GARCH Models. Structure, Statistical Inference and Financial Applications

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.

The appropriate, defensible way to prepare and present business valuations, with a strong emphasis on applications and models Financial Valuation provides an all-encompassing valuation tome that presents the application of financial valuation theory in an easily understood manner. Delivering valuation theory, the consensus view on application, and the tools to make it happen, James Hitchner?s all-star cast of contributors offer numerous examples, checklists, and models to assist in navigating a valuation project. The book contains hundreds of short, easily understandable «Valuation Tips,» and covers best practices from the view of these twenty-five experts.

A practically-focused resource for business valuation professionals Financial Valuation: Applications and Models provides authoritative reference and practical guidance on the appropriate, defensible way to prepare and present business valuations. With contributions by 30 top experts in the field, this new fourth edition provides an essential resource for those seeking the most up-to-date guidance, with a strong emphasis on applications and models. Coverage includes state-of-the-art methods for the valuation of closely-held businesses, nonpublic entities, intangible, and other assets, with comprehensive discussion on valuation theory, a consensus view on application, and the tools to make it happen. Packed with examples, checklists, and models to help you navigate your valuation project, this book also provides hundreds of expert «tips» and best practices in clear, easy-to-follow language. The companion website provides access to extensive appendix materials, and the perspectives of valuation thought-leaders add critical insight throughout each step of the process. Valuation is an important part of any organization's overall financial strategy, and seemingly-small inaccuracies or incomplete assessments can have big repercussions. This book walks you through the valuation process to give you the skills and understanding you need to get it done right. Learn best practices from 30 field-leading experts Follow clear examples for complex or unfamiliar scenarios Access practical tools that streamline the valuation process Understand valuation models and real-world applications The business valuation process can become very complex very quickly, and there's no substitute for clear guidance and a delineated framework in the run-up to completion. Get organized from the beginning, and be systematic and methodical every step of the way. Financial Valuation: Applications and Models is the all-encompassing, expert guide to business valuation projects.

Praise for Financial Valuation «This Second Edition addresses virtually all of the recent hot topics in business valuation, and there are many of them since the first edition. Most chapters are updated with new material, including, especially, the Duff & Phelps Risk Premium Report as an alternative to Ibbotson's risk premium data. As with the first edition, the authors are very well-known and provide incisive analysis.» –Shannon Pratt, CFA, FASA, MCBA, CM&AA, CEO, Shannon Pratt Valuations, LLC «Though the first edition of Mr. Hitchner's book was excellent in all regards, this Second Edition squarely puts Hitchner and his team of authors at the top of the list of authorities in the field of business valuation. Few publications on the subject even come close to the book's thorough coverage of the topic, but equally impressive is the clarity with which Hitchner depicts and explains highly complex subject matters. So impressed with Financial Valuation Applications and Models, the National Association of Certified Valuation Analysts has developed a three-day course based upon this book entitled, 'Advanced Valuation and Case Study Workshop,' which is now a cornerstone training program for our organization.» –Parnell Black, MBA, CPA, CVA, Chief Executive Officer, NACVA «This book is a valuable resource for every BV library. It has material not covered in other BV books and this Second Edition has much more information than the first. Financial Valuation Applications and Models is the primary textbook for AICPA's business valuation education and it covers most of the topics on the test for the AICPA's Accredited in Business Valuation (ABV) credential. Its thirty authors are nationally respected practitioners who have written this book for practitioners. Many of the authors are current or former members of the AICPA Business Valuation Committee and the AICPA BV Hall of Fame.» –Michael A. Crain, CPA/ABV, ASA, CFA, CFE, Chair, AICPA Business Valuation Committee, Managing Director, The Financial Valuation Group «This book has a tremendous wealth of information that all valuation analysts must have in their libraries. From those just starting their careers to the most experienced practitioner, all valuation analysts will benefit from the invaluable information, ranging from fundamental practices to the most innovative economic and valuation ideas of today.» –Scott R. Saltzman, CPA, CVA, ASA, DABFA, Managing Member, Saltzman LLC; President, National Association of Certified Valuation Analysts Coauthors: Mel H. Abraham, R. James Alerding, Terry Jacoby Allen, Larry R. Cook, Michael A. Crain, Don M. Drysdale, Robert E. Duffy, Edward J. Dupke, Nancy J. Fannon, John R. Gilbert, Chris Hamilton, Thomas E. Hilton, James R. Hitchner, Steven D. Hyden, Gregory S. Koonsman, Mark G. Kucik, Eva M. Lang, Derald L. Lyons, Michael J. Mard, Harold G. Martin Jr., Michael Mattson, Edward F. Moran Jr., Raymond E. Moran, James S. Rigby Jr., Ronald L. Seigneur, Robin E. Taylor, Linda B. Trugman, Samuel Y. Wessinger, Don Wisehart, and Kevin R. Yeanoplos

A multidisciplinary approach that emphasizes learning by analyzing real-world data sets This book is the result of the authors' hands-on classroom experience and is tailored to reflect how students best learn to analyze linear relationships. The text begins with the introduction of four simple examples of actual data sets. These examples are developed and analyzed throughout the text, and more complicated examples of data sets are introduced along the way. Taking a multidisciplinary approach, the book traces the conclusion of the analyses of data sets taken from geology, biology, economics, psychology, education, sociology, and environmental science. As students learn to analyze the data sets, they master increasingly sophisticated linear modeling techniques, including: * Simple linear models * Multivariate models * Model building * Analysis of variance (ANOVA) * Analysis of covariance (ANCOVA) * Logistic regression * Total least squares The basics of statistical analysis are developed and emphasized, particularly in testing the assumptions and drawing inferences from linear models. Exercises are included at the end of each chapter to test students' skills before moving on to more advanced techniques and models. These exercises are marked to indicate whether calculus, linear algebra, or computer skills are needed. Unlike other texts in the field, the mathematics underlying the models is carefully explained and accessible to students who may not have any background in calculus or linear algebra. Most chapters include an optional final section on linear algebra for students interested in developing a deeper understanding. The many data sets that appear in the text are available on the book's Web site. The MINITAB(r) software program is used to illustrate many of the examples. For students unfamiliar with MINITAB(r), an appendix introduces the key features needed to study linear models. With its multidisciplinary approach and use of real-world data sets that bring the subject alive, this is an excellent introduction to linear models for students in any of the natural or social sciences.

Mathematical finance has grown into a huge area of research which requires a lot of care and a large number of sophisticated mathematical tools. Mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, it considers various aspects of the application of statistical methods in finance and illustrates some of the many ways that statistical tools are used in financial applications. Financial Statistics and Mathematical Finance: Provides an introduction to the basics of financial statistics and mathematical finance. Explains the use and importance of statistical methods in econometrics and financial engineering. Illustrates the importance of derivatives and calculus to aid understanding in methods and results. Looks at advanced topics such as martingale theory, stochastic processes and stochastic integration. Features examples throughout to illustrate applications in mathematical and statistical finance. Is supported by an accompanying website featuring R code and data sets. Financial Statistics and Mathematical Finance introduces the financial methodology and the relevant mathematical tools in a style that is both mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, both graduate students and researchers in statistics, finance, econometrics and business administration will benefit from this book.

Updated classic statistics text, with new problems and examples [/i][i]Probability and Statistical Inference, Third Edition helps students grasp essential concepts of statistics and its probabilistic foundations. This book focuses on the development of intuition and understanding in the subject through a wealth of examples illustrating concepts, theorems, and methods. The reader will recognize and fully understand the why and not just the how behind the introduced material. In this Third Edition, the reader will find a new chapter on Bayesian statistics, 70 new problems and an appendix with the supporting R code. This book is suitable for upper-level undergraduates or first-year graduate students studying statistics or related disciplines, such as mathematics or engineering. This Third Edition: Introduces an all-new chapter on Bayesian statistics and offers thorough explanations of advanced statistics and probability topics Includes 650 problems and over 400 examples – an excellent resource for the mathematical statistics class sequence in the increasingly popular «flipped classroom» format Offers students in statistics, mathematics, engineering and related fields a user-friendly resource Provides practicing professionals valuable insight into statistical tools  Probability and Statistical Inference  offers a unique approach to problems that allows the reader to fully integrate the knowledge gained from the text, thus, enhancing a more complete and honest understanding of the topic.

Autoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection. Key Features: Presents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique. Assumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value-at-risk, option pricing and model evaluation. Uses empirical examples to demonstrate how the recent developments in ARCH can be implemented. Provides step-by-step instructive examples, using econometric software, such as Econometric Views and the G@RCH module for the Ox software package, used in Estimating and Forecasting ARCH Models. Accompanied by a CD-ROM containing links to the software as well as the datasets used in the examples. Aimed at readers wishing to gain an aptitude in the applications of financial econometric modelling with a focus on practical implementation, via applications to real data and via examples worked with econometrics packages.

This book summarizes the results of various models under normal theory with a brief review of the literature. Statistical Inference for Models with Multivariate t-Distributed Errors: Includes a wide array of applications for the analysis of multivariate observations Emphasizes the development of linear statistical models with applications to engineering, the physical sciences, and mathematics Contains an up-to-date bibliography featuring the latest trends and advances in the field to provide a collective source for research on the topic Addresses linear regression models with non-normal errors with practical real-world examples Uniquely addresses regression models in Student's t-distributed errors and t-models Supplemented with an Instructor's Solutions Manual, which is available via written request by the Publisher

An up-to-date approach to understanding statistical inference Statistical inference is finding useful applications in numerous fields, from sociology and econometrics to biostatistics. This volume enables professionals in these and related fields to master the concepts of statistical inference under inequality constraints and to apply the theory to problems in a variety of areas. Constrained Statistical Inference: Order, Inequality, and Shape Constraints provides a unified and up-to-date treatment of the methodology. It clearly illustrates concepts with practical examples from a variety of fields, focusing on sociology, econometrics, and biostatistics. The authors also discuss a broad range of other inequality-constrained inference problems that do not fit well in the contemplated unified framework, providing a meaningful way for readers to comprehend methodological resolutions. Chapter coverage includes: Population means and isotonic regression Inequality-constrained tests on normal means Tests in general parametric models Likelihood and alternatives Analysis of categorical data Inference on monotone density function, unimodal density function, shape constraints, and DMRL functions Bayesian perspectives, including Stein’s Paradox, shrinkage estimation, and decision theory