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Since its introduction in the early 1980s, the risk-neutral valuation principle has proved to be an important tool in the pricing and hedging of financial derivatives. Following the success of the first edition of ‘Risk-Neutral Valuation’, the authors have thoroughly revised the entire book, taking into account recent developments in the field, and changes in their own thinking and teaching. In particular, the chapters on Incomplete Markets and Interest Rate Theory have been updated and extended, there is a new chapter on the important and growing area of Credit Risk and, in recognition of the increasing popularity of Lévy finance, there is considerable new material on: · Infinite divisibility and Lévy processes · Lévy-based models in incomplete markets Further material such as exercises, solutions to exercises and lecture slides are also available via the web to provide additional support for lecturers.

Investments --- Finance --- Investissements --- Finances --- Mathematical models --- Modèles mathématiques --- 51 <081> --- 51 <09> --- Mathematics--Verzameld werk van individuele auteurs --- Mathematics--Geschiedenis van ... --- 51 <09> Mathematics--Geschiedenis van ... --- 51 <081> Mathematics--Verzameld werk van individuele auteurs --- Modèles mathématiques --- Mathematics--Geschiedenis van .. --- Economics, Mathematical . --- Finance. --- Quantitative Finance. --- Finance, general. --- Funding --- Funds --- Economics --- Currency question --- Mathematical economics --- Econometrics --- Mathematics --- Methodology --- Mathematics--Geschiedenis van . --- Mathematics--Geschiedenis van --- Mathématiques financières. --- Processus stochastiques. --- Gestion du risque --- Mathematical models. --- Modèles mathématiques.

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Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential. Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions. The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments. Aimed at 2nd and 3rd year undergraduates studying Statistics, Regression: Linear Models in Statistics requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh's Probability Models, and T. S. Blyth & E.F. Robertsons' Basic Linear Algebra and Further Linear Algebra.

Statistical science --- Mathematics --- Applied physical engineering --- toegepaste wiskunde --- economie --- statistiek --- wiskunde --- statistisch onderzoek --- Regression Analysis

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Regression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential. Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions. The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments. Aimed at 2nd and 3rd year undergraduates studying Statistics, Regression: Linear Models in Statistics requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh’s Probability Models, and T. S. Blyth & E.F. Robertsons’ Basic Linear Algebra and Further Linear Algebra.

Statistical science --- Mathematics --- Applied physical engineering --- toegepaste wiskunde --- economie --- statistiek --- wiskunde --- statistisch onderzoek

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517.51 --- Functions of real variables --- Real variables --- 517.51 Functions of a real variable. Real functions --- Functions of a real variable. Real functions --- Calculus. --- Functions of real variables. --- Calculus --- Functions of complex variables --- Analysis (Mathematics) --- Fluxions (Mathematics) --- Infinitesimal calculus --- Limits (Mathematics) --- Mathematical analysis --- Functions --- Geometry, Infinitesimal --- Functions of several real variables --- Functions of bounded variation --- Fonctions de plusieurs variables réelles --- Fonctions à variation bornée --- Functions of several real variables. --- Functions of bounded variation. --- Fonctions de plusieurs variables réelles --- Fonctions à variation bornée --- Fonctions d'une variable réelle --- Nombres, Théorie des --- Théorèmes taubériens

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This book is a comprehensive account of the theory and applications of regular variation. It is concerned with the asymptotic behaviour of a real function of a real variable x which is 'close' to a power of x. Such functions are much more than a convenient extension of powers. In many limit theorems regular variation is intrinsic to the result, and exactly characterises the limit behaviour. The book emphasises such characterisations, and gives a comprehensive treatment of those applications where regular variation plays an essential (rather then merely convenient) role. The authors rigorously develop the basic ideas of Karamata theory and de Haan theory including many new results and 'second-order' theorems. They go on to discuss the role of regular variation in Abelian, Tauberian, and Mercerian theorems. These results are then applied in analytic number theory, complex analysis, and probability, with the aim above all of setting the theory in context. A widely scattered literature is thus brought together in a unified approach. With several appendices and a comprehensive list of references, analysts, number theorists, and probabilists will find this an invaluable and complete account of regular variation. It will provide a rigorous and authoritative introduction to the subject for research students in these fields.

Functions of real variables. --- Calculus. --- Analysis (Mathematics) --- Fluxions (Mathematics) --- Infinitesimal calculus --- Limits (Mathematics) --- Mathematical analysis --- Functions --- Geometry, Infinitesimal --- Real variables --- Functions of complex variables