bluuebunny/crossref_metadata_2025_split huggingface dataset:
Derivative Securities
Authors: Cuthbertson Keith 
Published: September 2006

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.
Financial Markets
Authors: Nitzsche Dirk 
Published: September 2006

Abstract

Financial markets provide the antithesis of Polonius' advice to his son in Shakespeare's
Hamlet
, of ‘neither a borrower or lender be’. Individuals benefit not only from consuming goods and services that are primarily produced by firms but also by the government sector (e.g. education, health, and transport facilities in many countries). To produce ‘goods’, firms and the government often have to finance additional current and capital investment expenditures by borrowing funds. Financial markets and institutions facilitate the flow of funds between surplus and deficit units.
Financial Reinsurance
Authors: Halliwell Leigh J. 
Published: September 2006

Abstract
The article presents ‘financial reinsurance’ as a continuum between finance and reinsurance. It discusses features of finance and reinsurance, illustrates simple combinations of the two, and describes basic financial‐reinsurance contracts. It concludes with the benefits and dangers of financial reinsurance, its accounting treatment, and the need to define risk transfer.
Frailty
Authors: Hougaard Philip 
Published: September 2006

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.
Frontier between Public and Private Insurance Schemes
Authors: Zweifel Peter 
Published: September 2006

Abstract
From an economic point of view, insurance coverage is very much like any other goods or service purchased by households and firms. One would therefore expect individually contracted insurance coverage provided by private firms to dominate. However, mandated coverage provided by public insurance is common for the provision for old age, disability, workers' compensation, health, long‐term care and unemployment. Contributions to public insurance amount to about 30% of the Gross Domestic Product (GDP) and are still rising in major industrialized countries whereas the premium revenue of personal lines of private insurance make up as little as 5% of GDP even in the United Kingdom, a stronghold of private insurance. This raises the question, what determines the frontier between public and private insurance? and, in a normative vein, would a shifting of this frontier be in the interest of the insured?
Fuzzy Set Theory
Authors: Derrig Richard A. , Ostaszewski Krzysztof M. 
Published: September 2006

Abstract
Traditional actuarial methodologies have been built upon probabilistic models. They are often driven by stringent regulation of the insurance business. Deregulation and global competition of the last two decades have opened the door for new methodologies, among them, being fuzzy methods. The defining property of fuzzy sets is that their elements possibly have partial membership (between 0 and 1), with degree of membership of zero indicating not belonging to the set considered, and degree of membership of one indicating certainty of membership.
This article provides a quick overview of developing fuzzy sets methodologies in actuarial science, and the basic mathematics of fuzzy sets.
Gaussian Processes
Authors: Débicki Krzysztof 
Published: September 2006

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.
Graduation
Authors: Miller Robert B. 
Published: September 2006

Abstract
In actuarial science, graduation refers to the adjustment of a set of estimated quantities to make the adjusted quantities conform to a pattern suitable for insurance purposes. We distinguish between two broad categories of approaches to actuarial graduation: those that involve fitting curves defined by functions and those that involve other methods. Curve‐fitting approaches use popular functional forms such as Gompertz, Weibull, Makeham, and splines. Non–curve‐fitting approaches in this article are grouped under the headings Weighted Moving Average Methods, Kernel Methods, Whittaker Methods, and Bayesian Methods.
Heckman–Meyers Algorithm
Authors: Klugman Stuart A. 
Published: September 2006

Abstract
The collective risk model for aggregate losses states that total losses for a period comprise a random number of random payments where the payments have identical distributions and all variables are independent. Evaluating probabilities for this model can be difficult. The Heckman–Meyers Algorithm is one of several approaches to this problem. While computationally challenging, it is accurate and in many situations is extremely fast.
Inflation Impact on Aggregate Claims
Authors: Garrido José , Léveillé Ghislain 
Published: September 2006

Abstract
Inflation can have a significant impact on insured losses. Insurance companies recognize this effect on their aggregate claims when setting premiums and reserves.
Interest earned by insurance companies on their reserves can, to some extent, offset the effect of inflation on claims. This article looks at the combined effect of these economical variables, inflation and interest, on the distribution of aggregate claims.
Insurance Regulation and Supervision
Authors: Tess Daniel 
Published: September 2006

Abstract
Aside from normal regulatory activities, companies and other entities that sell or provide insurance products are regulated in two special ways. Solvency regulation concerns insurers' financial ability to pay future claims against policies sold, and price regulation concerns the level of premiums that are charged to policyholders. Actuaries generally play a key active role in both of these regulatory systems.
Life Insurance Mathematics
Authors: Norberg Ragnar 
Published: September 2006

Abstract
The subject matter and methodology of modern life insurance mathematics are surveyed. Standard insurance products with payments depending only on life history events are described and analyzed in the commonly used Markov chain model under the assumption of deterministic interest rates. The actuarial equivalence principle for determining premiums and reserves is motivated by risk diversification in large portfolios. Differential equations for reserves and other conditional expected values are obtained and proclaimed basic constructive and computational tools. The traditional method for managing nondiversifiable risk arising from changes in, for example, mortality rates and interest rates is outlined; premiums are calculated under prudent assumptions and systematic surpluses thus created are repaid as bonus. An alternative way of managing nondiversifiable risk is to link the contractual payments to the development of mortality, interest, and possibly other demographic or economic indexes. Examples of so‐called index‐linked products are given, and premiums and reserves are determined by combined use of classical actuarial principles and principles of pricing and hedging from financial mathematics. Transfer of nondiversifiable risk to the financial markets through creation of tradable insurance derivatives is outlined as an idea. Methods for estimation of mortality and other vital rates, formerly a major issue in life insurance mathematics, are fetched from modern statistical life history analysis and therefore only briefly described.
Long Range Dependence
Authors: Samorodnitsky Gennady 
Published: September 2006

Abstract

Long‐range dependence (synonymous with
long memory
) is a property of some stationary processes. Intuitively, the notion is clear: a stationary stochastic process that has long‐range dependence is a process whose memory lasts very long. However, the term ‘long‐range dependence’ means more than simply ‘longer memory’. It means memory so long that the behavior of a stochastic process changes dramatically, and so long that the usual statistical tools break down when applied to observations arising from a long‐range dependent process.
Long‐Term Care Insurance
Authors: Jones Bruce L. 
Published: September 2006

Abstract
It is well known that older adults become increasingly frail as they age. Even in the absence of a disease, one eventually loses the physical and/or mental ability to live independently. The kind of assistance required when this occurs is referred to as long‐term care (LTC). LTC is very different from the health care needed by individuals with injury or disease, and it requires a different set of services. Often, long‐term care services can be provided in a person's home, and this is the ideal situation. However, many cases require that the individual be institutionalized in an appropriate LTC facility.
Loss‐of‐Profits Insurance
Authors: Fry Martin 
Published: September 2006

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.
Lotteries
Authors: Pflug Georg 
Published: September 2006

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.
Marine Insurance
Authors: Seltmann Astrid 
Published: September 2006

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.
Market Models
Authors: Pelsser Antoon 
Published: September 2006

AbstractInterest rate modelling is one of the crucial ingredients in the calculation of the market value of insurance products. The standard case is the LIBOR Market Model where all interest rates have log‐normal distributions. We deal with LIBOR rates and options on LIBOR rates (caplets and floorlets) first, then with swap rates and options on swap rates (swaptions).
Markov ChainMonteCarlo Methods
Authors: Roberts Gareth O. , Rosenthal Jeffrey S. 
Published: September 2006

AbstractOne of the simplest and most powerful practical uses of the ergodic theory of Markov chains is in Markov chain Monte Carlo (MCMC). Suppose we wish to simulate from a probability density π (which will be called thetargetdensity) but that direct simulation is either impossible or practically infeasible (possibly due to the high dimensionality of π). This generic problem occurs in diverse scientific applications, for instance, Statistics, Computer Science, and Statistical Physics.Markov chain Monte Carlo offers an indirect solution based on the observation that it is much easier to construct an ergodic Markov chain with π as a stationary probability measure, than to simulate directly from π. This is because of the ingeniousMetropolis–Hastingsalgorithm, which takes an arbitrary Markov chain and adjusts it using a simple accept–reject mechanism to ensure the stationarity of π for the resulting process.
Maximum Likelihood
Authors: Duan Jin‐Chuan , Fulop Andras 
Published: September 2006

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.
Moral Hazard
Authors: Sonnenholzner Michael , Wambach Achim 
Published: September 2006

Abstract
In an insurance context, moral hazard comprises the phenomenon that having insurance gives the insured an incentive to alter his behavior to the detriment of the insurer. This manifests itself by the insuree employing suboptimal precautionary effort to avoid the loss. We discuss the insurance‐specific moral hazard model and several extensions with regard to many effort levels, continuous loss distribution, multiperiod contracts, and renegotiation.
Mortality Laws
Authors: Forfar David O. 
Published: September 2006

AbstractSince the time of De Moivre in 1725, various mathematical formulae have been suggested to represent the pattern of mortality rates with age. Some of the formulae have applied at adult ages, some at very young ages, and some over the whole span of life. Certain formulae have been used in the graduation of mortality data. The article gives a survey of the mathematical laws used and examines how mortality might look in the foreseeable future if it goes on improving.
Screening Methods
Authors: Malutov Michail , Wynn Henry P. , Zhigljavsky Anatoly 
Published: September 2006

AbstractScreening is a term applied to the general approach of detecting active or significant factors, elements, units, and so on when there are a large number of inactive factors. The meaning of ‘active’ depends on the context: effective treatments, presence of a disease, fault, etc. The methods are of considerable importance in medical and industrial applications. The theoretical aspects, particularly finding optimal or efficient experimental designs are closely related to search methods and special combinatorial constructions.
Neural Networks
Authors: Kramer Bert 
Published: September 2006

Abstract
Neural networks, a machine learning technique, are especially good at pattern recognition tasks. They have been successfully applied to a large number of classification and prediction tasks, like insurer insolvency prediction.
Neural networks consist of a (large) number of relatively simple processing units, which are usually arranged in layers. The output of a neural network is determined by the strengths (the weights) of the connections between the units and by the activation function, which transforms the input of a specific unit into the output from this unit. Two types of networks can be distinguished: feedforward networks and recurrent networks. Feedforward neural networks are a subset of the class of nonlinear regression and discrimination models.

In order to obtain the desired characteristics, a neural network has to be trained. Training algorithms can be categorized as supervised or unsupervised learning. The most common supervised training method for feedforward neural networks is called
back‐propagation
. Unsupervised learning is most commonly used in the field of cluster analysis. A common unsupervised training method is Kohonen's self‐organizing map.
Derivative Pricing, Numerical Methods
Authors: Vetzal K.R. 
Published: September 2006

Abstract
Numerical methods are needed for derivatives pricing in cases where analytic solutions are either unavailable or not easily computable.
The subject of numerical methods in the area of derivatives valuation and hedging is very broad. A wide range of different types of contracts are available, and in many cases there are several candidate models for the stochastic evolution of the underlying state variables. Many subtle numerical issues can arise in various contexts. A complete description of these would be very lengthy, so in this article we will only give a sample of the issues involved.
The article first presents a detailed description of the different methods available in the context of the Black – Scholes – Merton model for simple European and American‐style equity options. It then describes how the methods can be adapted to more general contexts, such as derivatives dependent on more than one underlying factor, path‐dependent derivatives, and derivatives with discontinuous payoff functions.
Oligopoly in Insurance Markets
Authors: Sonnenholzner Michael , Wambach Achim 
Published: September 2006

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.
Ornstein–Uhlenbeck Process
Authors: Sørensen Michael 
Published: September 2006

Abstract
The Ornstein–Uhlenbeck process solves a linear stochastic differential equation driven by a Brownian motion and is therefore analytically tractable. Transition densities, asymptotics, and likelihood inference can be treated explicitly. Also the more general Ornstein–Uhlenbeck type processes that solve linear stochastic differential equations driven by Lévy processes are presented.
Outlier Detection
Authors: Gather Ursula , Pawlitschko Jörg 
Published: September 2006

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.
P&I Clubs
Authors: Fry Martin 
Published: September 2006

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.
P
areto Optimality
Authors: Dana Rose‐Anne 
Published: September 2006

Abstract
Any sequence of positive, independent and identically distributed random variables can be considered as a renewal process. Traditional examples are the interclaim times and the successive claims in a homogeneous portfolio. The article describes the most important quantities and results from renewal theory. Also defective, delayed and discrete‐type renewal processes are discussed and illustrated with examples from actuarial science.
Point Processes
Authors: Rolski Tomasz 
Published: September 2006

Abstract
The theory of point processes originates from problems of statistical demography, insurance, and mortality tables, although the connection may seem remote at first sight. More recent are problems from risk theory, in which one encounters the problem of how to describe a stream of arriving claims in a mathematically formal way.
The simplest case of a point process is the Poisson process. In general, we understand a point process to be a method of randomly allocating points to intervals or rectangles, etc. Formalizing this into the language of mathematics, a (stochastic) point process is a random denumerable collection of points in a state space 𝔼 with some small condition not allowing clustering. It turns out that it is sometimes mathematically convenient to define point processes as random variables assuming values at a space of measures.