bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unit‐Linked Business
Authors: Richards Stephen 
Published: September 2014

AbstractThis article is about unit‐linked insurance business, including unitised with‐profits (UWP) business. The major distinguishing feature of unit‐linked business is that a separate and explicit asset account is maintained for each policy. Each policy can therefore be invested entirely independently of the others, and this investment flexibility is under the control of the policyholder, not the insurer. The value of a unit‐linked policy is directly linked to the investment performance of the unitised funds selected by the policyholder. Unlike traditional with‐profits business, there is no element of discretion for setting bonuses for pure unit‐linked business. An important consequence of this for the insurer is that expenses and charges are explicitly separated, and the simple ‘cost‐plus’ approach to managing pricing has to be abandoned in favor of profit testing.
B
ayesian Claims Reserving
Authors: Alba Enrique 
Published: September 2014

Abstract
Claims reserving methods are usually classified as stochastic or nonstochastic (deterministic) depending on whether or not they allow for random variation. Bayesian methods fall within the first class. Being stochastic, they allow the actuary to carry out statistical inference of reserve estimates as opposed to deterministic models, like the traditional chain‐ladder. As an inference process, the Bayesian approach is an alternative to classical or frequentist statistical inference. From a theoretical point of view, Bayesian methods have an axiomatic foundation and are derived from first principles. From a practical perspective, Bayesian inference is the process of fitting a probability model to a set of data and summarizing the result by a probability distribution on the parameters of the model and on unobserved quantities, such as predictions for new observations.
Claims Reserving Using Credibility Methods
Authors: Verrall Richard 
Published: September 2014

Abstract
The background for the development of credibility theory was the situation in which there was a portfolio of similar policies, for which the natural thing would have been to use the same premium rate for all the policies. However, this would not have captured any individual differences between the policies and therefore a methodology was developed that also utilized the claims experience from the individual policy. In this way, credibility estimation can be seen to allow the pooling of information between risks in premium rating. The consequence of this is that the premium is not estimated using just the data for the risk being rated, but also using information from similar risks. In the context of claims reserving, the reason for using an approach based on credibility estimation is similar: information from different sources can be ‘shared’ in some way.
This article explains the principles of the reserving methods that use credibility theory from the actuarial literature, and refers the reader to the articles and other methods that use a similar modeling philosophy.
Random Number Generation and Quasi‐MonteCarlo
Authors: L'Ecuyer Pierre 
Published: September 2014

AbstractProbability theory defines random variables and stochastic processes in terms ofprobability spaces, a purely abstract notion whose concrete and exact realization on a computer is far from obvious. (Pseudo) random number generators (RNGs) implemented on computers are actually deterministic programs, whichimitateto some extent, independent random variables uniformly distributed over the interval [0,1] (i.i.d.U[0,1], for short). RNGs are a key ingredient for Monte Carlo simulations, probabilistic algorithms, computer games, cryptography, casino machines, and so on.
Copulas: Examples and Construction
Authors: Carrière J.F. 
Published: September 2014

AbstractThis introduction starts with a short history of copulas and their role in actuarial science. Special emphasis is given to bivariate copulas, where many useful families are given. We also give general definitions and Sklar's Theorem, arguably the most important result in copulas. We finish by showing how to construct copulas. Finally, a comprehensive reference list is offered.
Actuary
Authors: Lillevold Pål , Eyland Arne 
Published: September 2014

Abstract
The word ‘actuary’ derives from the Latin word ‘actuarius’, who was the business manager of the Senate of Ancient Rome. It was first applied to a mathematician of an insurance company in 1775 in the Equitable Life Insurance Society (of London, UK). By the middle of the nineteenth century, actuaries were active in life insurance, friendly societies, and pension schemes. As time has gone on, actuaries have also grown in importance in relation to general insurance, investment, health care, social security, and also in other financial applications such as banking, corporate finance, and financial engineering.

Over the times, there have been several attempts to give a concise definition of the term
actuary
. No such attempted definition has succeeded in becoming universally accepted. As a starting point, reference is made to the International Actuarial Association's description of what actuaries are:

Actuaries are multi‐skilled strategic thinkers, trained in the theory and application of mathematics, statistics, economics, probability and finance

and what they do:

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.

This essay will adopt a descriptive approach to what actuaries are and what they do, specifically by considering the actuarial community from the following angles:

Actuarial science – the foundation upon which actuarial practice rests.

Actuarial practice.

Some characteristics of the actuarial profession.
Transforms
Authors: Grübel Rudolf 
Published: September 2014

AbstractThe use of transforms is one of the pervasive ideas in mathematics and the sciences. From a pragmatic point of view, a transformation can be regarded as a tool for simplifying an otherwise difficult operation, for example, a differential equation can be turned into a simple algebraic equation with the help of Laplace transforms. Even the formula, log(x·y) = logx + logy, in connection with the traditional use of logarithmic tables and slide rules may be seen in this light. Transforming an object may also provide insight into its structure, for example, writing a periodic function as a superposition of sine waves. From a theoretical point of view, transforms of the type considered in this article are at the core of major mathematical theories such as harmonic analysis; they have initiated research that spans the whole range of contemporary mathematics.
Censored Distributions
Authors: Lawless J.F. 
Published: September 2014

Abstract
In some settings the exact value of a random variable is observed only if it lies within a certain range. If it lies outside this range, only that fact is known; this is referred to as censoring. This article describes censored distributions, which arise when random variables are censored. The special cases of left‐ and right‐ censoring are considered explicitly.
Maximum Likelihood
Authors: Duan Jin‐Chuan , Fulop Andras 
Published: September 2014

Abstract
We describe the maximum likelihood (ML) estimation and inference. Main theoretical results under the i.i.d. assumption are presented along with discussions. To demonstrate the ML inference, an example dealing with large medical claims and their determining characteristics is presented. We then turn to the ML estimation for stationary dynamic models (i.e. dependent data points) and introduce the quasi‐ML principle as a way of dealing with potential distributional misspecification. The US interest rate series is used to illustrate the quasi‐ML inference for dependent data.
Outlier Detection
Authors: Gather Ursula , Pawlitschko Jörg 
Published: September 2014

AbstractThe analysis of all kinds of statistical data usually includes the detection of outliers as a first exploratory step. The reason for this is twofold: on the one hand we want to screen for aberrant values firstly and on the other hand we are interested in identifying data points that are of special interest. This paper discusses methods of detecting outliers in statistical data for a couple of often‐arising data structures. It also aims at clarifying the notion of an outlier, which often is only vaguely described in the literature.
Frailty
Authors: Hougaard Philip 
Published: September 2014

Abstract
A frailty model is a random effects model for survival data or other time‐to‐event data. Conceptually, frailty models are similar to the normal distribution mixed models, but they are dedicated to the survival data case, where observations may be censored and where updating is natural as the events happen over time. In actuarial science this covers both life/pension for married couples, and repeated events, like car accidents. The frailty is the common random term and is assumed to enter multiplicatively in the hazard function. The gamma distribution is the most commonly used for the frailty, but other distributions may also be useful. Over the last 20 years, both theory and applications have developed so that by now the frailty models have almost become standard models.
Gaussian Process: Overview
Authors: Débicki Krzysztof 
Published: September 2014

AbstractThe class ofGaussian stochastic processesplays an important role in stochastic modeling and is widely used as a rich source of models in many areas of applied probability, including actuarial and financial mathematics.There are many practical and theoretical reasons for using Gaussian processes to model actuarial problems. For instance, the family of Gaussian processes covers a large class of correlation structures and enables explicit analysis of models for which classical renewal‐type tools do not work.On the other hand, theoretical results, mostly based on central limit theorem‐type argumentation, formally justify the use of Gaussian processes.
M
arkov Chains and
M
arkov Processes
Authors: Jacobsen Martin 
Published: September 2014

Abstract
In many applications, the usual assumption of independent and identically distributed random variables has to be abandoned. Markov dependence is a first possibility. We deal with Markov chains in discrete and continuous time. We then extend the setting to Markov processes on more general state spaces. We pay special attention to stationary processes and to piecewise deterministic Markov processes where the behavior of the process between jumps is basically deterministic.
Queueing Theory: Further Observations
Authors: Sigman Karl 
Published: September 2014

Abstract

The mathematical theory of congestion is associated with delays while waiting in a line or
queue
for service in a
system
. Examples of such systems include banks, post offices, and supermarkets as well as telecommunication systems involving telephones, computer networks, the Internet/the World Wide Web, inventory, and manufacturing processes. The early development of the field was motivated by telephone systems and the ways in which they could be made to operate more efficiently.

Much of the theory relies heavily on the use of
probability theory
and
stochastic processes
, and as such is widely viewed as a subfield of stochastic modeling and applied probability. There is also significant interplay with other fields such as scheduling theory, inventory theory, and insurance risk theory; it is a central part of
operations research
.
Nonparametric Statistics
Authors: Brazauskas Vytaras 
Published: September 2014

AbstractThe intent of this article is to give a brief overview of nonparametric statistical methods. We first describe the most commonly used nonparametric procedures in standard statistical settings involving one, two, or more samples. Then we turn to more specialized topics, such asempirical estimation,resampling methods, andsmoothing techniques, which find frequent application in actuarial research and practice. In each case, the emphasis is on the description of the problem and a particular standard approach to its solution, rather than attempting to review all existing procedures that are available for the problem. Also, for each topic, a list of references that treat applications of these methods to actuarial problems is provided.
Stability
Authors: Rusaityte Deimante 
Published: September 2014

Abstract
We discuss the stability problem for stochastic models. The general stability concept was introduced by Zolotarev. Its basic statements with a view towards actuarial risk theory were reproduced by Kalashnikov. Particular attention is payed to the stability of the ruin probability. We provide two examples of stability bounds: for finite state Markov chains and the ruin probability in the Sparre Andersen risk model.
Resampling
Authors: Verrall Richard 
Published: September 2014

Abstract
Resampling methods have become popular in statistics because of the advance in computer power. They can be used to simplify many of the commonly used statistical procedures. In actuarial science, they are particularly relevant to the increasing use of stochastic and Simulation of stochastic processes simulation‐based techniques and it is likely that their use will increase further. We give an introduction to resampling methods, in particular, bootstrapping, and provide some examples of their use in practice.
Cohort
Authors: Eben Kryštof , Malý Marek 
Published: September 2014

AbstractA cohort is a group of subjects, identified by a common characteristic, which is studied over a period of time with respect to an outcome – event of interest. The defining characteristic is often year of birth. The event of interest may be death or disease onset or generally any event that possesses a well‐defined intensity of occurrence.In actuarial science, purchase of a life or medical insurance policy may play the role of defining characteristic – death or insurance claim is the event of interest in this case. On the other hand, marketing of insurance products may require a study of birth cohorts with respect to purchasing life or medical insurance. Here, obviously the purchase is the event of interest.
Life Table Data, Combining
Authors: Tolley H. Dennis , Fellingham Gilbert W. 
Published: September 2014

AbstractThis article discusses methods of combining data from several life tables to form an overall life table. Combining life tables entails building a statistical model using the data from the various tables. The simplest statistical model is to assume that the probabilities of decrement for each age are the same across all tables. In this case, combining entails simple addition. When this assumption is untenable, a more complex statistical model must be built. This article illustrates how to combine life table data using a specific family of statistical models. Other candidate models for this step are mentioned at the end.
Annual Statements
Authors: Babcock Nathan J. 
Published: September 2014

Abstract

Insurance companies and other business organizations that engage in the business of insuring or reinsuring property–casualty exposures are usually required to file
Annual Statements
with the governmental agencies that regulate insurance operations in the pertinent geographic region. These Annual Statements are detailed financial documents that provide information that are not typically available in a company's annual report (such as is provided to stockholders, for example). However, Annual Statements are generally available to interested members of the public, as they are documents in the public record.
Liability Insurance
Authors: Tess Daniel 
Published: September 2014

Abstract
Liability insurance generally provides cover for financial loss, which arises as a result of a finding of liability under law. Many types of specialized insurance products have developed which covers certain types of liability or specific situations in which liability can arise. Some products, which are often grouped together with liability insurance do not cover liability per se, but rather cover the cost of defined benefits to injured persons in lieu of liability awards.
Loss‐of‐Profits Insurance
Authors: Fry Martin 
Published: September 2014

Abstract
Loss‐of‐Profits insurance is intended to cover losses to a business following physical damage by fire or breakdown, until turnover has recovered to a similar level to that prior to the damage. It requires that a fire or machinery breakdown policy has been effected by the insured. Cover is for a maximum sum insured representing the insured's gross profit and the increased cost of working for the anticipated period that production is interrupted following the physical damage, and for a maximum indemnity period representing the anticipated period to restore the business to normal operations and profitability.
Insolvency
Authors: David Cummins J. 
Published: September 2014

AbstractThe objective of insurance is to provide risk pooling and diversification for individuals and business firms facing the risk of loss of life or property due to contingent events. Each individual unit insured through an insurance enterprise pays a premium to the insurer in return for the promise that those individuals suffering a defined loss event during the coverage period will be compensated for the loss by the insurance company. Some period of time elapses between the date of issuance of the insurance contract and the payment of losses to claimants against the pool. This can be anywhere from a few weeks, as in the case of short‐term property insurance policies, to a period of years or decades, as in the case of long‐tail liability insurance policies, life insurance, and annuities. The insurance company holds the funds generated by collecting and investing premiums to finance the claim payments that are eventually made against the pool. Because losses can be larger than expected, insurance companies hold equity capital over and above the expected value of future claim costs in order to ensure policyholders that claims will be paid in the event of adverse deviations. Nevertheless, there is a significant risk that the funds held by the insurer will be inadequate to pay claims. This is the risk of insolvency or bankruptcy, which provides the primary rationale for the intense regulation of insurance companies worldwide.
Lotteries
Authors: Pflug Georg 
Published: September 2014

AbstractLottery companies sell risk to customers. They are natural counterparts to insurance companies who buy risk from customers. Insurance contracts contain a positive risk premium, that is, the asked premium exceeds the expected losses to cover the administrative costs and to allow profit and a safety margin for the insurance company. Lottery contracts contain a negative risk premium, that is, the price of the lottery ticket exceeds the expected win, again to allow a profit for the lottery company or – in state‐owned lotteries – for the national finances.It seems quite paradoxical that a person may hold some insurance contracts (thus seems to be risk‐averse) and at the same time buy lottery tickets (thus seem to be risk‐seeking). A preliminary explanation can be found in the nature of the two risks: While insurance protects against unwanted risks and risks that cannot be controlled (accident, fire, etc.), lotteries offer new, independent, maybe even thrilling risks.
Nonexpected Utility Theory
Authors: Machina Mark J. 
Published: September 2014

AbstractAlthough the expected utility model of risk preferences has long been the most widely used model of choice under uncertainty, a growing body of evidence has uncovered systematic departures from expected utility maximization. Researchers have characterized the form of these departures, developed several models of nonexpected utility risk preferences, and have applied these models to the analysis of standard questions such as the theory of insurance.
Oligopoly in Insurance Markets
Authors: Sonnenholzner Michael , Wambach Achim 
Published: September 2014

Abstract
Although empirical evidence on oligopolistic behavior in some sectors of the insurance industry exists, relatively few theoretical models have been developed. We discuss the cause for this discrepancy, elaborate on the classical oligopoly models and their applicability to the insurance market, and introduce the few models that are specific to the insurance industry. The underlying mechanism in these models is that undercutting a rival makes the pool of risks unattractive and thus, oligopolistic market power can be sustained.
Capital Allocation for
P
&
C
Insurers: A Survey of Methods
Authors: Venter Gary G. 
Published: September 2014

Abstract
Capital allocation is never an end in itself, but rather an intermediate step in a decision‐making process. Trying to determine which business units are most profitable relative to the risk they bear is a typical example. Pricing for risk is another.
While the general topic is capital allocation, this article looks at methods of answering the questions that capital allocation addresses. The following four basic approaches will be reviewed:

Selecting a risk measure and an allocation method, and using them to allocate all capital.

Comparing the actual versus model pricing by a business unit.

Computing the cost of the marginal capital needed for or released by target strategies.

Evaluating the profitability in comparison to a leveraged mutual fund.
DFA‐Dynamic Financial Analysis
Authors: Blum Peter , Dacorogna Michel 
Published: September 2014

Abstract
Dynamic Financial Analysis (‘DFA’) is a systematic approach based on large‐scale computer simulations for the integrated financial modeling of non‐life insurance and reinsurance companies aimed at assessing the risks and the benefits associated with strategic decisions.
The most important characteristic of DFA is that it takes an integrated, holistic point of view, contrary to classic financial or actuarial analysis in which different aspects of one company were considered in isolation from each other. Specifically, DFA models the reactions of the company in response to a large number of interrelated risk factors including both underwriting risks – usually from several different lines of business, as well as asset risks. In order to account for the long time horizons that are typical in insurance and reinsurance, DFA allows dynamic projections to be made for several time periods into the future, where one time period is usually one year, sometimes also one quarter. DFA models normally reflect the full financial structure of the modeled company, including the impact of accounting and tax structures. Thus, DFA allows projections to be made for the balance sheet and for the profit‐and‐loss account (‘P&L’) of the company. Technically, DFA is a platform using various models and techniques from finance and actuarial science by integrating them into one multivariate dynamic simulation model. Given the complexity and the long time horizons of such a model, it is not anymore possible to make analytical evaluations. Therefore, DFA is based on stochastic simulation (also called Monte Carlo imulation), where large numbers of random scenarios are generated, the reaction of the company on each one of the scenarios is evaluated, and the resulting outcomes are then analyzed statistically.