provides better out-of-sample results than competing portfolio strategies except for the case of severe market conditions with frequent rebalancements.

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Inspired by the consideration of some inside and future market information in financial market, a class of anticipated backward doubly stochastic Volterra integral equations (ABDSVIEs) are introduced to induce dynamic risk measures for risk quantification. The theory, including the existence, uniqueness and a comparison theorem for ABDSVIEs, is provided. Finally, dynamic convex risk measures by ABDSVIEs are discussed.

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In this article, the "truncated-composed" scheme was applied to the Burr X distribution to motivate a new family of univariate continuous-type distributions, called the truncated Burr X generated family. It is mathematically simple and provides more modeling freedom for any parental distribution. Additional functionality is conferred on the probability density and hazard rate functions, improving their peak, asymmetry, tail, and flatness levels. These characteristics are represented analytically and graphically with three special distributions of the family derived from the exponential, Rayleigh, and Lindley distributions. Subsequently, we conducted asymptotic, first-order stochastic dominance, series expansion, Tsallis entropy, and moment studies. Useful risk measures were also investigated. The remainder of the study was devoted to the statistical use of the associated models. In particular, we developed an adapted maximum likelihood methodology aiming to efficiently estimate the model parameters. The special distribution extending the exponential distribution was applied as a statistical model to fit two sets of actuarial and financial data. It performed better than a wide variety of selected competing non-nested models. Numerical applications for risk measures are also given.

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In this paper, we study the dynamic risk measures for processes induced by backward stochastic differential equations driven by Teugel's martingales associated with Lévy processes (BSDELs). The representation theorem for generators of BSDELs is provided. Furthermore, the time consistency of the coherent and convex dynamic risk measures for processes is characterized by means of the generators of BSDELs. Moreover, the coherency and convexity of dynamic risk measures for processes are characterized by the generators of BSDELs. Finally, we provide two numerical examples to illustrate the proposed dynamic risk measures.

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The practice of evidence-based medicine includes the critical analysis of clinical research studies, and, within it, the interpretation of the results reported. In addition, to statistical data, there are estimators that can help clinicians transfer research findings to routine clinical practice. These estimators are measures of risk, association, and impact. Risk measures report current uncertainty or probability (prevalence of a disease, sensitivity, specificity) or for future events (cumulative incidence, incidence density). Measures of association are related to the identification of the risk in order to determine whether certain factors increase or decrease the probability of development of a disease (relative risk, odds ratio, hazard ratio). While measures of impact allow, among other things, to estimate the effect of a treatment (relative risk reduction, absolute risk reduction, number needed to treat). In this review, each of these estimators is described, defined, and presented with examples.

Parte del ejercicio de la medicina basada en evidencia incluye el análisis crítico de los estudios de investigación clínica y dentro de este, la interpretación de los resultados presentados. Además de los datos estadísticos, existen estimadores que pueden ayudar a los clínicos a trasladar los hallazgos de las investigaciones a la práctica clínica habitual. Estos estimadores son las medidas de riesgo, asociación e impacto. Las medidas de riesgo informan sobre la incertidumbre o probabilidad en el presente (prevalencia de una enfermedad, sensibilidad, especificidad) o para eventos futuros (incidencia acumulada, densidad de incidencia). Las medidas de asociación se relacionan con la identificación del riesgo para determinar si ciertos factores aumentan o disminuyen la probabilidad del desarrollo de una enfermedad (riesgo relativo, razón de momios, cociente de riesgo). Mientras que las medidas de impacto permiten, entre otros, estimar el efecto de un tratamiento (reducción del riesgo relativo, reducción del riesgo absoluto, número necesario por tratar). En esta revisión se describen, definen y presentan ejemplos de cada uno de estos estimadores.

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In risk analysis, sensitivity measures quantify the extent to which the probability distribution of a model output is affected by changes (stresses) in individual random input factors. For input factors that are statistically dependent, we argue that a stress on one input should also precipitate stresses in other input factors. We introduce a novel sensitivity measure, termed cascade sensitivity, defined as a derivative of a risk measure applied on the output, in the direction of an input factor. The derivative is taken after suitably transforming the random vector of inputs, thus explicitly capturing the direct impact of the stressed input factor, as well as indirect effects via other inputs. Furthermore, alternative representations of the cascade sensitivity measure are derived, allowing us to address practical issues, such as incomplete specification of the model and high computational costs. The applicability of the methodology is illustrated through the analysis of a commercially used insurance risk model.

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The Covid-19 pandemic has brought about a heavy impact on the world economy, which arouses growing concerns about potential systemic risk, taking place in countries and regions. At this critical moment, it makes sense to interpret the systemic risk from the perspective of the financial crisis framework. By combing the latest research on systemic risks, we may arrive at some precautions relating to the current events. This literature review verifies the origin of systemic risk research. By comparing the retrieved and screened systemic literature with the relevant research on the financial crisis, more focus on the micro-foundations of systemic risk has been discovered. Besides, the measurement methods of systemic risks and the introduction of interdisciplinary methods have made the research in this field particularly active. This paper synthesizes the previous research conclusions to find the appropriate definition of systemic risk and combs the research literature of systemic risk from two lines: Firstly, conducting the division according to the sub-branch fields within the financial discipline and the relevant interdisciplinary research methods, which is helpful for scholars within and outside the discipline to have a more systematic understanding of the research in this field. Secondly predicting the research direction that can be expanded in this field.

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In this paper, we propose an adaptive entropy model (AEM), which incorporates the entropy measurement and the adaptability into the conventional Markowitz's mean-variance model (MVM). We evaluate the performance of AEM, based on several portfolio performance indicators using the five-year Shanghai Stock Exchange 50 (SSE50) index constituent stocks data set. Our outcomes show, compared with the traditional portfolio selection model, that AEM tends to make our investments more decentralized and hence helps to neutralize unsystematic risks. Due to the existence of self-adaptation, AEM turns out to be more adaptable to market fluctuations and helps to maintain the balance between the decentralized and concentrated investments in order to meet investors' expectations. Our model applies equally well to portfolio optimizations for other financial markets.

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BACKGROUND/AIMS: The Clinical Trials Coordination and Facilitation Group has issued recommendations on contraception and pregnancy testing to help sponsors meet regulatory expectations and harmonize practices to limit embryofetal risks in clinical trials. Our objective was to assess the compliance of French academic clinical trials with these recommendations and to describe the mitigation measures required by sponsors in their trials. METHODS: A cross-sectional study was performed on the French academic drug trials authorized by the national competent authority between January 2015 and June 2018. We included trials which tested systemic administration of drugs and enrolled men or women of childbearing potential. RESULTS: Data from 97 trials included were compiled. One-third of the trials (23.8%-43.3%, 95% confidence interval) complied with the Clinical Trial Facilitation and Coordination Group recommendations. No improvement over time or according to embryofetotoxic status or drug duration exposure was found. Contraception was required in 56.7% of trials and was more often required in case of potentially embryofetotoxic drugs (68.5% vs 41.9%, p = 0.013) or exposure over 1 month (71.7% vs 43.8%, p = 0.006). Pregnancy testing at inclusion was required in 59.1% of trials and additional testing in 17.2%. Pregnancy testing at inclusion was more often required in trials with drug exposure above 1 month (67.4% vs 45.8%, p = 0.035). CONCLUSION: French academic sponsors barely met the recommendations on contraception and pregnancy testing potentially leading to potential embryofetal risks in case of pregnancy. They need to implement these recommendations quickly.

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The use of Static tools (Static-99, Static-99R, Static-2002, and Static-2002R) in risk decision making involving sexual offenders is widespread internationally. This study compared the predictive accuracy and incremental validity of four Static risk measures in a sample of 621 Australian sexual offenders. Results indicated that approximately 45% of the sample recidivated (with 18.8% committing sexual offenses). All of the Static measures investigated yielded moderate predictive validity for sexual recidivism, which was comparable with other Australian and overseas studies. Area under the curve (AUC) values for the four measures across the 5-, 10-, and 15-year intervals ranged from .67 to .69. All of the Static measures discriminated quite well between low-risk and high-risk sexual offenders but less well for the moderate risk categories. When pitted together, none of the tools accounted for additional variance in sexual recidivism, above and beyond what the other measures accounted for. The overall results provide support for the use of Static measures as a component of risk assessment and decision making with Australian sexual offending populations. The limitations of this study and recommendations for further research are also discussed.

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Time unit invariance is introduced as an additional requirement for multiperiod risk measures: for a constant portfolio under an i.i.d. risk factor process, the multiperiod risk should equal the one period risk of the aggregated loss, for an appropriate choice of parameters and independent of the portfolio and its distribution. Multiperiod Maximum Loss over a sequence of Kullback-Leibler balls is time unit invariant. This is also the case for the entropic risk measure. On the other hand, multiperiod Value at Risk and multiperiod Expected Shortfall are not time unit invariant.

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In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly linking sensitivity and uncertainty analyses. We focus on the case of distortion risk measures, defined as weighted averages of output percentiles, and prove a representation of the sensitivity measure that can be evaluated on a Monte Carlo sample, as a weighted average of gradients over the input space. When the analytical model is unknown or hard to work with, nonparametric techniques are used for gradient estimation. This process is demonstrated through the example of a nonlinear insurance loss model. Furthermore, the proposed framework is extended in order to measure sensitivity to constant model parameters, uncertain statistical parameters, and random factors driving dependence between model inputs.