Isbn: 9780471920830 - bayesian methods in finance (frank j. fabozzi series) (9 Ergebnisse)

Zustand: Acceptable. Item in acceptable condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. pp. xviii + 329 Illus.

Zustand: New. In English.

Zustand: Very Good.

Hardcover. Zustand: Brand New. 1st edition. 329 pages. 9.00x6.50x1.00 inches. In Stock.

Zustand: New. Bayesian Methods in Finance explains and illustrates the foundations of the Bayesian methodology in clear and accessible terms. It provides a unified examination of the use of the Bayesian theory and practice to analyze and evaluate asset management. Series: Frank J. Fabozzi Series. Num Pages: 330 pages, Illustrations. BIC Classification: KFF. Category: (P) Professional & Vocational. Dimension: 154 x 231 x 33. Weight in Grams: 572. . 2008. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. …

Gebunden. Zustand: New. 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.Jo.

Buch. Zustand: Neu. Neuware - An accessible overview of the theory and practice of Bayesian Methods in FinanceThis 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.…