Robust Bayesian Analysis of Heavy-tailed Stochastic Volatility Models Using Scale Mixtures of Normal Distributions PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Robust Bayesian Analysis of Heavy-tailed Stochastic Volatility Models Using Scale Mixtures of Normal Distributions PDF full book. Access full book title Robust Bayesian Analysis of Heavy-tailed Stochastic Volatility Models Using Scale Mixtures of Normal Distributions by C. A. Abanto-Valle. Download full books in PDF and EPUB format.
Author: C. A. Abanto-Valle
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 42
Get Book Here
Book Description
Author: C. A. Abanto-Valle
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 42
Get Book Here
Book Description
Author: David Insua
Publisher: John Wiley & Sons
ISBN: 1118304039
Category : Mathematics
Languages : en
Pages : 315
Get Book Here
Book Description
Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.
Author: Jayakrishnan Nair
Publisher: Cambridge University Press
ISBN: 1009062964
Category : Mathematics
Languages : en
Pages : 266
Get Book Here
Book Description
Heavy tails –extreme events or values more common than expected –emerge everywhere: the economy, natural events, and social and information networks are just a few examples. Yet after decades of progress, they are still treated as mysterious, surprising, and even controversial, primarily because the necessary mathematical models and statistical methods are not widely known. This book, for the first time, provides a rigorous introduction to heavy-tailed distributions accessible to anyone who knows elementary probability. It tackles and tames the zoo of terminology for models and properties, demystifying topics such as the generalized central limit theorem and regular variation. It tracks the natural emergence of heavy-tailed distributions from a wide variety of general processes, building intuition. And it reveals the controversy surrounding heavy tails to be the result of flawed statistics, then equips readers to identify and estimate with confidence. Over 100 exercises complete this engaging package.
Author: Marc G. Genton
Publisher: CRC Press
ISBN: 0203492005
Category : Mathematics
Languages : en
Pages : 420
Get Book Here
Book Description
This book reviews the state-of-the-art advances in skew-elliptical distributions and provides many new developments in a single volume, collecting theoretical results and applications previously scattered throughout the literature. The main goal of this research area is to develop flexible parametric classes of distributions beyond the classical no
Author: G. S. Maddala
Publisher:
ISBN:
Category : Business & Economics
Languages : en
Pages : 760
Get Book Here
Book Description
A comprehensive reference work for teaching at graduate level and research in empirical finance. The chapters cover a wide range of statistical and probabilistic methods applied to a variety of financial methods and are written by internationally renowned experts.
Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 1116
Get Book Here
Book Description
Author: Robert A. Meyers
Publisher: Springer Science & Business Media
ISBN: 1441977007
Category : Business & Economics
Languages : en
Pages : 919
Get Book Here
Book Description
Finance, Econometrics and System Dynamics presents an overview of the concepts and tools for analyzing complex systems in a wide range of fields. The text integrates complexity with deterministic equations and concepts from real world examples, and appeals to a broad audience.
Author: Michele Leonardo Bianchi
Publisher: World Scientific
ISBN: 9813276215
Category : Business & Economics
Languages : en
Pages : 598
Get Book Here
Book Description
The study of heavy-tailed distributions allows researchers to represent phenomena that occasionally exhibit very large deviations from the mean. The dynamics underlying these phenomena is an interesting theoretical subject, but the study of their statistical properties is in itself a very useful endeavor from the point of view of managing assets and controlling risk. In this book, the authors are primarily concerned with the statistical properties of heavy-tailed distributions and with the processes that exhibit jumps. A detailed overview with a Matlab implementation of heavy-tailed models applied in asset management and risk managements is presented. The book is not intended as a theoretical treatise on probability or statistics, but as a tool to understand the main concepts regarding heavy-tailed random variables and processes as applied to real-world applications in finance. Accordingly, the authors review approaches and methodologies whose realization will be useful for developing new methods for forecasting of financial variables where extreme events are not treated as anomalies, but as intrinsic parts of the economic process.
Author: David Ruppert
Publisher: Springer
ISBN: 1493926144
Category : Business & Economics
Languages : en
Pages : 736
Get Book Here
Book Description
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.
Author: Giovanni Petris
Publisher: Springer Science & Business Media
ISBN: 0387772383
Category : Mathematics
Languages : en
Pages : 258
Get Book Here
Book Description
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
