bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cyber Risk Management
Authors: Wallner James 
Published: September 2008

Abstract

In most modern information systems (IS) and process control systems (PCS), functionality and security are competing design goals. Therefore, system designers and operators are constantly forced to make security‐related trade‐off decisions. To reach the right balance between functionality and security, systems security engineers and network operators have to build information and process control systems that are secure against real‐world cyber attacks without overengineering against any particular one. By understanding which cyber attacks are most likely and which risks are most serious, system designers and operators can make informed security‐related trade‐off decisions.
Cyber risk management
is the process of studying the threats, vulnerabilities, and mission impacts of cyber attacks that provides the appropriate metric for trading off between the competing design goals of security and functionality of information and process control systems.
Benchmark Dose Estimation
Authors: Al‐Saidy Obaid M. 
Published: September 2008

Abstract
The benchmark dose (BMD) methodology is a statistical procedure, first proposed by Crump in 1984, to determine allowable daily intakes of chemicals. The BMD was proposed as an alternative to the no‐observed‐adverse‐effect‐level (NOAEL) approach. The procedure is now widely used in risk assessment to establish health standards and guidelines for exposure of toxins to humans or to the environment. In this article, we review the BMD procedure, give details on how to compute the BMD and BMDL for both quantal and continuous endpoints and survey some of the recent developments in benchmark dose estimation.
Dose–Response Analysis
Authors: Edler Lutz 
Published: September 2008

Abstract

Dose‐response analysis is a substantial part of risk assessment aiming both at estimation of risk at a particular dose and at estimation of a minimum dose for a particular risk. The proof of the existence of a dose‐response relationship is a basic element of the proof of causality in experimental sciences. Dose‐response analysis requires the establishment of a dose‐response model and the availability of dose‐response data, both addressed in this chapter. Dose‐response data describe the extent of exposure of the individual subject by amounts, or concentrations of the agent. Dose‐response models are mathematical formula relating dose
D
to effect
Y
(
D
) and risk
R
(
D
), which itself is a function of the effect. The most common models are provided in explicit form. Finally, the procedure of a dose‐response analysis is described in generic terms and the methods of statistical inference are addressed.
Extremely Low Frequency Electric and Magnetic Fields
Authors: Kheifets Leeka , Mezei Gabor 
Published: September 2008

Abstract
Extremely low frequency (ELF) electric and magnetic fields (EMFs) are imperceptible and are ubiquitously present in modern societies. Numerous epidemiologic studies have investigated possible health risks from residential and occupational exposure to EMFs. ELF magnetic fields have been classified as a “possible human carcinogen”. This classification was based on epidemiologic evidence showing a consistent association between exposure to ELF magnetic fields and childhood leukemia and on laboratory studies in animals and cells, which do not support an association between exposure to ELF magnetic fields and cancer development. Data for other health outcomes is negative or inconsistent. Several risk characterization attempts have indicated that the public health impact of residential magnetic fields on childhood leukemia is likely to be limited and uncertain.
Environmental Health Triage, Chemical Risk Assessment For
Authors: Bogen Kenneth T. 
Published: September 2008

Abstract

Most chemical risk assessments incorporate large uncertainties that are usually addressed by “safety” factors intended to guarantee acceptable levels of risk. An unbiased, quantitative approach to chemical risk assessment may be preferable when circumstances require balancing or trading off different risks that involve toxic chemical exposures. If planners and responders cannot reliably compare chemical risks, or compare chemical
versus
nonchemical risks, they cannot prevent or minimize associated casualties in a risk‐based fashion. These ideas are illustrated by an example of unbiased risk assessment comparing exposure to methyl isocyanate
versus
hydrogen cyanide in a triage context.
Statistics for Environmental Justice
Authors: Waller Lance A. 
Published: September 2008

Abstract
We briefly review the historical, legal, and regulatory background of environmental justice assessments, paying particular attention to the role of statistical techniques in such assessments. We stress the difference between “disparate impact” and intentional discrimination and the role of statistical comparisons as defined in the associated regulatory statutes. We contrast two general approaches, the first comparing demographic proportions within exposure categories and the second comparing exposure (or exposure surrogate) distributions between demographic categories.
Environmental Performance Index
Authors: Srebotnjak Tanja 
Published: September 2008

Abstract

The Pilot 2006 Environmental Performance Index (EPI) is an aggregate measure of a country's policy results to achieve 16 performance targets in protecting human environmental health and maintaining ecological vitality. The objective underlying the EPI is to move the environmental debate from emotional and rhetoric arguments to more data‐ and evidence‐based action that facilitates performance tracking and accountability of decision makers. The EPI thereby helps to fill a significant gap in environmental policy making and supports the international community's ongoing effort to curb extreme poverty and promote human development through an ambitious measurement initiative called the
Millennium Development Goals
.
Environmental Tobacco Smoke
Authors: Hackshaw Allan 
Published: September 2008

Abstract
Breathing other people's tobacco smoke is associated with several thousand premature deaths each year among nonsmoking adults, including lung cancer, heart disease, chronic obstructive lung disease and stroke in adults, and several respiratory disorders in children. There is biological plausibility and direct estimates of the increased risk have been obtained from epidemiological studies of never‐smokers. Reducing exposure to environmental tobacco smoke by banning smoking in workplaces and enclosed public places is now part of public health policy in several countries. As a consequence, this should avoid many cases of early death and disability.
Statistics for Environmental Toxicity
Authors: Bailer A. John , Oris James T. 
Published: September 2008

Abstract
Environmental toxicity refers to the impact of a hazard or stressor at all levels of biological organization. This impact can range from molecular and cellular levels to whole organism, population, ecosystem, or landscape levels. The statistical methods employed to examine these impacts will reflect the spectrum of the level of organization and the measurements taken at this level. The methods associated with this spectrum are described and illustrated in this article.
Pesticide Risk
Authors: Peterson Robert K.D. 
Published: September 2008

Abstract
Pesticides are substances that are made to deliberately kill or deter species that humans have designated as pests. And, these organism‐killing substances are intentionally disseminated into the environment. Because of these rather unusual aspects, pesticide risk assessment is straightforward, yet, at the same time, complex. Pesticide risk assessment typically follows the well‐known steps in environmental risk assessment: problem formulation, hazard identification, dose–response assessment, exposure assessment, and risk characterization. Additionally, assessments often rely on a tiered modeling approach extending from deterministic models (Tier 1) based on conservative assumptions to probabilistic models (Tier 4) using refined assumptions. Risk characterization often involves determining the joint probability of effect and exposure. A rich database of effect and exposure data for pesticides allows for risk assessments for many different human and ecological receptors; probabilistic assessments are thus becoming more common. Pesticide risk assessment continues to progress with changes in pesticide and risk assessment technologies.
Environmental Remediation
Authors: Stein Alfred , Kerle Norman 
Published: September 2008

Abstract

This entry gives an overview of environmental remediation, with an emphasis on soil issues. The concept of risk is defined, followed by different ways of modeling (deterministic, probabilistic, and hybrid). A distinction is made between
in situ
and
ex situ
remediation and issues of spatial statistics are included. The issue is illustrated with an example on an environmental cost model.
Risk–Benefit Analysis for Environmental Applications
Authors: Gaylor David W. 
Published: September 2008

Abstract
The issue examined in a risk–benefit or risk–risk analysis is the trade‐off between losses suffered from illnesses or diseases, perhaps due to exposures to an environmental contaminant, and losses due to toxic effects induced by environmental remediation, e.g., chemicals used in cleanup or disinfectants employed to reduce microbial exposures. Risks and benefits may be incurred by different individuals. A management or societal issue arises as to whether the benefits (e.g., reductions of the severity or incidences of illnesses, diseases, or death) outweigh the risks introduced by interventions, e.g., disinfectant by‐products from chemicals used for drinking water treatment. A risk–benefit issue also arises for drugs with side effects and vitamins or essential minerals that become toxic at high doses. A procedure is described for estimating the optimum dose for a chemical intervention, drug, vitamin, or essential mineral that minimizes the total expected loss from the risk of an illness or disease and the loss induced by chemical, drug, or dietary intervention.
Spatial Risk Assessment
Authors: Lawson Andrew B. 
Published: September 2008

Abstract
The spatial analysis of risk is defined as the analysis of georeferenced data pertaining to some adverse outcome. Approaches used for this usually lie in the area of clustering methodology either as hypothesis tests or modeling of risk itself. For fixed spatial sites, and clustering around these, there are well‐developed methods related to distance and direction that can be used. When a focus site is not available, more sophisticated models or scan tests must be employed.
Threshold Models
Authors: Kopp‐Schneider Annette 
Published: September 2008

Abstract

Dose–response models link the dose of an agent to the effect observed in the organism it is administered to. They constitute the central ingredient for risk assessment and are used for estimation of dose corresponding to a specified risk of an adverse effect. Threshold models assume that doses below a threshold,
d
*, lead to the same response as background. Alternatively, a less stringent definition of threshold model requires that the first derivative of response with respect to dose, i.e., the rate of change in response is zero at dose 0. If threshold doses exist and can be estimated, safe exposure levels can be derived, leading to tolerable small exposures. Hence, it is essential for quantitative risk assessment for a specific agent to resolve the question as to whether it exhibits threshold behavior.

Apparent thresholds may be observed in the statistical evaluation if organisms receiving a positive dose show no statistically significant difference in response compared to background. Theoretical arguments have been collected to support the existence of thresholds. Possible threshold mechanisms include metabolic inactivation of the compound, DNA repair, cell cycle arrest, apoptosis of damaged cells, and control of the immune system. The discussion about the existence of thresholds has been closely related to the discussed distinction between genotoxic and nongenotoxic carcinogenic compounds.
Actuary
Authors: Eyland Arne , Lillevold Pål 
Published: September 2008

Abstract
An actuary is educated in the theory and application of mathematics, statistics, economics, probability, and finance. Using sophisticated analytical techniques, actuaries confidently make financial sense of the short term as well as the distant future by identifying, projecting, and managing a spectrum of contingent and financial risks. Life insurance, general insurance, and pension funds have been the traditional working fields of actuaries, and over the recent years it has become more common to employ actuaries also in the wider financial industry.
Comonotonicity
Authors: Dhaene Jan , Vanduffel Steven , Goovaerts Marc J. 
Published: September 2008

Abstract

In an actuarial or financial context it is often necessary to calculate the distribution function or risk measures for random variables of the type

where the marginal distributions of the
X

i

are known but their dependence structure is too difficult to work with. Comonotonicity, which is an extremal form of positive dependence, can be useful to determine easily computable and accurate upper and lower bounds for the distribution of
S
, and hence also for risk measures related to
S
.
Fraud in Insurance
Authors: Guillen Montserrat , Ayuso Mercedes 
Published: September 2008

Abstract
Insurance fraud occurs when the insurer does not know all the facts about the insured and the claim, and when the fraudster believes that some monetary benefits can be gained by distortion of such facts. We explain how fraud can be fought with deterrence and detection, and present several ways to control fraud, specialized investigation units, statistical analysis of claims information, and fraud detection methods, together with the ways for evaluation of their performance and some hints for their practical implementation. Also, we give an overview of the claims handling process. We describe various types of fraud currently encountered in insurance and give examples.
Nonlife Insurance Markets
Authors: Holtan Jon 
Published: September 2008

Abstract
The article describes market characteristics and operational business strategies in nonlife insurance from a risk assessment point of view. The first part highlights general market characteristics like regulations and legislations; profitability and underwriting cycles; market rivalry and entry dynamics; as well as other more specific characteristics like moral hazard, adverse risk selection, and uninsurable risks. The second part describes pricing and underwriting; distribution and sales; and risk and capital management as three main competitive market levers of nonlife insurance suppliers. Market outlook and trends are finally commented.
Optimal Risk‐Sharing and Deductibles in Insurance
Authors: Aase Knut K. 
Published: September 2008

Abstract
Risk sharing in insurance is analyzed, with a view toward explaining the prevalence of deductibles.
First we introduce, in a modern setting, the main concepts of the theory of risk sharing in a group of agents. We apply this theory to the risk‐sharing problem between an insurer and an insurance customer. We develop the concept further through simple examples, illustrating some of the subtle points of this theory.
To deduce deductibles endogenously, not explained in the neoclassical model, we separately introduce (a) the insurable asset as a decision variable, (b) administrative costs, and (c) moral hazard, and illustrate these with the help of examples.
Risk Measures and Economic Capital for (Re)Insurers
Authors: Tsanakas Andreas 
Published: September 2008

Abstract

This contribution relates to the use of risk measures for determining (re)insurers' economic capital requirements. Alternative sets of properties of risk measures are discussed. Furthermore, methods for constructing risk measures
via
indifference arguments, representation results, and reweighting of probability distributions are presented. How these different approaches relate to popular risk measures, such as value‐at‐risk, expected shortfall, distortion risk measures, and the exponential premium principle, is also dealt with. The problem of allocating aggregate economic capital to subportfolios (e.g., insurers' lines of business) is then considered, with particular emphasis on marginal‐cost‐type methods. The relationship between insurance pricing and capital allocation is briefly discussed, based on concepts such as the opunity and frictional costs of capital and the impact of the potential of default on insurance rates.
Ruin Probabilities: Computational Aspects
Authors: Lefèvre Claude 
Published: September 2008

Abstract
This paper is concerned with the numerical evaluation of the probability of ruin or nonruin in insurance mathematics. Our attention is primarily focused on the classical model in collective risk theory, named the compound Poisson model. We present a review of the different methods that are usually proposed to calculate or approximate the (non)ruin probabilities, over an infinite horizon and in finite time.
Early Warning Systems (
EWSs
) for Predicting Financial Crisis
Authors: Yung Sunny W.S. 
Published: September 2008

Abstract
Research on the development of early warning systems (EWSs) for predicting financial crises has gathered momentum in the 1990s. EWS provides a systematic, consistent, and unbiased means to process and analyze information pertinent to financial crises. EWS build on the premise that an economy and its financial markets would behave differently prior to an imminent financial crisis. The “abnormal” behavior has a systematic and recurrent pattern. Therefore, one can judge whether a crisis is about to occur, from the movements of relevant economic and financial indicators. Recent research efforts on EWS have been focusing on currency crises. The two most extensive and well‐documented approaches are the signal approach and the limited dependent regression approach. While EWS may not be sufficiently robust to be relied upon as the sole tool to predict financial crises, they perform substantially better than nonmodel‐based forecasts in the prediction of the Asian financial crises in the late 1990s. Development in other research frameworks that are complementary to EWS is encouraging. One such effort is the resilience framework that assesses the soundness of economic and financial systems at a particular point of time rather than predicting the occurrence of financial crises.
Role of Alternative Assets in Portfolio Construction
Authors: Mulvey John M. , Kim Woo Chang 
Published: September 2008

Abstract

This article discusses the role of nontraditional assets in constructing a portfolio for long‐term investors. Alternative investments can provide novel patterns of returns and increase diversification benefits. In turn, investors can apply leverage in conjunction with wide diversification to improve their risk‐adjusted performance. The concepts are illustrated
via
popular categories of alternative investments. We focus on long‐term investors who are able to rebalance their portfolios on a regular basis. Careful risk management is critical in this domain.
Volatility Smile
Authors: Duan Jin‐Chuan , Li Yun 
Published: September 2008

Abstract

Although the Black–Scholes model lays the foundation to the modern option pricing theory, its empirical performance is rather unsatisfactory as manifested in the phenomenon known as
volatility smile
. This article explains what volatility smile is, and describes some nonparametric techniques and parametric models that may be used to deal with volatility smile for pricing and/or hedging purposes. Kernel regression and the GARCH option pricing model are chosen to be the nonparametric technique and parametric model, respectively, in our demonstration involving a sample of S&P 500 index options. Kernel regression is found to perform better than the GARCH model, but its applicability is limited to pricing the same type of options. Without providing a dynamic for the underlying asset price, kernel regression is also ill‐suited for hedging purposes. In contrast, the GARCH model is not subject to the same application limitations even though its performance on the same type of options is poorer. Thus, both nonparametric techniques and parametric models serve useful purposes in dealing with derivative contracts.
Informational Value of Corporate Issuer Credit Ratings
Authors: Rijken Herbert A. , Altman Edward I. 
Published: September 2008

Abstract
In the transparent capital market for US corporates, rating agencies mostly fulfill the certification needs of investors, and not the need for accurate point‐in‐time credit risk information. When ratings are used as portfolio eligibility standards set by regulators, fund trustees, or boards of directors, investors prefer stable ratings. To support this certification need Moody's and Standard & Poor's stabilize their ratings to a great extent. However, the negative impact of rating stabilization on rating timeliness and default prediction performance fully offsets the information advantage that agencies naturally have from in‐depth analysis by their ratings analysts. In a new competitive environment—after the implementation of the Credit Rating Agency Reform Act in the United States, which abandons the regulatory protection by a National Recognized Statistical Rating Organization (NRSRO) designation—rating agencies may have to push the balance between rating stability and rating timeliness more to the edge.
Credit Scoring

via

Altman
Z
‐Score
Authors: Altman Edward I. , Chan Ngai H. , Wong Samuel Po‐Shing 
Published: September 2008

Abstract

During the last several years, assessing the probability of default (PD) and loss give default (LGD) on credit assets have become a high priority task in the internal credit risk systems of the financial institutions. Such an intense surge of interest is caused by the following factors:

A historic high in the frequency and severity of bankruptcy at the turn of the century have attracted much media attention.

Basel Accord on credit risk capital adequacy—the so‐called Basel II Accord; see Basel Commission on Banking Supervision—for banks and financial institutions to qualify for the advanced internal rating based (IRB) approach, they need to develop better assessment of PD and LGD.

To address the enormous need of PD and LGD evaluations, various quantitative techniques have been employed not only by financial institutions, but also regulatory bodies and the rating agencies. In this chapter, a brief review of the techniques of credit scoring in estimating PD is presented. For LGD computations, one can refer to Chapter 15 of Altman and Hotchkiss.
Structural Models of Corporate Credit Risk
Authors: Wong Hoi Y. 
Published: September 2008

Abstract
Credit risk is related to the ability of a corporation or an individual to honor a financial obligation. A typical example is the default risk embedded in corporate bond markets. Structural models of credit risk are specifically designed to describe corporate credit risk using the capital structure of a firm. The capital structure comprises the firm's underlying assets, equities, and debts. A comprehensive approach even takes into account the optimal allocation of the firm's assets, the dividend policy of the management, and protective covenants of bond holders. The Moody's KMV, a corporation owned by the Moody's Investor Services, is the first rating agency to put a structural model into commercial use since the early 1990s. Popular structural models include, but are not limited to, the Merton model, the Black–Cox model, the Leland–Toft model, and the KMV approach. In this article, a brief overview of the formulation of a structural approach, specifications within the aforementioned models, and the implementation issues is given.