By Ruey S. Tsay
Offers statistical instruments and methods had to comprehend trendy monetary markets the second one variation of this significantly acclaimed textual content offers a finished and systematic advent to monetary econometric types and their purposes in modeling and predicting monetary time sequence facts. This newest version maintains to stress empirical monetary facts and specializes in real-world examples. Following this method, readers will grasp key facets of monetary time sequence, together with volatility modeling, neural community purposes, industry microstructure and high-frequency monetary information, continuous-time versions and Ito's Lemma, price in danger, a number of returns research, monetary issue types, and econometric modeling through computation-intensive equipment. the writer starts off with the fundamental features of economic time sequence info, environment the root for the 3 major issues: research and alertness of univariate monetary time sequence go back sequence of a number of resources Bayesian inference in finance tools This new version is a completely revised and up-to-date textual content, together with the addition of S-Plus® instructions and illustrations. workouts were completely up to date and improved and comprise the most up-tp-date information, supplying readers with extra possibilities to place the versions and strategies into perform. one of the new fabric additional to the textual content, readers will locate: constant covariance estimation less than heteroscedasticity and serial correlation replacement methods to volatility modeling monetary issue versions State-space versions Kalman filtering Estimation of stochastic diffusion versions The instruments supplied during this textual content reduction readers in constructing a deeper knowing of monetary markets via firsthand adventure in operating with monetary information. this can be a fantastic textbook for MBA scholars in addition to a reference for researchers and execs in company and finance.
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Additional info for Analysis of Financial Time Series (Wiley Series in Probability and Statistics)2nd edition
From Eq. 4), we have which is referred to as the present value of an asset that is worth A dollars n years from now, assuming that the continuously compounded interest rate is r per annum. 1. 10517 a The time interval is 1 year and the interest rate is 10% per annum. Page 5 Continuously Compounded Return The natural logarithm of the simple gross return of an asset is called the continuously compounded return or log return: where pt = ln(Pt). Continuously compounded returns rt enjoy some advantages over the simple net returns Rt.
Empirical examples are used to demonstrate the applications. The state-space model and Kalman filter are added to demonstrate their applicability in finance and ease in computation. They are used in Chapter 12 to estimate stochastic volatility models under the general Markov chain Monte Carlo (MCMC) framework. The estimation also uses the technique of forward filtering and backward sampling to gain computational efficiency. A brief summary of the added material in the second edition is: 1. To update the data used throughout the book.
M. Smith, Jozef L. Teugels Editors Emeriti: Vic Barnett, J. Stuart Hunter, David G. Kendall A complete list of the titles in this series appears at the end of this volume. Page iii Analysis of Financial Time Series Second Edition RUEY S. TSAY University of Chicago Graduate School of Business Page iv Copyright © 2005 by John Wiley Sons, Inc. All rights reserved. , Hoboken, New Jersey. Published simultaneously in Canada. com. com/go/permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose.