Hedge Relationship (finance)
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Hedge Relationship (finance)
In Accounting, a Hedge relationship refers to the treatment of an insurance contract for risk mitigation on an underlying asset, and the set of tests for the valuation of this insurer/insuree contract. More specifically, the accounting term "Hedge relationship" describes the criteria for including the fair value of derivatives on balance sheet as part of an effort to regulate and normalize the use of hedging in corporate accounting. These contracts are valuable to a company and standardized means of including their fair value on corporate balance sheets is of interest to lenders and investors. In general, the use of hedges and financial derivatives to protect against risk should reflect a fair value assessment of the hedge and should not appear as items in corporate income. For companies operating outside of the financial services sector an effective hedge should protect against undue loss without being a major component of company income statements. To account for the value ...
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Insurance Contract
In insurance, the insurance policy is a contract (generally a standard form contract) between the insurer and the policyholder, which determines the claims which the insurer is legally required to pay. In exchange for an initial payment, known as the premium, the insurer promises to pay for loss caused by perils covered under the policy language. Insurance contracts are designed to meet specific needs and thus have many features not found in many other types of contracts. Since insurance policies are standard forms, they feature boilerplate language which is similar across a wide variety of different types of insurance policies. Available through HeinOnline. The insurance policy is generally an integrated contract, meaning that it includes all forms associated with the agreement between the insured and insurer.Wollner KS. (1999). How to Draft and Interpret Insurance Policies. Casualty Risk Publishing LLC. In some cases, however, supplementary writings such as letters sent after ...
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Monte Carlo Methods In Finance
Monte Carlo methods are used in corporate finance and mathematical finance to value and analyze (complex) instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes. This is usually done by help of stochastic asset models. The advantage of Monte Carlo methods over other techniques increases as the dimensions (sources of uncertainty) of the problem increase. Monte Carlo methods were first introduced to finance in 1964 by David B. Hertz through his ''Harvard Business Review'' article, discussing their application in Corporate Finance. In 1977, Phelim Boyle pioneered the use of simulation in derivative valuation in his seminal ''Journal of Financial Economics'' paper. This article discusses typical financial problems in which Monte Carlo methods are used. It also touches on the use of so-called "quasi-random" methods such as the use of Sobo ...
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Derivatives (finance)
The derivative of a function is the rate of change of the function's output relative to its input value. Derivative may also refer to: In mathematics and economics *Brzozowski derivative in the theory of formal languages *Formal derivative, an operation on elements of a polynomial ring which mimics the form of the derivative from calculus * Radon–Nikodym derivative in measure theory *Derivative (set theory), a concept applicable to normal functions *Derivative (graph theory), an alternative term for a line graph deva *Derivative (finance), a contract whose value is derived from that of other quantities *Derivative suit or derivative action, a type of lawsuit filed by shareholders of a corporation In science and engineering *Derivative (chemistry), a type of compound which is a product of the process of derivatization *Derivative (linguistics), the process of forming a new word on the basis of an existing word, e.g. happiness and unhappy from happy * Aeroderivative gas turbine, ...
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Standard Deviation
In statistics, the standard deviation is a measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the values are spread out over a wider range. Standard deviation may be abbreviated SD, and is most commonly represented in mathematical texts and equations by the lower case Greek letter σ (sigma), for the population standard deviation, or the Latin letter '' s'', for the sample standard deviation. The standard deviation of a random variable, sample, statistical population, data set, or probability distribution is the square root of its variance. It is algebraically simpler, though in practice less robust, than the average absolute deviation. A useful property of the standard deviation is that, unlike the variance, it is expressed in the same unit as the data. The standard deviation of a popu ...
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Andrew Kalotay
Andrew Kalotay (born 1941) is a Hungarian-born finance professor, Wall Street quant and chess master. He is best known as an authority on fixed income valuation and institutional debt management. He is currently the President of Andrew Kalotay Associates, and an adjunct professor at Polytechnic Institute of New York University. His innovations include the concept of refunding efficiency — a widely used tool for managing callable debt, the ratchet bond — a surrogate for conventional callable bonds, and the volatility reduction measure — for testing hedge effectiveness. Kalotay has also made numerous contributions to the quantitative analysis of option-adjusted spread (OAS), interest rate derivatives, and mortgage-backed securities (MBS); he is an author of the Kalotay–Williams–Fabozzi model. In 1997, he was inducted into the Fixed Income Analysts Society's Hall of Fame. Kalotay emigrated to Canada following the 1956 Hungarian Revolution. He graduated fro ...
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R Squared
In statistics, the coefficient of determination, denoted ''R''2 or ''r''2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). It is a statistic used in the context of statistical models whose main purpose is either the prediction of future outcomes or the testing of hypotheses, on the basis of other related information. It provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model. There are several definitions of ''R''2 that are only sometimes equivalent. One class of such cases includes that of simple linear regression where ''r''2 is used instead of ''R''2. When only an intercept is included, then ''r''2 is simply the square of the sample correlation coefficient (i.e., ''r'') between the observed outcomes and the observed predictor values. If additional regressors are included, ''R''2 is ...
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