[下载][分享]Financial Econometrics---Analysis of Financial time series   [推广有奖]

在论坛里好久了，下了不少好东东，非常感谢大家

 第一次发帖，不知这本书对大家是否有用，金融时间序列分析，金融计量方面的，但还是把她放在金融板块了，不知是否合理。

本来想免费给大家，可小弟我在这里混迹了将近半年，才有26金钱（截至发帖）有很多资料买不起。

我这里还有一些资料，会慢慢上传，还希望大家支持。

以下是书中的内容，包括版权，目录，前言

Analysis of Financial
Time Series
Financial Econometrics

RUEY S. TSAY
University of Chicago
A

This book is printed on acid-free paper. ∞
Copyright
c 2002 by John Wiley & Sons, Inc. All rights reserved.
Published simultaneously in Canada.
No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form
or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as
permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without either the prior
written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to
the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978)
750-4744. Requests to the Publisher for permission should be addressed to the Permissions Department,
John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, (212) 850-6011, fax (212)
850-6008. E-Mail: PERMREQ@WILEY.COM.
For ordering and customer service, call 1-800-CALL-WILEY.
Library of Congress Cataloging-in-Publication Data
Tsay, Ruey S., 1951–
Analysis of financial time series / Ruey S. Tsay.
p. cm. —(Wiley series in probability and statistics. Financial engineering section)
“A Wiley-Interscience publication.”
Includes bibliographical references and index.
ISBN 0-471-41544-8 (cloth : alk. paper)
1. Time-series analysis. 2. Econometrics. 3. Risk management. I. Title. II. Series.
HA30.3 T76 2001
332.015195—dc21 2001026944
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1

Contents
Preface xi
1. Financial Time Series and Their Characteristics 1
1.1 Asset Returns, 2
1.2 Distributional Properties of Returns, 6
1.3 Processes Considered, 17
2. Linear Time Series Analysis and Its Applications 22
2.1 Stationarity, 23
2.2 Correlation and Autocorrelation Function, 23
2.3 White Noise and Linear Time Series, 26
2.4 Simple Autoregressive Models, 28
2.5 Simple Moving-Average Models, 42
2.6 Simple ARMA Models, 48
2.7 Unit-Root Nonstationarity, 56
2.8 Seasonal Models, 61
2.9 Regression Models with Time Series Errors, 66
2.10 Long-Memory Models, 72
Appendix A. Some SCA Commands, 74
3. Conditional Heteroscedastic Models 79
3.1 Characteristics of Volatility, 80
3.2 Structure of a Model, 81
3.3 The ARCH Model, 82
3.4 The GARCH Model, 93
3.5 The Integrated GARCH Model, 100
3.6 The GARCH-M Model, 101
3.7 The Exponential GARCH Model, 102
vii
viii CONTENTS
3.8 The CHARMA Model, 107
3.9 Random Coefficient Autoregressive Models, 109
3.10 The Stochastic Volatility Model, 110
3.11 The Long-Memory Stochastic Volatility Model, 110
3.12 An Alternative Approach, 112
3.13 Application, 114
3.14 Kurtosis of GARCH Models, 118
Appendix A. Some RATS Programs for Estimating Volatility
Models, 120
4. Nonlinear Models and Their Applications 126
4.1 Nonlinear Models, 128
4.2 Nonlinearity Tests, 152
4.3 Modeling, 161
4.4 Forecasting, 161
4.5 Application, 164
Appendix A. Some RATS Programs for Nonlinear Volatility
Models, 168
Appendix B. S-Plus Commands for Neural Network, 169
5. High-Frequency Data Analysis and Market Microstructure 175
5.1 Nonsynchronous Trading, 176
5.2 Bid-Ask Spread, 179
5.3 Empirical Characteristics of Transactions Data, 181
5.4 Models for Price Changes, 187
5.5 Duration Models, 194
5.6 Nonlinear Duration Models, 206
5.7 Bivariate Models for Price Change and Duration, 207
Appendix A. Review of Some Probability Distributions, 212
Appendix B. Hazard Function, 215
Appendix C. Some RATS Programs for Duration Models, 216
6. Continuous-Time Models and Their Applications 221
6.1 Options, 222
6.2 Some Continuous-Time Stochastic Processes, 222
6.3 Ito’s Lemma, 226
6.4 Distributions of Stock Prices and Log Returns, 231
6.5 Derivation of Black–Scholes Differential Equation, 232
CONTENTS ix
6.6 Black–Scholes Pricing Formulas, 234
6.7 An Extension of Ito’s Lemma, 240
6.8 Stochastic Integral, 242
6.9 Jump Diffusion Models, 244
6.10 Estimation of Continuous-Time Models, 251
Appendix A. Integration of Black–Scholes Formula, 251
Appendix B. Approximation to Standard Normal Probability, 253
7. Extreme Values, Quantile Estimation, and Value at Risk 256
7.1 Value at Risk, 256
7.2 RiskMetrics, 259
7.3 An Econometric Approach to VaR Calculation, 262
7.4 Quantile Estimation, 267
7.5 Extreme Value Theory, 270
7.6 An Extreme Value Approach to VaR, 279
7.7 A New Approach Based on the Extreme Value Theory, 284
8. Multivariate Time Series Analysis and Its Applications 299
8.1 Weak Stationarity and Cross-Correlation Matrixes, 300
8.2 Vector Autoregressive Models, 309
8.3 Vector Moving-Average Models, 318
8.4 Vector ARMA Models, 322
8.5 Unit-Root Nonstationarity and Co-Integration, 328
8.6 Threshold Co-Integration and Arbitrage, 332
8.7 Principal Component Analysis, 335
8.8 Factor Analysis, 341
Appendix A. Review of Vectors and Matrixes, 348
Appendix B. Multivariate Normal Distributions, 353
9. Multivariate Volatility Models and Their Applications 357
9.1 Reparameterization, 358
9.2 GARCH Models for Bivariate Returns, 363
9.3 Higher Dimensional Volatility Models, 376
9.4 Factor-Volatility Models, 383
9.5 Application, 385
9.6 Multivariate t Distribution, 387
Appendix A. Some Remarks on Estimation, 388
x CONTENTS
10. Markov Chain Monte Carlo Methods with Applications 395
10.1 Markov Chain Simulation, 396
10.2 Gibbs Sampling, 397
10.3 Bayesian Inference, 399
10.4 Alternative Algorithms, 403
10.5 Linear Regression with Time-Series Errors, 406
10.6 Missing Values and Outliers, 410
10.7 Stochastic Volatility Models, 418
10.8 Markov Switching Models, 429
10.9 Forecasting, 438
10.10 Other Applications, 441
Index 445

Preface
This book grew out of an MBA course in analysis of financial time series that I have
been teaching at the University of Chicago since 1999. It also covers materials of
Ph.D. courses in time series analysis that I taught over the years. It is an introductory
book intended to provide a comprehensive and systematic account of financial
econometric models and their application to modeling and prediction of financial
time series data. The goals are to learn basic characteristics of financial data, understand
the application of financial econometric models, and gain experience in analyzing
financial time series.
The book will be useful as a text of time series analysis for MBA students with
finance concentration or senior undergraduate and graduate students in business, economics,
mathematics, and statistics who are interested in financial econometrics. The
book is also a useful reference for researchers and practitioners in business, finance,
and insurance facing Value at Risk calculation, volatility modeling, and analysis of
serially correlated data.
The distinctive features of this book include the combination of recent developments
in financial econometrics in the econometric and statistical literature. The
developments discussed include the timely topics of Value at Risk (VaR), highfrequency
data analysis, and Markov Chain Monte Carlo (MCMC) methods. In particular,
the book covers some recent results that are yet to appear in academic journals;
see Chapter 6 on derivative pricing using jump diffusion with closed-form formulas,
Chapter 7 on Value at Risk calculation using extreme value theory based on
a nonhomogeneous two-dimensional Poisson process, and Chapter 9 on multivariate
volatility models with time-varying correlations.MCMC methods are introduced
because they are powerful and widely applicable in financial econometrics. These
methods will be used extensively in the future.
Another distinctive feature of this book is the emphasis on real examples and data
analysis. Real financial data are used throughout the book to demonstrate applications
of the models and methods discussed. The analysis is carried out by using several
computer packages; the SCA (the Scientific Computing Associates) for building
linear time series models, the RATS (Regression Analysis for Time Series) for
estimating volatility models, and the S-Plus for implementing neural networks and
obtaining postscript plots. Some commands required to run these packages are given
xi
xii PREFACE
in appendixes of appropriate chapters. In particular, complicated RATS programs
used to estimate multivariate volatility models are shown in Appendix A of Chapter
9. Some fortran programs written by myself and others are used to price simple
options, estimate extreme value models, calculate VaR, and to carry out Bayesian
analysis. Some data sets and programs are accessible from the World Wide Web at
http://www.gsb.uchicago.edu/fac/ruey.tsay/teaching/fts.
The book begins with some basic characteristics of financial time series data in
Chapter 1. The other chapters are divided into three parts. The first part, consisting
of Chapters 2 to 7, focuses on analysis and application of univariate financial time
series. The second part of the book covers Chapters 8 and 9 and is concerned with
the return series of multiple assets. The final part of the book is Chapter 10, which
introduces Bayesian inference in finance via MCMC methods.
A knowledge of basic statistical concepts is needed to fully understand the book.
Throughout the chapters, I have provided a brief review of the necessary statistical
concepts when they first appear. Even so, a prerequisite in statistics or business statistics
that includes probability distributions and linear regression analysis is highly
recommended. A knowledge in finance will be helpful in understanding the applications
discussed throughout the book. However, readers with advanced background in
econometrics and statistics can find interesting and challenging topics in many areas
of the book.
An MBA course may consist of Chapters 2 and 3 as a core component, followed
by some nonlinear methods (e.g., the neural network of Chapter 4 and the applications
discussed in Chapters 5-7 and 10). Readers who are interested in Bayesian
inference may start with the first five sections of Chapter 10.
Research in financial time series evolves rapidly and new results continue to
appear regularly. Although I have attempted to provide broad coverage, there are
many subjects that I do not cover or can only mention in passing.
I sincerely thank my teacher and dear friend, George C. Tiao, for his guidance,
encouragement and deep conviction regarding statistical applications over the
years. I am grateful to Steve Quigley, Heather Haselkorn, Leslie Galen, Danielle
LaCourciere, and Amy Hendrickson for making the publication of this book possible,
to Richard Smith for sending me the estimation program of extreme value
theory, to Bonnie K. Ray for helpful comments on several chapters, to Steve Kou
for sending me his preprint on jump diffusion models, to Robert E. McCulloch for
many years of collaboration on MCMC methods, to many students of my courses in
analysis of financial time series for their feedback and inputs, and to Jeffrey Russell
and Michael Zhang for insightful discussions concerning analysis of high-frequency
financial data. To all these wonderful people I owe a deep sense of gratitude. I
am also grateful to the support of the Graduate School of Business, University of
Chicago and the National Science Foundation. Finally, my heart goes to my wife,
Teresa, for her continuous support, encouragement, and understanding, to Julie,
Richard, and Vicki for bringing me joys and inspirations; and to my parents for their
love and care.
R. S. T.
Chicago, Illinois

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