financial derivatives in theory and practice wiley series in probability and statistics

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Financial Derivatives In Theory And Practice

Author : Philip Hunt
ISBN : 9780470863602
Genre : Mathematics
File Size : 77. 72 MB
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The term Financial Derivative is a very broad term which has come to mean any financial transaction whose value depends on the underlying value of the asset concerned. Sophisticated statistical modelling of derivatives enables practitioners in the banking industry to reduce financial risk and ultimately increase profits made from these transactions. The book originally published in March 2000 to widespread acclaim. This revised edition has been updated with minor corrections and new references, and now includes a chapter of exercises and solutions, enabling use as a course text. Comprehensive introduction to the theory and practice of financial derivatives. Discusses and elaborates on the theory of interest rate derivatives, an area of increasing interest. Divided into two self-contained parts ? the first concentrating on the theory of stochastic calculus, and the second describes in detail the pricing of a number of different derivatives in practice. Written by well respected academics with experience in the banking industry. A valuable text for practitioners in research departments of all banking and finance sectors. Academic researchers and graduate students working in mathematical finance.

Financial Derivatives In Theory And Practice

Author : P. J. Hunt
ISBN : STANFORD:36105025229654
Genre : Mathematics
File Size : 79. 47 MB
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This book brings together in one volume both a complete, rigorous and yet readable account of the mathematics underlying derivative pricing and a guide to applying these ideas to solve real pricing problems. It is aimed at practitioners and researchers who wish to understand the latest finance literature and develop their own pricing models. The authors' combination of strong theoretical knowledge and extensive market experience make this book particularly relevant for those interested in real world applications of mathematical finance. Features: detailed coverage of interest rate derivatives, from 'vanilla' instruments through to many of the more exotic products currently being traded overview of popular term structure models along with their relationships to each other (including Heath-Jarrow-Morton, short rate models and the latest market models) explanation of numeraires as a modelling and pricing tool pricing models for constant maturity swaps and other convexity products models and efficient algorithms for path-dependent and Bermudan swaptions insights into how to go about pricing products beyond those treated in the text accessible yet rigorous treatment of the stochastic calculus required for option pricing

Planning Construction And Statistical Analysis Of Comparative Experiments

Author : Francis G. Giesbrecht
ISBN : 0471213950
Genre : Mathematics
File Size : 36. 87 MB
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This text provides a balanced treatment between mathematical necessities and appropriate applications. The theoretical basis compliments the complete documentation of experimental design includingafield theory, number theory and linear algebra.

Models For Probability And Statistical Inference

Author : James H. Stapleton
ISBN : 0470183403
Genre : Mathematics
File Size : 44. 27 MB
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This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readers Models for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping. Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses modes of convergence of sequences of random variables, with special attention to convergence in distribution. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Further areas of exploration include: distributions defined in terms of the multivariate normal, chi-square, t, and F (central and non-central); the one- and two-sample Wilcoxon test, together with methods of estimation based on both; linear models with a linear space-projection approach; and logistic regression. Each section contains a set of problems ranging in difficulty from simple to more complex, and selected answers as well as proofs to almost all statements are provided. An abundant amount of figures in addition to helpful simulations and graphs produced by the statistical package S-Plus(r) are included to help build the intuition of readers.

Modes Of Parametric Statistical Inference

Author : Seymour Geisser
ISBN : 9780471743125
Genre : Mathematics
File Size : 85. 73 MB
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A fascinating investigation into the foundations of statisticalinference This publication examines the distinct philosophical foundations ofdifferent statistical modes of parametric inference. Unlike manyother texts that focus on methodology and applications, this bookfocuses on a rather unique combination of theoretical andfoundational aspects that underlie the field of statisticalinference. Readers gain a deeper understanding of the evolution andunderlying logic of each mode as well as each mode's strengths andweaknesses. The book begins with fascinating highlights from the history ofstatistical inference. Readers are given historical examples ofstatistical reasoning used to address practical problems that arosethroughout the centuries. Next, the book goes on to scrutinize fourmajor modes of statistical inference: * Frequentist * Likelihood * Fiducial * Bayesian The author provides readers with specific examples andcounterexamples of situations and datasets where the modes yieldboth similar and dissimilar results, including a violation of thelikelihood principle in which Bayesian and likelihood methodsdiffer from frequentist methods. Each example is followed by adetailed discussion of why the results may have varied from onemode to another, helping the reader to gain a greater understandingof each mode and how it works. Moreover, the author providesconsiderable mathematical detail on certain points to highlight keyaspects of theoretical development. The author's writing style and use of examples make the text clearand engaging. This book is fundamental reading for graduate-levelstudents in statistics as well as anyone with an interest in thefoundations of statistics and the principles underlying statisticalinference, including students in mathematics and the philosophy ofscience. Readers with a background in theoretical statistics willfind the text both accessible and absorbing.

Time Series

Author : Ngai Hang Chan
ISBN : 9781118030714
Genre : Mathematics
File Size : 89. 36 MB
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A new edition of the comprehensive, hands-on guide to financialtime series, now featuring S-Plus® and R software Time Series: Applications to Finance with R and S-Plus®,Second Edition is designed to present an in-depth introduction tothe conceptual underpinnings and modern ideas of time seriesanalysis. Utilizing interesting, real-world applications and thelatest software packages, this book successfully helps readersgrasp the technical and conceptual manner of the topic in order togain a deeper understanding of the ever-changing dynamics of thefinancial world. With balanced coverage of both theory and applications, thisSecond Edition includes new content to accurately reflect thecurrent state-of-the-art nature of financial time series analysis.A new chapter on Markov Chain Monte Carlo presents Bayesian methodsfor time series with coverage of Metropolis-Hastings algorithm,Gibbs sampling, and a case study that explores the relevance ofthese techniques for understanding activity in the Dow JonesIndustrial Average. The author also supplies a new presentation ofstatistical arbitrage that includes discussion of pairs trading andcointegration. In addition to standard topics such as forecastingand spectral analysis, real-world financial examples are used toillustrate recent developments in nonstandard techniques,including: Nonstationarity Heteroscedasticity Multivariate time series State space modeling and stochastic volatility Multivariate GARCH Cointegration and common trends The book's succinct and focused organization allows readers tograsp the important ideas of time series. All examples aresystematically illustrated with S-Plus® and R software,highlighting the relevance of time series in financialapplications. End-of-chapter exercises and selected solutions allowreaders to test their comprehension of the presented material, anda related Web site features additional data sets. Time Series: Applications to Finance with R and S-Plus® isan excellent book for courses on financial time series at theupper-undergraduate and beginning graduate levels. It also servesas an indispensible resource for practitioners working withfinancial data in the fields of statistics, economics, business,and risk management.

Operational Risk

Author : Harry H. Panjer
ISBN : 9780470051306
Genre : Business & Economics
File Size : 82. 15 MB
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Discover how to optimize business strategies from both qualitative and quantitative points of view Operational Risk: Modeling Analytics is organized around the principle that the analysis of operational risk consists, in part, of the collection of data and the building of mathematical models to describe risk. This book is designed to provide risk analysts with a framework of the mathematical models and methods used in the measurement and modeling of operational risk in both the banking and insurance sectors. Beginning with a foundation for operational risk modeling and a focus on the modeling process, the book flows logically to discussion of probabilistic tools for operational risk modeling and statistical methods for calibrating models of operational risk. Exercises are included in chapters involving numerical computations for students' practice and reinforcement of concepts. Written by Harry Panjer, one of the foremost authorities in the world on risk modeling and its effects in business management, this is the first comprehensive book dedicated to the quantitative assessment of operational risk using the tools of probability, statistics, and actuarial science. In addition to providing great detail of the many probabilistic and statistical methods used in operational risk, this book features: * Ample exercises to further elucidate the concepts in the text * Definitive coverage of distribution functions and related concepts * Models for the size of losses * Models for frequency of loss * Aggregate loss modeling * Extreme value modeling * Dependency modeling using copulas * Statistical methods in model selection and calibration Assuming no previous expertise in either operational risk terminology or in mathematical statistics, the text is designed for beginning graduate-level courses on risk and operational management or enterprise risk management. This book is also useful as a reference for practitioners in both enterprise risk management and risk and operational management.

Time Series Analysis And Forecasting By Example

Author : Søren Bisgaard
ISBN : 1118056957
Genre : Mathematics
File Size : 81. 97 MB
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An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including: Graphical tools in time series analysis Procedures for developing stationary, non-stationary, and seasonal models How to choose the best time series model Constant term and cancellation of terms in ARIMA models Forecasting using transfer function-noise models The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS®, JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets. With its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. it also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.

Longitudinal Data Analysis

Author : Donald Hedeker
ISBN : 9780470036471
Genre : Mathematics
File Size : 21. 91 MB
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Longitudinal data analysis for biomedical and behavioral sciences This innovative book sets forth and describes methods for the analysis of longitudinaldata, emphasizing applications to problems in the biomedical and behavioral sciences. Reflecting the growing importance and use of longitudinal data across many areas of research, the text is designed to help users of statistics better analyze and understand this type of data. Much of the material from the book grew out of a course taught by Dr. Hedeker on longitudinal data analysis. The material is, therefore, thoroughly classroom tested and includes a number of features designed to help readers better understand and apply the material. Statistical procedures featured within the text include: * Repeated measures analysis of variance * Multivariate analysis of variance for repeated measures * Random-effects regression models (RRM) * Covariance-pattern models * Generalized-estimating equations (GEE) models * Generalizations of RRM and GEE for categorical outcomes Practical in their approach, the authors emphasize the applications of the methods, using real-world examples for illustration. Some syntax examples are provided, although the authors do not generally focus on software in this book. Several datasets and computer syntax examples are posted on this title's companion Web site. The authors intend to keep the syntax examples current as new versions of the software programs emerge. This text is designed for both undergraduate and graduate courses in longitudinal data analysis. Instructors can take advantage of overheads and additional course materials available online for adopters. Applied statisticians in biomedicine and the social sciences can also use the book as a convenient reference.

Regression Analysis By Example

Author : Samprit Chatterjee
ISBN : 9780470055458
Genre : Mathematics
File Size : 61. 47 MB
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The essentials of regression analysis through practical applications Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgement. Regression Analysis by Example, Fourth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. This new edition features the following enhancements: Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis A new chapter entitled Further Topics discusses advanced areas of regression analysis Reorganized, expanded, and upgraded exercises appear at the end of each chapter A fully integrated Web page provides data sets Numerous graphical displays highlight the significance of visual appeal Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

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