48,072 research outputs found
Uncertainty Analysis of the Adequacy Assessment Model of a Distributed Generation System
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
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 agenciesâ 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 insurerâs 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 agenciesâ 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 worldâs leading agencies â A.M. Best and Standard and Poor (S&P) â and
establishing the extent to which organizational variables can help predict: (i) insurance firmsâ
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 firmsâ 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 insurersâ 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. Bestâs 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 worldâs 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
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Financial liberalization and capital adequacy in models of financial crises
We characterize the effects of financial liberalization indices on OECD banking crises, controlling for the standard macro prudential variables that prevail in the current literature. We use the Fraser Instituteâs Economic Freedom of the World database. This yields a variable that captures credit market regulations which broadly measures the restrictions under which banks operate. We then test for the direct impacts of some of its components, deposit interest rate regulations and private sector credit controls, on crisis probabilities and their indirect effects via capital adequacy. Over the period 1980 â 2012, we find that less regulated markets are associated with a lower crisis frequency, and it appears that the channel comes through strengthening the defence that capital provides. Deposit interest rate liberalisation adds to the strength of capital in protecting against crises. However, private sector credit liberalisation, appears to increase the probability of having a crisis, albeit not significantly. If policy makers are concerned about the costs of low risk events, they may wish to control private sector credit even if it has a probability of affecting significantly crises of between 10 and 20 per cent
Validating Predictions of Unobserved Quantities
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
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 âumbrella supervisionâ by the Board of Governors of the Federal Reserve System
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 âumbrella supervisorâ 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 Boardâs current role as umbrella supervisor: What does âumbrella supervisionâ mean, and is it different from âconsolidated supervisionâ? 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)
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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