bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aquaculture Insurance
Authors: Theodorou John A. , Tzovenis Ioannis P. 
Published: September 2014

AbstractModern marine aquaculture emerged a few decades ago, triggered by the wild catch decline. Global production more than doubled between 1987 and 1997 and the prospects for future growth especially in Europe are very promising.During the last thirty years, the sector was a high‐risk activity as production technology was – and still is – developing with many experimental farms and a semiacademic character of most hatcheries. Reasonably, nonmanageable risks could be transferred to third parties by means of insurance, and as insurance coverage was important for the banks to finance the new sector, aquaculture drew the attention of insurance underwriters to focus on the venture's technology, history, and experience, financial strength, presentation, and technical qualities. However, aquaculture insurance treads on thin ice as there are few independent consultants and fewer loss adjusters with appropriate experience; protocols are variable, often propriety rendering it difficult to ascertain the claims, or discern mismanagement. Therefore, definition of risks and appropriate counteractions need to be established.
Commercial Multi‐Peril Insurance
Authors: Lynch James 
Published: September 2014

Abstract
Commercial multiple peril insurance is the most popular commercial package policy in US property and casualty insurance. It typically combines several property and general liability coverages under a single policy, creating a convenience in coverage for the insured. It often is sold at a discount, mainly reflecting better experience among risks that buy the package. It is rated by applying factors known as package modifications at the end of the rating process. This permits each coverage in the package to be analyzed in combination with results from the monoline coverage.
Employment Practices Liability Insurance
Authors: Barto Morris 
Published: September 2014

Abstract
Employment Practices Liability Insurance (EPLI) is gaining in popularity both within and outside the US. This policy responds to claims by employees against employers for wrongful employment practices. Such claims include allegations of wrongful refusal to hire, wrongful termination, sexual harassment, and discrimination based on age, race, ethnicity, or disability. Such claims also include claims for retaliation against workers for participating in legally protected activity. EPLI does not cover bodily injury or property damage, but responds for employees' economic losses as well as mental anguish. The coverage is almost always written on a claims‐made basis with expenses eroding limits. EPLI business tends to be highly segmented depending on the limits of coverage, the size of the self‐insured retention or deductible, the size and sophistication of the policyholders, and whether the policyholders are public or private entities.
Fire Insurance
Authors: Cassidy Paul 
Published: September 2014

Abstract
Fire policies cover owners of commercial or industrial property against material damage caused by fire and extraneous perils. Fire policies cover a wide range of insured properties from small rural sheds to billion dollar high‐rise buildings. Losses against these policies are dominated by large individual fire losses and other large losses caused by natural peril and manmade events that generate a large number of simultaneous claims.
Marine Insurance
Authors: Seltmann Astrid 
Published: September 2014

Abstract
Shipping is a business with long‐standing traditions as well as great risks at stake. But although marine insurance is the oldest form of insurance, it seems historically to have been relatively little touched by actuaries. This article will give an overview of marine insurance and its characteristics, the markets, and actuarial aspects. It will also suggest some reasons for the historically low actuarial involvement as well as point out the increasingly interesting potential, not the least due to recent developments in shipping and the general trend towards more sophisticated technical risk analysis and loss prevention measures in the industry in general.
P&I Clubs
Authors: Fry Martin 
Published: September 2014

Abstract

P&I (short for Protection and Indemnity) clubs provide a mutual not‐for‐profit indemnity pool (
see
Pooling in Insurance, Pooling of Employee Benefits) for owners or operators of ships. P&I insurance primarily covers owners or operators of ships for liability to third parties, although the clubs have responded to members' needs and to legislative changes with a range of other policies. The clubs operate by ‘calls’ on members. These calls are an allocation of anticipated claims costs, with adjustments made either by further calls or by refunds.
Risk Classification, Pricing Aspects
Authors: Feldblum Sholom 
Published: September 2014

Abstract
The cost of insurance coverage depends on the attributes of the insured: males have higher mortality than females and young male drivers have higher accident rates than young female drivers. Insurance premiums are based on expected losses and vary similarly. Differentiation among risks by expected cost is termed risk classification.
Risk classification uses the characteristics of a group to set rates for its members. Certain forms of classification are prohibited in many jurisdictions, such as classification by race or ethnic group; other types of classification, such as applicant age for life insurance or type of firm for workers' compensation insurance, are commonly accepted as the proper basis for insurance rates.
A good risk classification system has several attributes, not all of which are essential. Much debate focuses on the importance of each attribute; actuaries and economists emphasize the equity and homogeneity attributes, whereas other groups may emphasize more the intuition, incentives, or social acceptability attributes.
Equilibrium Theory
Authors: Citanna Alessandro 
Published: September 2014

AbstractThe idea of equilibrium was imported from the physical sciences into economics already in the nineteenth century [63]. Economists focused on the study of equilibrium because of the idea that this represents a rest point of an underlying dynamic system. A rest point is interpreted as a state of the economic system in which the dynamic forces governing its evolution have come to a static configuration. In economics, this static configuration has been assumed to mean essentially two things: (a) that individual economic behavior is socially consistent, that is, consumption, production, and exchange plans are feasible and can be carried out; and (b) that plans are optimal in the sense of satisfying the objectives of the individual decision makers (consumers/workers, managers, traders, and so on). The study of equilibrium was first developed in the context of markets in which each individual believes to have a negligible impact on aggregate variables such as the commodity or asset prices. These markets are called perfectly competitive, and the theory of equilibrium for perfectly competitive markets – known asWalrasian equilibrium– achieved a formal, general, and rigorous characterization in the 1950s, especially with the work of Arrow and Debreu and McKenzie. The work of Debreu and Smale established local uniqueness of equilibria, paving the way for comparative statics analysis for the Walrasian equilibrium. An alternative view would consider equilibria for an economic system in which each decision‐maker's actions have a nonnegligible impact on everyone else, and on the aggregate as well. The notion of equilibrium for these economies had been developed by Nash.
Financial Economics
Authors: Aase Knut K. 
Published: September 2014

Abstract
We consider a one period model of efficient risk sharing, when the set of possible sharing rules are constrained to be linear. This can be interpreted as a model of a market for common stocks. Here we study the properties of a competitive equilibrium in an incomplete market. The lack of Pareto optimality is then the typical case.

Since the marketed subspace
M
is a closed, linear subspace of
L
2
, we employ Hilbert space techniques to find the first order conditions. We also discuss conditions for the existence of an equilibrium in an incomplete market.

We conclude with a discussion of the different features of idiosyncratic risks in insurance, and risks in financial markets, where a common ground is suggested.
Optimal Risk Sharing
Authors: Aase Knut K. 
Published: September 2014

Abstract
Optimal risk sharing is considered from the perspective of the risk sharing model introduced by Karl Borch in the late 50's. First we introduce, in a modern setting, the main concepts from this theory. These we apply on the risk sharing problem between an insurer and an insurance customer. We motivate the development through a simple example, illustrating some fine points of this theory. In order to explain deductibles, we introduce transaction costs that are paid out ex post. Moral hazard provides another example.
Asset Management
Authors: Sherris Michael 
Published: September 2014

AbstractAsset management is integral to the actuarial management of any financial security system, although the earliest practical developments in actuarial science took place mostly in life insurance. In more recent years, there has been an increased integration of modern financial theories into actuarial science and the adoption of actuarial techniques in areas of asset management such as credit risk and operational risk.This article will give a brief overview of the historical development of asset management in actuarial science, then briefly outline major asset management strategies used, as well as key models that have been developed, and the issues in implementing asset management strategies.
Derivative Securities
Authors: Cuthbertson Keith 
Published: September 2014

Abstract

There are three main types of derivative securities, namely futures, options, and swaps. Derivative securities are assets whose value depends on the value of some other (underlying) asset and their value is
derived
from the value of this underlying asset. A key feature of futures and options is that the contract calls for
deferred delivery
of the underlying asset (e.g. AT&T shares), whereas spot assets are for
immediate delivery
(although in practice, there is usually a delay of a few days). Derivative securities can be used by hedgers, speculators, and arbitrageurs.

Derivatives often receive a ‘bad press’ partly because there have been some quite spectacular derivatives losses. Perhaps the most famous are the losses of Nick Leeson who worked for Barings Bank in Singapore and who lost $1.4 bn when trading futures and options on the Nikkei‐225, the Japanese stock index. This led to Barings going bankrupt. More recently in 1998, Long Term Capital Management (LTCM), a hedge fund that levered its trades using derivatives had to be rescued by a consortium of banks under the imprimatur of the Federal Reserve Board. This was all the more galling since Myron Scholes and Robert Merton, two academics who received the Nobel Prize for their work on derivatives were key players in the LTCM debacle. The theory of derivatives is a bit like nuclear physics. The derivative products that they have spawned can be used for good but they can also be dangerous, if used incorrectly.
Hedging and Risk Management
Authors: Cairns Andrew J.G. 
Published: September 2014

Abstract
This article summarizes some of the techniques that are used to hedge derivative contracts and manage the risks that arise within a portfolio of derivatives. For simplicity of exposition, the article concentrates here on equity derivatives.
The most straightforward form of hedging is a buy‐and‐hold strategy. This involves finding the optimal mix of cash and equity that will, for example, minimize the variance of the hedging error at the maturity date of the derivative. However, this method can only reduce risk by a small amount. To improve on it, one needs to consider dynamic portfolio strategies, particularly ones where the optimal mix of cash and equity depends on the state of the market at given times in the future.
Interest‐Rate Modeling
Authors: Cairns Andrew J.G. 
Published: September 2014

AbstractThis article describes some of the main developments in interest‐rate modeling since Black & Scholes' and Merton's original articles on the pricing of equity derivatives. In particular, the article focues on continuous‐time, arbitrage‐free models for the fullterm structure of interest rates. Other models that model a limited number of key interest rates or which operate in discrete time (e.g. the Wilkie model) will be considered elsewhere.In this article we will describe the basic principles of arbitrage‐free pricing and cover various frameworks for modeling: short‐rate models (e.g. Vasicek, Cox–Ingersoll–Ross, Hull–White); the Heath–Jarrow–Morton approach for modeling the forward‐rate curve; pricing using state‐price deflators including the Flesaker–Hughston/Potential approach; and the Markov‐functional approach.The article works through various approaches and models in a historical sequence. Partly, this is for history's sake, but, more importantly, the older models are simpler and easier to understand. This will allow us to build up gradually to the more up‐to‐date, but more complex, modeling techniques.
Value‐at‐Risk
Authors: Dowd Kevin 
Published: September 2014

Abstract

The Value at Risk (VaR) on a portfolio is the maximum loss we might expect over a given period, at a given level of confidence. It is therefore defined over two parameters – the period concerned, usually known as the holding period, and the confidence level – whose values are arbitrarily chosen. The VaR is a relatively recent risk measure whose roots go back to Baumol, who suggested a risk measure equal to μ –
k
σ, where μ and σ are the mean and standard deviation of the distribution concerned, and
k
is a subjective parameter that reflects the user's attitude to risk. The term
value at risk
only came into widespread use much later. The notion itself lay dormant for nearly 30 years, and then shot to prominence in the early to mid 1990s as the centerpiece of the risk measurement models developed by leading securities firms. The most notable of these, Morgan's Risk‐Metrics model, was made public in October 1994, and gave rise to a widespread debate on the merits, and otherwise, of VaR. Since then, there have been major improvements in the methods used to estimate VaR, and VaR methodologies have been applied to other forms of financial risk besides the market risks for which it was first developed.
W
ilkie Investment Model
Authors: Hardy Mary R. 
Published: September 2014

Abstract
The Wilkie stochastic investment model, developed by Professor Wilkie, is described fully in two papers; the original version was published in 1986 and the model was reviewed, updated, and extended in 1995. In addition, the latter paper includes much detail on the process of fitting the model and estimating the parameters. It is an excellent exposition, both comprehensive and readable. It is highly recommended for any reader who wishes to implement the Wilkie model for themselves, or to develop and fit their own model.
The Wilkie model is commonly used to simulate the joint distribution of inflation rates, bond yields, and returns on equities. The 1995 paper also extends the model to incorporate wage inflation, property yields, and exchange rates. The model has proved to be an invaluable tool for actuaries, particularly in the context of measuring and managing financial risk. In this article, we will describe fully the inflation, equity, and bond processes of the model. Before doing so, it is worth considering the historical circumstances that led to the development of the model.
Experience‐Rating
Authors: Neuhaus Walther 
Published: September 2014

Abstract
Experience‐rating occurs when the premium charged for an insurance policy is explicitly linked to the previous claim record of the policy or the policyholder.
The most common form of experience‐rating involves two components:

– A tariff premium that is calculated on the basis of the policy's known risk characteristics. Depending on the line of business, known risk characteristics could involve sums insured, type and usage of building or vehicle, the occupation of the insured workers, and so on.

– An adjustment that is calculated on the basis of the policy's previous claim record.

Because it makes such obvious sense to consider a policyholder's claim record before deciding on his or her renewal premium, every pricing actuary and underwriter is experience‐rating most of the time – although not always in a formula‐driven way.
Actuaries have studied formula‐driven ways of experience‐rating under two main headings: Credibility theory and Bonus–malus systems. The formalized use of credibility methods probably originated in workers' compensation insurance schemes in the United States about 100 years ago. Bonus–malus Systems (BMS) are an integral feature of motor vehicle insurance in many developed countries.
This article provides a brief outline of different methods of experience‐rating, discusses properties that an experience‐rating scheme should have and outlines some practical issues.
Reinsurance Pricing
Authors: Schmitter Hans 
Published: September 2014

Abstract
The technical premium is the sum of the risk premium, the fluctuation loading, and the loading for management expenses. In practice, excess‐loss risk premiums are estimated using experience‐rating or exposure rating. Stop‐loss risk premiums are usually estimated by means of rather simple mathematical models. More accurate methods for stop‐loss calculations are applied for determining sliding‐scale premium rates.
Berkson,Joseph
Authors: O'Fallon W. Michael 
Published: September 2014

AbstractBorn in 1899 in Brooklyn, NY; died in 1982 in Rochester, MN. Developed Berkson's fallacy (or Berkson's bias) pointing out that studies of hospitalized patients have a likely selection bias that may invalidate results. Among other health problems, he applied his methods to links between smoking and lung cancer
Halperin,Max
Authors: Greenhouse Samuel W. 
Published: September 2014

AbstractApart from two periods working in the private sector, Halperin (1917–1988) had a career at the National Institutes of Health and later at George Washington University. He contributed to a wide range of statistical theory, including multivariate analysis, regression, interval estimation, distribution‐free tests, and multiple comparisons. He helped to lay the foundations of multicenter clinical trials, writing on planning, data and safety monitoring, and stochastic curtailment.
H
ill,
A
ustin
B
radford
Authors: Doll R. 
Published: September 2014

Abstract
Born 1897; died 1991. A. Bradford Hill introduced the principle of randomization in clinical trials. He was influential in establishing the link between smoking and lung cancer, and as a result of this research drew up guidelines for reaching a positive conclusion about causality. These criteria have come to be widely used in epidemiology and, on occasion, the law.
H
otelling,
H
arold
Authors: Gehan Edmund A. 
Published: September 2014

Abstract
Harold Hotelling was born on September 29, 1895, in Fulda, Minnesota and died on December 26, 1973, in Chapel Hill, North Carolina. He was responsible for much original theoretical work in both statistics and mathematical economics and did much to advance the teaching of statistics at US universities, including Columbia and the University of North Carolina. Hotelling's undergraduate degree was a B.A. in journalism from the University of Washington in 1919, but his mathematical talent was recognized and encouraged by Eric T. Bell. Hotelling received an M.S. degree in Mathematics at the University of Washington in 1921 and a Doctorate of Philosophy at Princeton University in 1924, with a dissertation in the field of topology.
M
antel,
N
athan
Authors: Hale Lauren , Hale Benjamin 
Published: September 2014

Abstract
Nathan Mantel (1919–2002) was most noted for his contribution to the Mantel–Haenszel procedure and its extensions, which allows for combining data from different sources, and so can be used in a variety of contexts: retrospective studies, prospective studies, and laboratory experiments, including those with litter‐matched samples. The technique was also used to develop the logrank test.
S
nedecor,
G
eorge
W
addel
Authors: Cox D. F. 
Published: September 2014

Abstract
G.W. Snedecor (1881–1974) worked at Iowa State University from 1913 to 1958, directing the new Statistical Laboratory from 1933 to 1947. He introduced a course in statistics in 1915 and, with H.A. Wallace, wrote a book on correlation and machine calculation in 1925. He promoted the methods of R.A. Fisher and in 1937 wrote his influential book on statistical methods, the fifth and subsequent editions of which were coauthored with W.G. Cochran.
Categorical Data Analysis
Authors: Imrey Peter B. , Koch Gary G. 
Published: September 2014

AbstractCategorical data consist of observations counted and subclassified into one among a set of disjoint, exhaustive groups. The groups may be defined on the basis of a single or a combination of qualitative characteristics and/or quantitative measurements. When such categorizations are to be modeled as probabilistic “responses” or “outcomes”, statistical analysis must be based upon discrete distributions and requires extensive generalization of the classical notions of regression and analysis of variance commonly used for continuous observations. This overview article reviews statistical inference for categorical data, from the basic issues in analyses of 2 × 2 contingency tables through generalized linear mixed models, with emphasis on approaches that have increasingly unified our concepts of categorical and continuous data modeling.
Categorizing Continuous Variables
Authors: Altman Douglas G. 
Published: September 2014

AbstractContinuous variables are often converted to categorical variables by grouping values. For descriptive purposes, this can be useful but problems can arise when this is done as part of a data analysis. This article discusses relevant issues with categorization of explanatory variables in regression. Effects of categorization are discussed, such as loss of information, power, and efficiency. This is considered first for categorizing into two groups and then for categorizing into several groups. Rather than group according to observed values with several variables, one might group according to a weighted sum of the variables–a prognostic index.
Chi‐Square, Partition of
Authors: Gilula Z. , Haberman Shelby J. 
Published: September 2014

AbstractA chi‐square statistic suitable for testing a primary hypothesis can be partitioned into components such that each component gives a test for a corresponding secondary hypothesis. Some partitionings are exact and some are approximate. The theory is based on the Fisher–Cochran theorem about decomposing quadratic functions of normal variables. The history of this technique is surveyed. Applications are described for contingency tables, with the main focus on the two‐way table and likelihood‐ratio statistics. Brief mention is also made of partitioning into non‐chi‐square components, such as a decomposition that forms the basis of correspondence analysis.
Exact Inference for Categorical Data
Authors: Corcoran Christopher D. , Senchaudhuri Pralay , Mehta Cyrus R. , Patel Nitin R. 
Published: September 2014

AbstractCommonly used methods for analyzing categorical data rely on asymptotic theory and may be misleading for small data sets. ExactPvalues and confidence limits are available based on the permutation distributions of appropriate test statistics. Details are given here for a wide range of settings with contingency tables, which may be stratified or involve clustering, including also logistic regression. Various software packages are available.