48,072 research outputs found

    Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System

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    Due to the inherent aleatory uncertainties in renewable generators, the reliability/adequacy assessments of distributed generation (DG) systems have been particularly focused on the probabilistic modeling of random behaviors, given sufficient informative data. However, another type of uncertainty (epistemic uncertainty) must be accounted for in the modeling, due to incomplete knowledge of the phenomena and imprecise evaluation of the related characteristic parameters. In circumstances of few informative data, this type of uncertainty calls for alternative methods of representation, propagation, analysis and interpretation. In this study, we make a first attempt to identify, model, and jointly propagate aleatory and epistemic uncertainties in the context of DG systems modeling for adequacy assessment. Probability and possibility distributions are used to model the aleatory and epistemic uncertainties, respectively. Evidence theory is used to incorporate the two uncertainties under a single framework. Based on the plausibility and belief functions of evidence theory, the hybrid propagation approach is introduced. A demonstration is given on a DG system adapted from the IEEE 34 nodes distribution test feeder. Compared to the pure probabilistic approach, it is shown that the hybrid propagation is capable of explicitly expressing the imprecision in the knowledge on the DG parameters into the final adequacy values assessed. It also effectively captures the growth of uncertainties with higher DG penetration levels

    The Determinants of Credit Ratings in the United Kingdom Insurance Industry

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    Executive Summary The Determinants of Credit Ratings in the United Kingdom Insurance Industry Academic researchers have devoted a considerable amount of attention to the activities of credit rating agencies over the past 20 years, focusing in particular on the agencies’ potential role in overseeing corporate financial strength and promoting the efficient operation of financial markets. Examinations of credit rating practices has recently extended to the insurance industry, where the complex technical nature of market transactions leads to policyholders, investors and others facing particularly acute information asymmetries at the point-of-sale. Published credit ratings are therefore seen as helping to alleviate imperfections in insurance markets by providing a third party opinion on the adequacy of an insurer’s financial health and the likelihood of it meeting obligations to policyholders and others in the future. Although the United Kingdom (UK) insurance market is now one of the five largest in the world, relatively little is known about the practices of the major firms and policy-makers which influence its operations. In particular, whilst the determinants of rating agencies’ assessments of United States (US) insurers is well documented, published studies have yet to provide comprehensive evidence about insurance company ratings in the UK. This study attempts to fill this gap by examining the ratings awarded by two of the world’s leading agencies – A.M. Best and Standard and Poor (S&P) – and establishing the extent to which organizational variables can help predict: (i) insurance firms’ decision to be rated; and (ii) the assigned ratings themselves. Our sample of UK data comprises ratings made by A.M. Best and S&P over the period 1993-1997 for both life and property-liability insurers. The panel data we use is ordinal in nature and is therefore analysed using an ordered probit model. However, because neither A.M. Best or S&P rate the full population of UK insurance firms our data set is potentially subject to selfselection bias and we therefore extend the model to correct for such problems. In particular, the paper examines the effect of eight firm-specific variables (namely, capital adequacy, profitability, liquidity, growth, size, mutual/stockowner status, reinsurance level, and short/long-term nature of business) on the ratings awarded by the two agencies, as well as on insurance firms’ decisions to volunteer for the ratings in the first place. In general terms, our evidence concurs with earlier US findings, and suggests that although the decision to be rated by either of the agencies is largely influenced by a common set of factors, the determinants of the ratings themselves appear to differ. Specifically, our first main finding is that insurers’ decisions to be rated by either A.M. Best or S&P is positively related to surplus growth, profitability and leverage. Second, while we find that A.M. Best’s ratings are positively linked to profitability and liquidity, as well as being generally higher for mutual insurers, the findings for S&P differ substantially. Although liquidity again exerted a positive influence on assigned ratings, the only other statistically significant variable was financial leverage, which had a negative sign. We believe that the results of our research are of potential importance for companies operating in insurance markets as well as for policy-makers, brokers and others. For example, the evidence that mutual insurers are generally assigned higher ratings than stock insurers suggests that certain publicly-traded insurers, in particular new entrants, might not possess sound financial strength and may require closer regulatory scrutiny than other, more established, insurance firms. In addition, the finding that liquidity has a significantly positive effect on ratings assigned by two of the world’s leading credit agencies should provide a measure of confidence about the robustness of the ratings to industry regulators, policyholders and investors in the UK. This could imply that external ratings might eventually play a role in substituting for costly industry regulation. The study concludes that although the factors influencing the decision to be rated by A.M. Best or S&P are broadly the same, a degree of variability exists in the variables which influence the actual ratings themselves. Insurance company managers should be aware of this when contemplating whether to seek an independent rating and which agency to choose for the assessment. We therefore believe that this study fills an important gap in the literature about key players in the important UK insurance market and provides a basis for the conduct of future research

    Validating Predictions of Unobserved Quantities

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    The ultimate purpose of most computational models is to make predictions, commonly in support of some decision-making process (e.g., for design or operation of some system). The quantities that need to be predicted (the quantities of interest or QoIs) are generally not experimentally observable before the prediction, since otherwise no prediction would be needed. Assessing the validity of such extrapolative predictions, which is critical to informed decision-making, is challenging. In classical approaches to validation, model outputs for observed quantities are compared to observations to determine if they are consistent. By itself, this consistency only ensures that the model can predict the observed quantities under the conditions of the observations. This limitation dramatically reduces the utility of the validation effort for decision making because it implies nothing about predictions of unobserved QoIs or for scenarios outside of the range of observations. However, there is no agreement in the scientific community today regarding best practices for validation of extrapolative predictions made using computational models. The purpose of this paper is to propose and explore a validation and predictive assessment process that supports extrapolative predictions for models with known sources of error. The process includes stochastic modeling, calibration, validation, and predictive assessment phases where representations of known sources of uncertainty and error are built, informed, and tested. The proposed methodology is applied to an illustrative extrapolation problem involving a misspecified nonlinear oscillator

    Efficient sampling methodologies for lake littoral invertebrates in compliance with the European Water Framework Directive

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    Lake shores are characterised by a high natural variability, which is increasingly threatened by a multitude of anthropogenic disturbances including morphological alterations to the littoral zone. The European Water Framework Directive (EU WFD) calls for the assessment of lake ecological status by monitoring biological quality elements including benthic macroinvertebrates. To identify cost- and time-efficient sampling strategies for routine lake monitoring, we sampled littoral invertebrates in 32 lakes located in different geographical regions in Europe. We compared the efficiency of two sampling methodologies, defined as habitat-specific and pooled composite sampling protocols. Benthic samples were collected from unmodified and morphologically altered shorelines. Variability within macroinvertebrate communities did not differ significantly between sampling protocols across alteration types, lake types and geographical regions. Community composition showed no significant differences between field composite samples and artificially generated composite samples, and correlation coefficients between macroinvertebrate metrics calculated with both methods and a predefined morphological stressor index were similar. We conclude that proportional composite sampling represents a time- and cost-efficient method for routine lake monitoring as requested under the EU WFD, and may be applied across various European geographical regions

    Historical review of “umbrella supervision” by the Board of Governors of the Federal Reserve System

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    The article reviews legislative history and supervisory practices related to bank holding companies with a view toward understanding what Congress meant by referring to the Board of Governors of the Federal Reserve System as the “umbrella supervisor” in the Gramm-Leach-Bliley Act. The first part of the article looks at the historical development of bank holding company law and regulation, which laid the foundation for the current practice of umbrella supervision. The second part of the article provides answers to questions related to the Board’s current role as umbrella supervisor: What does “umbrella supervision” mean, and is it different from “consolidated supervision”? How does the GLB Act limit the Board's authority and practice and when did the Board obtain all of the legal authority to allow it to practice umbrella supervision?Bank holding companies ; Bank supervision ; Gramm-Leach-Bliley Act ; Banking law

    Psychiatric morbidity in older people with moderate and severe learning disability (mental retardation). Part I: development and reliability of the patient interview (the PAS-ADD)

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    This paper describes the development of the PAS-ADD, a semistructured clinical interview for use specifically with patients with learning disabilities, based on items drawn from the PSE. The PAS-ADD includes a number of novel features including: parallel interviewing of patient and informant; a three-tier structure to provide a flexible interview appropriate to the patient's intellectual level; use of a memorable 'anchor event' in the patient's life to improve time focus; and simplified wording, improved organisation and lay out. Inter-rater reliability was investigated using an experimental design in which two raters viewed and re-rated videotaped PAS-ADD interviews which had been conducted by an experienced clinician. Reliability results compared favourably with those obtained in a major study of PSE reliability with a sample drawn from non-learning disabled individuals. Mean kappa for all items was 0.72. Other indexes of reliability were also good. In the current phase of development, the PAS-ADD is to be expanded to include further diagnostic categories, including schizophrenia and autism. The new version will be updated for use with ICD-10 criteria
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