bluuebunny/crossref_metadata_2025 huggingface dataset:
Economic Capital
Authors: Rosen Dan , Saunders David
Published: May 2010

In financial institutions,
economic capital
(EC) acts as a buffer to absorb large losses, thereby protecting depositors and other claim holders and providing confidence to external investors and rating agencies on the financial health of the firm. EC covers all the risks that may force a financial institution into insolvency (e.g., market, credit, operational, liquidity, business, and strategic risks). Commonly, capital is designed to absorb
unexpected losses
, at a specified confidence level, while reserves are set aside to cover
expected losses
. Thus, EC is commonly defined as the difference between the portfolio's
Value at Risk
and the expected loss of the portfolio.
Risk‐Adjusted Return on Capital (RAROC)
Authors: Turnbull Stuart M.
Published: May 2010

This article describes some of the practical and academic issues that arise in allocating capital and measuring the performance of businesses within a bank.
Stress Testing
Authors: Schachter Barry
Published: May 2010

Stress testing and scenario analysis encompass a variety of nonstatistical tools for evaluating the risk of loss, that is, reduction in market value, in a portfolio of financial instruments. Typically, these are used in conjunction with the other risk measurement methods, most notably Value at Risk. These tools are used in the construction of portfolio hedges, in the determination of regulatory and economic capital adequacy, and in setting portfolio risk limits, among others.
Backtesting
Authors: Christoffersen Peter
Published: May 2010

We survey methods for backtesting risk models using theex anterisk measure forecasts from the model and theex postrealized portfolio profit or loss. The risk measure forecast can take the form of a Value at Risk, an expected shortfall, or a distribution forecast. The backtesting can be seen as a final diagnostic check on the aggregate risk model carried out by the risk management team that constructed the risk model or they can be used by external model evaluators such as bank supervisors. The approaches suggested require only information on the dailyex anterisk model forecast and the dailyex postcorresponding profit and loss. In particular, knowledge about the assumptions behind the risk model and its construction is not required.
Multivariate Distributions
Authors: Frey Rüdiger
Published: May 2010

Financial risk models, whether for market or credit risks, are inherently multivariate. In this article, we review some multivariate distributions (models for the distribution of random vectors) that are particularly useful for financial data.
Electricity Markets
Authors: Meyer‐Brandis Thilo
Published: May 2010

Since the early 1990s, an increasing number of countries worldwide have liberalized their electricity power sectors. Contrary to the situation that prevailed earlier, when power sectors were not open to competition and prices were set by regulators according to the cost of generation, transmission, and distribution, electricity prices are now determined by an equilibrium of supply and demand. On one hand, electricity contracts are traded over the counter through bilateral agreements. On the other hand, the deregulation of electricity markets has led to the creation of organized electricity exchanges, where electricity is quoted almost as any other commodity. One effect of the liberalization of electricity markets is the introduction of substantial price risk with volatilities much higher than on stock markets and with distinct features like impressive price spikes.

Therefore, a precise statistical modeling of electricity price behavior is necessary for energy risk management, pricing of electricity‐related options, and evaluation of production assets. In this article, we give a short introduction to modern electricity markets, before we focus on an overview of reduced‐form models for electricity spot prices proposed in the literature.
Electricity Forward Contracts
Authors: Benth Fred E.
Published: May 2010

Electricity is by nature a nonstorable commodity, and this raises questions on the exact link between spot and forward/futures prices. From arbitrage theory the discounted forward price in frictionless markets is today's spot price. This is a result of the buy‐and‐hold hedging strategy, which is infeasible for electricity because of no‐storability of the power. We discuss how to obtain forward prices for electricity based on spot models and view some recent results in this regard.
Commodity Risk
Authors: Kleindorfer Paul R.
Published: May 2010

This article surveys the underlying theory related to the use of options and other derivative contract forms in support of commodity hedging in supply management. Such derivatives, on both capacity and output, play an important role in integrating long‐term and short‐term contracting between multiple buyers and sellers in such markets. We focus on the case of a single buyer interested in hedging commodity risk exposure through contracts and derivative instruments, the latter assumed to be defined on a competitive spot market. Sellers compete to supply the buyer in a market in which, in the short run, capacities and technologies are fixed. The buyer can reserve capacity through contracts for physical delivery obtained from any seller and the buyer can also hedge these contracts through financial contracts on the same (or correlated) underlying indices from financial intermediaries. Output on the day can be obtained either by executing such contracts or in the associated spot market. Determining optimal Value‐at‐Risk (VaR)‐constrained portfolios of sourcing and hedging options across contracts and spot market opportunities is the key problem addressed.
Commodity Trading
Authors: Falbo Paolo , Felletti Daniele , Stefani Silvana
Published: May 2010

Mean‐reverting processes find large applications in commodities and energy markets as well as in large sectors of financial markets. In this article, we face the problem of defining optimal strategies for a trader wishing to take a speculative position based on the locally predictable trend that mean‐reverting processes possess. For this purpose, we develop two different trading strategies: one optimizes the expected return for a fixed risk level (fixed risk strategy, FRS) and the other just maximizes the expected return (risk neutral—maximum return strategy, MRS). An empirical application is performed on several commodity price series.
Oil Market
Authors: Barone‐Adesi Giovanni , Cattani Corinne
Published: May 2010

The dynamics of oil pricing, production and reserves are reviewed. The forward market for crude oil and basic trading strategies are briefly described.
Emissions Trading
Authors: Harris Chris
Published: May 2010

Constraints on emissions and other environmental impacts have various forms such as taxes, nontradable limits, and limits with tradable allowances. This article captures the key commercial features of emission schemes and expresses them in terms of traded derivatives, so that the shadow costs of emission abatement and the market prices can be modeled using the quantitative methods described in this encylopedia.
Weather Derivatives
Authors: Barrieu Pauline , Scaillet Olivier
Published: May 2010

We give a short introduction to weather derivatives. We discuss what they are made for. We describe the markets on which they are exchanged, and how they are used to promote agricultural risk transfer in developing countries
via
the World Bank program. We also treat some specific issues such as basis risk, pricing, and design.
Bid–Ask Spreads
Authors: Bessembinder Hendrik , Venkataraman Kumar
Published: May 2010

Bid‐ask spreads, which measure trade execution costs, and reflect the price concessions necessary to complete transactions quickly, are important as indicators of market quality and in determining traders' actual investment results. Execution costs arise because it is costly to provide liquidity, and can be estimated on the basis of comparisons of trade prices with proxies for underlying security value, with the most common proxy being the quote midpoint. Comparisons can be of trade prices to midpoints at or before the time of the trade, as in effective spread measures, or to midpoints after the trade, as in realized spread measures. Recent research indicates that trade execution costs have declined in US markets in recent years, and documents substantial variation in average trading costs across international equity markets.
Liquidity Premium
Authors: Amihud Yakov , Mendelson Haim
Published: May 2010

An asset is liquid if it can be traded quickly and at low cost. In addition to risk, liquidity affects asset prices and returns. Because investors want to be compensated for bearing the costs of illiquidity,
asset returns are increasing in illiquidity
. Thus, asset prices should depend on two asset characteristics: risk and liquidity. This article surveys research on the effects of liquidity on asset prices and returns. We find that liquidity is an important factor in capital asset pricing.
Adverse Selection
Authors: Parlour Christine A. , Rajan Uday
Published: May 2010

Adverse selection arises when a trader has private information related to the value of an asset. Strategic behavior by the informed trader reduces the payoff of an uninformed counterparty. In quote‐driven markets, adverse selection directly leads to a spread between bid and ask prices for the asset. In limit order markets, a limit order is subject to adverse selection, since the value of the asset may change before a market order comes in. Market orders from uninformed traders also face adverse selection, since limit orders may themselves have been submitted by informed traders.
Price Impact
Authors: Bouchaud Jean‐Philippe
Published: May 2010

We define what “price impact” means, and how it is measured and modeled in the recent literature. Although this notion seems to convey the idea of a forceful and intuitive mechanism, we discuss why things might not be that simple. Empirical studies show that while the correlation between signed order flow and price changes is strong, the impact of trades on prices is neither linear in volume nor permanent. Impact allows private information to be reflected in prices, but by the same token, random fluctuations in order flow must also contribute to the volatility of markets
Inventory Effects
Authors: Moulton Pamela C.
Published: May 2010

Traditional economic models of price‐setting focus on call‐auction markets in which all trading occurs simultaneously, at preestablished discrete times, with no market makers involved. Such models leave no role for any of the three sources of friction found in modern models of market liquidity: inventory, order‐processing costs, and adverse selection. With the development of market microstructure research, researchers studying the links between market‐maker inventories and liquidity have shed considerable light on how market makers (often modeled as dealers) resolve temporal imbalances in the continuous trading environment that characterizes most financial markets. In general, inventory models predict that market makers set ask prices above bid prices, that they lower their quotes when they have very large inventory positions, and that they may or may not change the magnitude of their quoted spreads as their inventory changes, depending on whether they are capital constrained or not. Models in which market makers face capital constraints also offer an explanation of flight to quality, in which the riskiest securities suffer the greatest liquidity declines. Multidealer inventory models predict that relative inventory positions give rise to interdealer trading and determine which dealers have the best (lowest ask or highest bid) quotes in a market. These predictions have been tested and largely borne out in empirical studies to date.
High‐Frequency Data
Authors: Lillo Fabrizio , MiccichÈ Salvatore
Published: May 2010

We introduce some of the most common types of high‐frequency financial data: tick‐by‐tick data, trade and quote data, order book data, and market member data. We describe the types of variables that are usually available in the most popular high‐frequency financial databases. We discuss the issues related to the handling of these data, including cleaning protocols, timing issues, and issues related to data size. We then briefly consider the issues related to the stylized facts detected in the empirical analysis of high‐frequency data. Specifically, we consider (i) the irregular temporal spacing of the events at high frequency and its relevance for the econometric modeling of financial variables, (ii) the discreteness of the financial variables under investigation, (iii) the problems related to proper definition of financial variables, (iv) their daily periodicity, that is, typical intraday patterns, (v) their temporal correlations, for example, the bid–ask bounce and long memory properties, and (vi) problems related to the specificity of the market structure and rules.
Order Flow
Authors: Lillo Fabrizio
Published: May 2010

We present the definition of the different components of the order flow in a double auction market characterized by a limit order book. We present some statistical regularities of order flow observed by separately considering market orders, limit orders, and cancellations. The unconditional regularities include the fat‐tailed distribution of order volume and the long memory of order signs. For limit orders and cancellations, we also present unconditional statistical regularities on the limit price of the order. We then discuss how the submission rate of different types of orders is dependent on the size of the bid–ask spread and on the depth of the market. We discuss the selective liquidity taking mechanism, that is, how market order volumes are influenced by the volume at the best. Finally, we discuss some empirical results on commonalities of order flow across a portfolio of stocks.
Probability of Informed Trading
Authors: Hvidkjaer Soeren
Published: May 2010

The extent of private information in markets is a critical determinant in the price formation process. By modeling the trading process and the market maker's learning process, we can use trade data to make inferences about this private information or, more precisely, about the probability of informed trading (PIN). This article presents the PIN model, shows how to estimate the model
via
maximum likelihood, and discusses applications in market microstruture and asset pricing.
Call Auction Markets
Authors: Schwartz Robert A. , Davis Paul L.
Published: May 2010

A call auction batches orders together for simultaneous execution in a multilateral trade, at a single price, at a single point in time. Order batching amasses liquidity, tempers price volatility, lowers execution costs for participants, and sharpens price discovery for the broad market. Accordingly, calls are being widely used to open and to close trading in electronic exchange markets (for instance, London, Frankfurt, Paris, Zurich, and Nasdaq). The New York Stock Exchange also opens and closes its markets with calls but, as of this writing, its calls are not fully electronic. Despite their desirable attributes, however, a call's ability to attract critical mass order flow is not foreordained but depends critically on its specific architectural design. This article describes how orders are handled and clearing prices are set in call auction trading. It further considers the advent of electronic calls; their use at market openings and closings to temper volatility and sharpen price discovery; and their particular importance for trading mid‐ and small‐cap stocks and for handling the large orders of institutional investors. Lastly, the properties of limit and market orders in a call auction are contrasted with their attributes in a continuous trading environment.
Limit Order Markets
Authors: Foucault Thierry
Published: May 2010

Trading in financial markets increasingly takes place in limit order markets. This article provides an overview of the academic research on these markets.
Specialist Markets
Authors: Saar Gideon
Published: May 2010

A specialist market system is defined as a hybrid market structure that includes an auction component (e.g., a floor auction or a limit order book) together with one or more designated market makers (“specialists”) who trade as dealers for their own account. The designated market makers have some responsibility for the market; for example, specialists are usually obligated to continuously post quotes with reasonable depth. In return, they get a preferential treatment, be it access to order flow, information about the state of the market, discounted trading fees, or direct monetary compensation from the exchange or the listed firm.

While market microstructure theory shows that the hybrid structure gives rise to complex strategic interaction between limit order traders and specialists, it also suggests that the presence of specialists is more beneficial for thinly traded stocks. Preferential treatment of specialists is required for such stocks because the potential for profitable dealing is small relative to the cost of maintaining a market presence. An interesting trend over the past two decades has been the adoption of this market structure by derivatives exchanges and stock markets around the world for the trading of less active securities. Empirical research documents improvements in market quality when these securities move from a pure limit order book structure to a hybrid specialist structure.
Order Types
Authors: Kockelkoren Julien
Published: May 2010

On electronic markets, investors can choose between different order types when they want to buy or sell stocks. The two basic types are market orders and limit orders, but there exist various other types. In this article, the most frequently used order types and review are reviewed. Some empirical facts on order types are highlighted.
Factor Models
Authors: Connor Gregory , Korajczyk Robert
Published: May 2010

Factor models of security returns decompose the random return on each of a cross‐section of assets into pervasive components, affecting almost all assets, and a diversifiable component. We describe four alternative approaches to factor modeling of asset returns. We also discuss issues related to estimating factor models and testing for the appropriate number of factors.
Stochastic Volatility Models
Authors: Fouque Jean‐Pierre
Published: May 2010

Models of stochastic volatility are presented and risk‐neutral pricing is introduced. Positive aspects and difficulties of stochastic volatility modeling are summarized.
Mixed Data Sampling
Authors: Ghysels Eric
Published: May 2010

MIxed DAta Sampling (MIDAS) regression models were introduced in both filtering and regression context to deal with situations, often encountered in practice, where economic time series are not sampled at the same frequency. We draw comparisons between temporal aggregation and MIDAS and discuss volatility forecasting in this context.
Pricing Kernels
Authors: Hansen Lars Peter , Renault Eric
Published: May 2010

Pricing kernels or stochastic discount factors (SDFs) are used to represent arbitrage‐free valuation operators in dynamic stochastic economies. After deriving convenient representations for prices, we provide several examples of SDFs and discuss econometric methods for estimation and testing of asset pricing models that restrict the SDFs.
Mixture of Distribution Hypothesis
Authors: Hill Jonathan B.
Published: May 2010

We outline the mixture of distributions hypothesis as a means to explain heavy tails in financial time series. We discuss the hypothesis' historical roots, and present the most popular form of the hypothesis and its implications for modeling asset returns. Original contributions and modern extensions are cited.
Simulation‐Based Estimation
Authors: Monfort Alain
Published: May 2010

In many dynamic financial models involving latent variables, the exact computation of the likelihood function is impossible. To define simulation‐based estimation methods, three kinds of simulations have been introduced: the unconditional simulations, the sequentially conditional simulations, and the globally conditional simulations. The estimation methods are also of three kinds: those which change the likelihood function into another objective function, like the indirect inference method, those which keep the likelihood function, and the Bayesian methods. The indirect inference method is described in some detail.
Extreme Value Theory
Authors: Deheuvels Paul
Published: May 2010

We describe the limiting behaviour of extreme values generated by a finite sequence (or time‐series) of random variables when the number of observations becomes increasingly large. Under the assumptions that the sequence components are independent and identically distributed, with the proper degree of distributional smoothness, the asymptotic limit laws reduce to a specific class of probability distributions. These are usually named after some of the most prominent scientists who have contributed to this theory, namely Gumbel, Fréchet and Weibull. This fundamental result is very often invoked to fit models to the distributions of extreme values from finite sets of observations, even though the latter do not necessary fulfil the required idealistic assumptions. Such techniques are hazardous, since, even when the conditions of independence and stationarity are satisfied, limiting distributions of extremes may not exist, because of insufficient smoothness of the component distributions. We discuss the statistical treatment of real‐life observations, by stressing the huge practical difficulties of selecting the right extremal models on the basis of small samples. Tail estimation through Hill‐type estimators is introduced as an example of such a statistical methodology relating to extreme values. It illustrates the numerous problems of modeling distributions with Pareto‐type components. Multivariate extremes are also evoked in relation with copulas. Our general conclusion is that, in most cases of interest, the application of extreme‐value theory is rendered highly difficult because of the unavoidable smallness of the available data sets.
Jump Processes
Authors: Eberlein Ernst
Published: May 2010

As a consequence of the distributional deficiencies of the classical models that are driven by Brownian motions, advanced models in finance are now based on processes that include jumps or that consist entirely of jumps. Starting from a semimartingale and Lévy process perspective, we discuss properties of jump processes such as finiteness of moments, finite and infinite activity, and finite and infinite variation. The standard examples of jump processes are discussed. This includes counting processes, compound Poisson processes as well as generalized hyperbolic, stable, and CGMY Lévy processes.
Copulas In Econometrics
Authors: Fan Yanqin
Published: May 2010

In this article, a brief review of copulas and the two areas in which copulas have played important roles, multivariate modeling and the general Fréchet problem, is provided. Although the focus of the article is on bivariate copulas, references on recent advances in constructing higher dimensional copulas are also provided.
Time Change
Authors: Veraart Almut E.D. , Winkel Matthias
Published: May 2010

The mathematical operation of time‐changing continuous‐time stochastic processes can be regarded as a standard method for building financial models. We briefly review the theory on time‐changed stochastic processes and relate them to stochastic volatility models in finance. Popular models, including time‐changed Lévy processes, where the time‐change process is given by a subordinator or an absolutely continuous time change, are presented. Finally, we discuss the potential and the limitations of using such processes for constructing multivariate financial models.
Stochastic Volatility Models: Extremal Behavior
Authors: Klüppelberg Claudia , Lindner A.
Published: May 2010

Stochastic volatility determines, as a rule, the extreme risk in price fluctuations. We review some of the most important stochastic volatility models concerning their extreme behavior. This includes the tail behavior as well as the cluster possibilities of such models. The following pattern is common for discrete‐time and continuous‐time models. In linear models the volatility inherits the tails from the driving noise sequence, and clusters occur basically through the driving noise, whenever it is heavy tailed. In nonlinear models like the GARCH and COGARCH models, heavy tails and clusters in extremes occur from the feedback structure of the underlying random recurrence equation.
Securitization
Authors: Barrieu Pauline
Published: May 2010

Securitization can be seen as any process by which tradable securities are created and introduced into capital markets. Such a process generally involves typically the isolation of a pool of assets and their repackaging into securities. Securitization enables the originator of such a transaction to manage and diversify its risk, to optimize the use of capital, while investors in these new securities are looking for diversification and higher return. Various types of risks have been securitized and different structures have been issued in the capital markets.
ABS
Indices
Authors: Li David X.
Published: May 2010

We provide an overview of the ABX asset backed securities indices, and a short‐lived tranche market, TABX based on the ABX indices. We provide the background on the index, its construction rule and premium leg and loss leg payments. We then briefly touch upon the modeling of the index and its tranches since there is no market consensus on a standard model for the TABX market.
Modigliani–Miller Theorem
Authors: Braouezec Yann
Published: May 2010

The Modigliani–Miller theorem states that the value of a firm is invariant with respect to its leverage policy in an arbitrage‐free market when there is no corporate income tax and no bankruptcy cost: whether the firm is financed through debt or equity, its value remains the same. We present various forms of this result and contrast it with empirical evidence on capital structure of firms.
Multifractals
Authors: Bacry Emmanuel , Muzy Jean‐Fran
Published: May 2010

We present an overview of multifractal models of asset returns. All the proposed models rely upon the notion of random multiplicative cascades. We focus in more details on the simplest of such models namely the log‐normal multifractal random walk. This model can be seen as a stochastic volatility model where the (log‐) volatility has a peculiar long‐range correlated memory. We briefly address calibration issues of such models and their applications to volatility and Value at Risk (VaR) forecasting.
Random Matrix Theory
Authors: El Karoui Noureddine
Published: May 2010

Random matrix theory is concerned with the study of matrices with random entries. It is relevant in the study of portfolio optimization problems, in particular, when the number of assets in the portfolio is of the same order of magnitude as the number of observations taken to compute the correlations between the assets. The article gives an overview of key results concerning eigenvalue properties of large dimensional sample covariance matrices.
Convexity Adjustments
Authors: Gaspar Raquel M. , Murgoci Agatha
Published: May 2010

A convexity adjustment (or convexity correction) in fixed income markets arises when one uses prices of standard (plain vanilla) products plus an
adjustment
to price nonstandard products. We explain the basic and appealing idea behind the use of convexity adjustments and focus on the situations of particular importance to practitioners: yield convexity adjustments, forward
versus
futures convexity adjustments, timing and quanto convexity adjustments. We claim that the appropriate way to look into any of these adjustments is as a side effect of a measure change. This approach bypasses the need for Taylor approximations or unrealistic assumptions.
Algorithmic Trading
Authors: Kolm Petter N. , Maclin Lee
Published: May 2010

In a broad sense, the term
algorithmic trading
is used to describe trading in an automated manner according to a set of rules. It is often used interchangeably with statistical trading or statistical arbitrage, which may or may not be automated, but is based on signals derived from statistical analyses or models. Smart order routing, program trading, and rules‐based trading are some of the other terms associated with algorithmic trading. More recently, the range of functions and activities associated with algorithmic trading has grown to include market impact modeling, execution risk analytics, cost aware portfolio construction, and the use of market microstructure effects. In this article, we first explain the basic ideas of market impact and optimal execution from both the sell‐ and buy‐side perspectives. We then provide an overview of the most popular algorithmic trading strategies.
Ruin Theory
Authors: Asmussen Søren
Published: May 2010

A ruin probability is defined as the probability that some stochastic process
R
enters some rare set (the set of ruin, typically (−∞, 0]) during some time interval, typically [0, ∞) or [0,
T
]. In the setting of the most basic insurance risk models, a survey is given of the behavior of ruin probabilities and how to calculate/approximate them.
Cramér–Lundberg Estimates
Authors: Lin X. Sheldon
Published: May 2010

Cramér–Lundberg estimates are used to estimate ruin probabilities of the surplus in an insurance risk model. Classical results on Cramér–Lundberg estimates and bounds and some recent developments in connection with the expected discounted penalty function are discussed.
Actuarial Premium Principles
Authors: Goovaerts Marc J. , Dhaene Jan , Rachedi Omar
Published: May 2010

An actuarial premium principle is a method for assigning an appropriate price for an insurance policy. Different classes of premium calculation principles emerge from different axiomatic settings. Several premium principles are presented, with an emphasis on the theories of choice under risk underlying them. Two approaches for deriving premium principles are provided: the first is based on Bühlmann's economic principle, while the second is based on a generalized Markov inequality.
Catastrophe Bonds
Authors: Wang Shaun
Published: May 2010

This article discusses catastrophe bonds, or insurance‐linked securities, and pricing of the embedded credit risks using the Wang transform.
Heavy Tails in Insurance
Authors: Asmussen Søren , Fasen Vicky , Klüppelberg Claudia
Published: May 2010

Large insurance losses happen infrequently, but they do happen. In this article, we present the standard distribution models used in fire, wind– storm, or flood insurance and mention some insurance applications.
Insurance Risk Models
Authors: Grandell Jan
Published: May 2010

An insurance risk model consists of three parts: a point process describing the occurrence of the claims, a model for the cost of the claims, and a premium. Here Poisson processes, renewal processes, and Cox processes are discussed. Examples of Cox processes which have been used in risk models are given. Classes of distributions for the cost of the claims mentioned are exponentially bounded distributions or light‐tailed distributions, subexponential distributions or heavy‐tailed distributions, and an intermediate case.
Solvency
Authors: Brender Allan
Published: May 2010

Studies of an insurer's solvency have shifted from an early focus on mathematical ruin theory to the use of computer simulation models. This has been stimulated by the development of increasingly sophisticated regulatory capital requirements and a move to establish consistent international standards for these. The development of advanced approaches for determining capital requirements, based upon an insurer's internal models, has stimulated firms to examine their economic capital requirements and expand their risk management programs, thereby focusing the firm's attention on its continuing solvency.

The business of insurance is essentially stochastic in nature. Many factors affecting an insurer's experience and financial standing, including insurance claims, aspects of policyholder behavior, interest rates, and equity values and rates of return, are best treated as random variables. Insurance companies price their products and provide for claims on the basis of their many years of experience. Pricing and the provision for policy liabilities are usually based upon the expected levels of that experience. To allow for unfavorable variations in future experience from assumed values, specific margins are usually added to the expected rates.

However, experience in any given year may vary considerably from average or expected levels. Premiums and policy liabilities or reserves may become insufficient to guarantee payments to policyholders and to provide for an insurer's continued operation. The insurer's solvency, its ability to meet all of its financial obligations, may be threatened. Since an insurer's fundamental product is its promise to pay benefits when circumstances warrant, it will usually seek to maintain the probability of its continuing solvency at an extremely high level. It is therefore necessary for an insurer to have sufficient financial resources, in addition to the value of its liabilities, available to survive the “rainy day”, that is, to absorb unexpectedly high losses and maintain its solvency. These additional funds are the company's capital.
Gerber–Shiu Function
Authors: Balbas Alejandro , Garrido Jose
Published: May 2010

Risk theory studies
insurance risk models
that describe the uncertainty associated with the claims recorded by an insurance company for the losses incurred by its policy holders. From premium and investment income, insurers set aside funds (surplus) to cover such losses. Ruin theory studies the fluctuations of these surplus processes. Classical problems include ruin (low) and dividend (high) barrier hitting times.

In the last decade, the expected discounted penalty function, proposed by Gerber and Shiu 5, has unified the treatment of the joint distribution of the time to ruin, the surplus just prior to ruin, and the deficit at ruin. This article centers on this expected discounted penalty function, commonly called the
Gerber–Shiu (G–S) function
in the actuarial literature.

The G–S function is somewhat akin to an expected discounted payoff function for financial instruments. A brief description of its general features is given here, together with references that discuss details, generalizations, and applications to insurance and finance.
Phase‐Type Distribution
Authors: Drekic Steve
Published: May 2010

This article summarizes some of the important distributional properties associated with the phase‐type distribution.
Credibility Theory
Authors: Gisler Alois
Published: May 2010

Credibility theory deals with the fundamental concepts ofindividual riskandcollectiveorindividual claims experienceanda priori knowledge.The aim is to assess the individual risks. There are two sources of information on an individual risk: observations of the individual risk on the one hand, anda prioriknowledge, gained from other similar risks in the portfolio (the collective) or from an expert opinion (personal belief), on the other. Credibility theory answers the question of how one should combine the information from these two sources to obtain a best estimate for the individual risk. It is the mathematical tool to describe heterogeneous collectives. Mathematically, it belongs to the area of Bayesian statistics, and credibility estimators are linear Bayes estimators.
Stochastic Control in Insurance
Authors: Taksar Michael
Published: May 2010

We outline the structure of stochastic control problems that appear in insurance models. These are classified according to the type of modeling used, the nature of the controls involved, and the objectives, such as ruin probability minimization or dividend optimization. We describe the procedure of the solution
via
the dynamic programming principle and list the recent advances in this area.
Esscher Transform
Authors: Siu Tak Kuen
Published: May 2010

The Esscher transform is a time‐honored tool in actuarial science. It was first brought to bear in the actuarial community by the seminal work of the Esscher (1930). Since then, it has found many important applications in actuarial science ranging from premium calculation to approximating aggregate loss distributions. The new era of applications of the Esscher transform to quantitative finance started from the pioneering work of Gerber and Shiu (1994) on option valuation by the Esscher transform. Their work paves a way for the cross‐fertilization of two important fields, namely, actuarial valuation and financial pricing, and sheds lights on the applications of the Esscher transform to other important fields in quantitative finance. Nowadays, the Esscher transform has been adopted to solve various important problems in quantitative finance and is getting popular in the quantitative finance community. In this article, we aim at highlighting the applications of this important tool to solve problems arising from three important aspects of quantitative finance, namely, option valuation, asset allocation, and risk management, which are called the three pillars in quantitative finance. We believe that the continuing dialogue between researchers in quantitative finance and actuaries would be beneficial to the healthy development of each of the two fields. With the Esscher transform as a platform, we wish to further stimulate the dialogue of researchers from the two fields.
Large Deviations
Authors: Blanchet Jose H. , Pacheco‐González Carlos G.
Published: May 2010

Modern large deviations theory, pioneered by Donsker and Varadhan, concerns the study of rare events, and it has become a common tool for the analysis of stochastic systems in a variety of scientific disciplines and in engineering. The theory developed by Donsker and Varadhan is a generalization of Laplace's principle and Cramér's theorem. Here we concentrate on applications to finance and risk management.
Water‐Soluble Polyfluorenes as an Interfacial Layer Leading to Cathode‐Independent High Performance of Organic Solar Cells
Authors: Oh Seung‐Hwan , Na Seok‐In , Jo Jang , Lim Bogyu , Vak Doojin , Kim Dong‐Yu
Published: May 2010

Novel poly[(9,9‐bis((6′‐(N,N,N‐trimethylammonium)hexyl)‐2,7‐fluorene)‐alt‐(9,9‐bis(2‐(2‐(2‐methoxyethoxy)ethoxy)ethyl)‐9‐fluorene)) dibromide (WPF‐6‐oxy‐F) and poly[(9,9‐bis((6′‐(N,N,N‐trimethylammonium)hexyl)‐2,7‐fluorene)‐alt‐(9,9‐bis(2‐(2‐methoxyethoxy)ethyl)‐fluorene)] dibromide (WPF‐oxy‐F) compounds are developed and the use of these water‐soluble polymers as an interfacial layer for low‐cost poly(3‐hexylthiophene):phenyl‐C61 butyric acid methyl ester (P3HT:PCBM) organic solar cells (OSCs) is investigated. When WPF‐oxy‐F or WPF‐6‐oxy‐F is simply inserted between the active layer and the cathode as an interfacial dipole layer by spin‐coating water‐soluble polyfluorenes, the open‐circuit voltage (Voc), fill factor (FF), and power‐conversion efficiency (PCE) of photovoltaic cells with high work‐function metal cathodes, such as Al, Ag, Au, and Cu, dramatically increases. For example, when WPF‐6‐oxy‐F is used with Al, Ag, Au, or Cu, regardless of the work‐function of the metal cathode, the Voc is 0.64, 0.64, 0.58, and 0.63 V, respectively, approaching the original value of the P3HT:PCBM system because of the formation of large interfacial dipoles through a reduction of the metal work‐function. In particular, introducing WPF‐6‐oxy‐F into a low‐cost Cu cathode dramatically enhanced the device efficiency from 0.8% to 3.36%.
Highly Efficient p‐i‐n and Tandem Organic Light‐Emitting Devices Using an Air‐Stable and Low‐Temperature‐Evaporable Metal Azide as an n‐Dopant
Authors: Yook Kyoung Soo , Jeon Soon Ok , Min Sung‐Yong , Lee Jun Yeob , Yang Ha‐Jin , Noh Taeyong , Kang Sung‐Kee , Lee Tae‐Woo
Published: May 2010

Cesium azide (CsN3) is employed as a novel n‐dopant because of its air stability and low deposition temperature. CsN3 is easily co‐deposited with the electron transporting materials in an organic molecular beam deposition chamber so that it works well as an n‐dopant in the electron transport layer because its evaporation temperature is similar to that of common organic materials. The driving voltage of the p‐i‐n device with the CsN3‐doped n‐type layer and a MoO3‐doped p‐type layer is greatly reduced, and this device exhibits a very high power efficiency (57 lm W−1). Additionally, an n‐doping mechanism study reveals that CsN3 was decomposed into Cs and N2 during the evaporation. The charge injection mechanism was investigated using transient electroluminescence and capacitance–voltage measurements. A very highly efficient tandem organic light‐emitting diodes (OLED; 84 cd A−1) is also created using an n–p junction that is composed of the CsN3‐doped n‐type organic layer/MoO3 p‐type inorganic layer as the interconnecting unit. This work demonstrates that an air‐stable and low‐temperature‐evaporable inorganic n‐dopant can very effectively enhance the device performance in p‐i‐n and tandem OLEDs, as well as simplify the material handling for the vacuum deposition process.
Mechanical Properties of Cellularly Responsive Hydrogels and Their Experimental Determination
Authors: Kloxin April M. , Kloxin Christopher J. , Bowman Christopher N. , Anseth Kristi S.
Published: May 2010

Hydrogels are increasingly employed as multidimensional cell culture platforms often with a necessity that they respond to or control the cellular environment. Specifically, synthetic hydrogels, such as poly(ethylene glycol) (PEG)‐based gels, are frequently utilized for probing the microenvironment's influence on cell function, as the gel properties can be precisely controlled in space and time. Synthetically tunable parameters, such as monomer structure and concentration, facilitate initial gel property control, while incorporation of responsive degradable units enables cell‐ and/or user‐directed degradation. Such responsive gel systems are complex with dynamic changes occurring over multiple time‐scales, and cells encapsulated in these synthetic hydrogels often experience and dictate local property changes profoundly different from those in the bulk material. Consequently, advances in bulk and local measurement techniques are needed to monitor property evolution quantatively and understand its effect on cell function. Here, recent progress in cell‐responsive PEG hydrogel synthesis and mechanical property characterization is reviewed.
Modeling the effect of chemotaxis on glioblastoma tumor progression
Authors: Vital‐Lopez Francisco G. , Armaou Antonios , Hutnik Michelle , Maranas Costas D.
Published: May 2010

Tumor progression depends on the intricate interplay between biological processes that span the molecular and macroscopic scales. A mathematical agent‐based model is presented to describe the 3‐D (three‐dimensional) progression of a brain tumor type (i.e., glioblastoma multiforme) as the collective behavior of individual tumor cells whose fate is determined by intracellular signaling pathways (i.e., MAPK pathway) that are governed by the temporal‐spatial distribution of key biochemical cues (i.e., growth factors, nutrients). The model is used to investigate how tumor growth and invasiveness depend on the response of migrating tumor cells to chemoattractants. Simulation results suggest that individual cell sensitivity to chemical gradients is necessary to generate in silico tumors with the irregular shape and diffusive tumor‐stroma interface characteristic of glioblastomas. In addition, vascular network damage influences tumor growth and invasiveness. The results quantitatively recapitulate the central role that nutrient availability and signaling proteins have on tumor invasive properties. © 2010 American Institute of Chemical Engineers AIChE J, 2011
Bayesian longitudinal plateau model of adult grip strength
Authors: Nahhas Ramzi W. , Choh Audrey C. , Lee Miryoung , Chumlea William M. Cameron , Duren Dana L. , Siervogel Roger M. , Sherwood Richard J. , Towne Bradford , Czerwinski Stefan A.
Published: May 2010

Objectives:

This article illustrates the use of applied Bayesian statistical methods in modeling the trajectory of adult grip strength and in evaluating potential risk factors that may influence that trajectory.

Methods:

The data consist of from 1 to 11 repeated grip strength measurements from each of 498 men and 533 women age 18–96 years in the Fels Longitudinal Study (Roche AF. 1992. Growth, maturation and body composition: the Fels longitudinal study 1929–1991. Cambridge: Cambridge University Press). In this analysis, the Bayesian framework was particularly useful for fitting a nonlinear mixed effects plateau model with two unknown change points and for the joint modeling of a time‐varying covariate. Multiple imputation (MI) was used to handle missing values with posterior inferences appropriately adjusted to account for between‐imputation variability.

Results:

On average, men and women attain peak grip strength at the same age (36 years), women begin to decline in grip strength sooner (age 50 years for women and 56 years for men), and men lose grip strength at a faster rate relative to their peak; there is an increasing secular trend in peak grip strength that is not attributable to concurrent secular trends in body size, and the grip strength trajectory varies with birth weight (men only), smoking (men only), alcohol consumption (men and women), and sports activity (women only).

Conclusions:

Longitudinal data analysis requires handling not only serial correlation but often also time‐varying covariates, missing data, and unknown change points. Bayesian methods, combined with MI, are useful in handling these issues. Am. J. Hum. Biol. 22:648–656, 2010. © 2010 Wiley‐Liss, Inc.
Chromosome 11p15 duplication in Silver‐Russell syndrome due to a maternally inherited translocation t(11;15)
Authors: Eggermann Thomas , Spengler Sabrina , Bachmann Nadine , Baudis Michael , Mau‐Holzmann Ulrike A. , Singer Sylke , Rossier Eva
Published: May 2010

The role of 11p15 disturbances in the aetiology of Silver‐Russell syndrome (SRS) is well established: in addition to hypomethylation of the H19/IGF2 differentially methylated regions, five patients with a duplication of maternal 11p15 material have been described. We report on a boy with SRS carrying a maternally inherited duplication of chromosome 11p15. The patient showed the typical clinical picture of SRS including severe intrauterine and postnatal growth restriction, relative macrocephaly, a prominent forehead, a triangular face, down‐turned corners of the mouth and fifth digit clinodactyly. Body asymmetry was not observed. By molecular genetic analyses, MLPA and microsatellite typing detected a duplication of chromosome 11p15 and cytogenetic analysis showed an unbalanced translocation t(11;15)(p15.5:p12). The size of the duplicated region is ∼8.8 Mb as determined by SNP‐array analysis. The healthy mother carried a balanced reciprocal chromosome translocation t(11;15). Thus, there is an increased risk for further children with SRS due to 11p15 duplication. Additionally, the family is at risk for offspring with an 11p15 deletion and Beckwith‐Wiedemann syndrome whereby the phenotype will be influenced by haploinsufficiency of additional genes at 11p15 due to the deletion. The balanced aberrant karyotype was identified in several other family members, but interestingly there was no history of recurrent miscarriages, intrauterine fetal death, or multiple congenital anomaly syndromes in the family. © 2010 Wiley‐Liss, Inc.