Sunday, May 16, 2010

Ebook Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi

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Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi

Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi


Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi


Ebook Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi

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Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi

Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management—since these are the areas in finance where Bayesian methods have had the greatest penetration to date.

  • Sales Rank: #1496527 in Books
  • Published on: 2008-02-08
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.30" h x 1.20" w x 6.40" l, 1.21 pounds
  • Binding: Hardcover
  • 329 pages

From the Inside Flap
Recent years have seen an impressive growth in the variety and complexity of quantitative models and modeling techniques used in finance, particularly in portfolio and risk management. While criticisms of the excessive reliance on quantitative models resurface with each turmoil in the financial markets, the focus should be on employing techniques such that the likelihood of extreme events as well as the uncertainty of the decision-making environment are properly accounted for. Bayesian methods, coupled with heavy-tailed distributional assumptions, provide one theoretically sound avenue to achieve this goal.

Together with the ability to incorporate inform-ation from different sources and tackle complex estimation problems, dealing with estimation uncertainty has been a driving factor behind the increased popularity of Bayesian methods among academics and practitioners alike.

The aim of Bayesian Methods in Finance is to provide an overview of the theory of Bayesian methods and explain their real-world applications to financial modeling. While the principles and concepts explained in the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management, since these are the areas in finance where Bayesian methods have had the greatest penetration to date.

Bayesian Methods in Finance offers both students of finance and practitioners an invaluable resource in the form of a previously unavailable, highly accessible, unified look at the use of the Bayesian methodology—as well as numerical computational methods—in financial models and asset management.

From the Back Cover

An accessible overview of the theory and practice of Bayesian Methods in Finance

This first-of-its-kind book explains and illustrates the fundamentals of the Bayesian methodology and their applications to finance in clear and accessible terms.

Bayesian Methods in Finance provides a unified examination of the use of Bayesian theory and practice in portfolio and risk management—explaining the concepts and techniques that can be applied to real-world financial problems.

This book is a guide to using Bayesian methods and, notably, the Markov Chain Monte Carlo toolbox to: incorporate prior views of an analyst or a fund manager into the asset allocation process; estimate and predict volatility; improve risk forecasts; and combine the conclusions of different models. Each application presentation begins with the basics, works through the traditional "frequentist" perspective, and then follows with the Bayesian treatment.

This invaluable resource presents a theoretically sound framework for combining various sources of information and a robust estimation setting that explicitly incorporates estimation risk, and brings within reach the flexibility to handle complex and realistic models.

About the Author

Svetlozar T. Rachev, PhD, Doctor of Science, is Chair-Professor at the University of Karlsruhe in the School of Economics and Business Engineering; Professor Emeritus at the University of California, Santa Barbara; and Chief-Scientist of FinAnalytica Inc.

John S. J. Hsu, PhD, is Professor of Statistics and Applied Probability at the University of California, Santa Barbara.

Biliana S. Bagasheva, PhD, has research interests in the areas of risk management, portfolio construction, Bayesian methods, and financial econometrics. Currently, she is a consultant in London.

Frank J. Fabozzi, PhD, CFA, is Professor in the Practice of Finance and Becton Fellow at Yale University's School of Management and the Editor of the Journal of Portfolio Management.

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Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi PDF

Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi PDF
Bayesian Methods in FinanceBy Svetlozar T. Rachev, John S. J. Hsu, Biliana S. Bagasheva, Frank J. Fabozzi PDF

Monday, May 3, 2010

PDF Ebook Elementary Functional Analysis (Dover Books on Mathematics)By Georgi E. Shilov

PDF Ebook Elementary Functional Analysis (Dover Books on Mathematics)By Georgi E. Shilov

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Elementary Functional Analysis (Dover Books on Mathematics)By Georgi E. Shilov

Elementary Functional Analysis (Dover Books on Mathematics)By Georgi E. Shilov


Elementary Functional Analysis (Dover Books on Mathematics)By Georgi E. Shilov


PDF Ebook Elementary Functional Analysis (Dover Books on Mathematics)By Georgi E. Shilov

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Elementary Functional Analysis (Dover Books on Mathematics)By Georgi E. Shilov

In this introductory work on mathematical analysis, the noted mathematician Georgi E. Shilov begins with an extensive and important chapter on the basic structures of mathematical analysis: linear spaces, metric spaces, normed linear spaces, Hilbert spaces, and normed algebras. The standard models for all these spaces are sets of functions (hence the term "functional analysis"), rather than sets of points in a finite-dimensional space.
Chapter 2 is devoted to differential equations, and contains the basic theorems on existence and uniqueness of solutions of ordinary differential equations for functions taking values in a Banach space. The solution of the linear equation with constant (operator) coefficients is written in general form in terms of the exponential of the operator. This leads, in the finite-dimensional case, to explicit formulas not only for the solutions of first-order equations, but also to the solutions of higher-order equations and systems of equations. The third chapter presents a theory of curvature for curve in a multidimensional space.
The final two chapters essentially comprise an introduction to Fourier analysis. In the treatment of orthogonal expansions, a key role is played by Fourier series and the various kinds of convergence and summability for such series. The material on Fourier transforms, in addition to presenting the more familiar theory, also deals with problems in the complex domain, in particular with problems involving the Laplace transform.
Designed for students at the upper-undergraduate or graduate level, the text includes a set of problems for each chapter, with hints and answers at the end of the book.

  • Sales Rank: #1262201 in Books
  • Published on: 1996-01-18
  • Released on: 1996-01-18
  • Original language: English
  • Number of items: 1
  • Dimensions: 8.44" h x .67" w x 5.37" l, .70 pounds
  • Binding: Paperback
  • 352 pages

Language Notes
Text: English (translation)
Original Language: Russian

About the Author
Soviet mathematician Georgi E. Shilov (1917-75) was a longtime Professor of Mathematics at Moscow State University and the author of several other successful Dover reprints.

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