Analysis of Financial Time Series, 2nd Edition,9780471690740

Analysis of Financial Time Series, 2nd Edition

by
Edition: 2nd
ISBN13: 9780471690740
ISBN10: 0471690740
Format: Hardcover
Pub. Date: 2005-08-01
Publisher(s): Wiley-Interscience
  • Free Shipping Icon

    This Item Qualifies for Free Shipping!*

    *Excludes marketplace orders.

List Price: $155.40

Rent Textbook

Select for Price
Add to Cart
There was a problem. Please try again later.

New Textbook

We're Sorry
Sold Out

Used Textbook

We're Sorry
Sold Out

eTextbook

We're Sorry
Not Available

Buy from our Marketplace starting at $89.44

How Marketplace Works:

  • This item is offered by an independent seller and not shipped from our warehouse
  • Item details like edition and cover design may differ from our description; see seller's comments before ordering.
  • Sellers much confirm and ship within two business days; otherwise, the order will be cancelled and refunded.
  • Marketplace purchases cannot be returned to eCampus.com. Contact the seller directly for inquiries; if no response within two days, contact customer service.
  • Additional shipping costs apply to Marketplace purchases. Review shipping costs at checkout.

Summary

Gain the statistical tools and techniques you need to understand today's financial markets with the Second Edition of this critically acclaimed book.Youll find a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This edition continues to emphasize empirical financial data and focuses on real-world examples. Youll master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods.This is an ideal textbook for MBA students and a key reference for researchers and professionals in business and finance. Order your copy today.

Author Biography

RUEY S. TSAY, PHD, is H. G. B. Alexander Professor of Econometrics and Statistics, Graduate School of Business, University of Chicago. Dr. Tsay is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics.

Table of Contents

Preface xvii
Preface to First Edition xix
Financial Time Series and Their Characteristics
1(23)
Asset Returns
2(5)
Distributional Properties of Returns
7(13)
Review of Statistical Distributions and Their Moments
7(6)
Distributions of Returns
13(3)
Multivariate Returns
16(1)
Likelihood Function of Returns
17(1)
Empirical Properties of Returns
17(3)
Processes Considered
20(4)
Exercises
22(1)
References
23(1)
Linear Time Series Analysis and Its Applications
24(73)
Stationarity
25(1)
Correlation and Autocorrelation Function
25(6)
White Noise and Linear Time Series
31(1)
Simple Autoregressive Models
32(18)
Properties of AR Models
33(7)
Identifying AR Models in Practice
40(6)
Goodness of Fit
46(1)
Forecasting
47(3)
Simple Moving-Average Models
50(6)
Properties of MA Models
51(1)
Identifying MA Order
52(1)
Estimation
53(1)
Forecasting Using MA Models
54(2)
Simple ARMA Models
56(8)
Properties of ARMA(1,1) Models
57(1)
General ARMA Models
58(1)
Identifying ARMA Models
59(2)
Forecasting Using an ARMA Model
61(1)
Three Model Representations for an ARMA Model
62(2)
Unit-Root Nonstationarity
64(8)
Random Walk
64(1)
Random Walk with Drift
65(2)
Trend-Stationary Time Series
67(1)
General Unit-Root Nonstationary Models
67(1)
Unit-Root Test
68(4)
Seasonal Models
72(8)
Seasonal Differencing
73(2)
Multiplicative Seasonal Models
75(5)
Regression Models with Time Series Errors
80(6)
Consistent Covariance Matrix Estimation
86(3)
Long-Memory Models
89(8)
Appendix: Some SCA Commands
91(2)
Exercises
93(3)
References
96(1)
Conditional Heteroscedastic Models
97(57)
Characteristics of Volatility
98(1)
Structure of a Model
99(2)
Model Building
101(1)
Testing for ARCH Effect
101(1)
The ARCH Model
102(11)
Properties of ARCH Models
104(2)
Weaknesses of ARCH Models
106(1)
Building an ARCH Model
106(3)
Some Examples
109(4)
The GARCH Model
113(9)
An Illustrative Example
116(5)
Forecasting Evaluation
121(1)
A Two-Pass Estimation Method
121(1)
The Integrated GARCH Model
122(1)
The GARCH-M Model
123(1)
The Exponential GARCH Model
124(6)
An Alternative Model Form
125(1)
An Illustrative Example
126(1)
Second Example
126(2)
Forecasting Using an EGARCH Model
128(2)
The Threshold GARCH Model
130(1)
The CHARMA Model
131(2)
Effects of Explanatory Variables
133(1)
Random Coefficient Autoregressive Models
133(1)
The Stochastic Volatility Model
134(1)
The Long-Memory Stochastic Volatility Model
134(2)
Application
136(4)
Alternative Approaches
140(5)
Use of High-Frequency Data
140(3)
Use of Daily Open, High, Low, and Close Prices
143(2)
Kurtosis of GARCH Models
145(9)
Appendix: Some RATS Programs for Estimating Volatility Models
147(1)
Exercises
148(3)
References
151(3)
Nonlinear Models and Their Applications
154(52)
Nonlinear Models
156(27)
Bilinear Model
156(1)
Threshold Autoregressive (TAR) Model
157(6)
Smooth Transition AR (STAR) Model
163(1)
Markov Switching Model
164(3)
Nonparametric Methods
167(8)
Functional Coefficient AR Model
175(1)
Nonlinear Additive AR Model
176(1)
Nonlinear State-Space Model
176(1)
Neural Networks
177(6)
Nonlinearity Tests
183(8)
Nonparametric Tests
183(3)
Parametric Tests
186(4)
Applications
190(1)
Modeling
191(1)
Forecasting
192(2)
Parametric Bootstrap
192(1)
Forecasting Evaluation
192(2)
Application
194(12)
Appendix A: Some RATS Programs for Nonlinear Volatility Models
199(1)
Appendix B: S-Plus Commands for Neural Network
200(1)
Exercises
200(2)
References
202(4)
High-Frequency Data Analysis and Market Microstructure
206(45)
Nonsynchronous Trading
207(3)
Bid--Ask Spread
210(2)
Empirical Characteristics of Transactions Data
212(6)
Models for Price Changes
218(7)
Ordered Probit Model
218(3)
A Decomposition Model
221(4)
Duration Models
225(11)
The ACD Model
227(2)
Simulation
229(3)
Estimation
232(4)
Nonlinear Duration Models
236(1)
Bivariate Models for Price Change and Duration
237(14)
Appendix A: Review of Some Probability Distributions
242(3)
Appendix B: Hazard Function
245(1)
Appendix C: Some RATS Programs for Duration Models
246(2)
Exercises
248(2)
References
250(1)
Continuous-Time Models and Their Applications
251(36)
Options
252(1)
Some Continuous-Time Stochastic Processes
252(4)
The Wiener Process
253(2)
Generalized Wiener Processes
255(1)
Ito Processes
256(1)
Ito's Lemma
256(5)
Review of Differentiation
256(1)
Stochastic Differentiation
257(1)
An Application
258(1)
Estimation of μ and σ
259(2)
Distributions of Stock Prices and Log Returns
261(1)
Derivation of Black--Scholes Differential Equation
262(2)
Black--Scholes Pricing Formulas
264(8)
Risk-Neutral World
264(1)
Formulas
264(3)
Lower Bounds of European Options
267(1)
Discussion
268(4)
An Extension of Ito's Lemma
272(1)
Stochastic Integral
273(1)
Jump Diffusion Models
274(8)
Option Pricing Under Jump Diffusion
279(3)
Estimation of Continuous-Time Models
282(5)
Appendix A: Integration of Black--Scholes Formula
282(2)
Appendix B: Approximation to Standard Normal Probability
284(1)
Exercises
284(1)
References
285(2)
Extreme Values, Quantile Estimation, and Value at Risk
287(52)
Value at Risk
287(3)
RiskMetrics
290(4)
Discussion
293(1)
Multiple Positions
293(1)
An Econometric Approach to VaR Calculation
294(4)
Multiple Periods
296(2)
Quantile Estimation
298(3)
Quantile and Order Statistics
299(1)
Quantile Regression
300(1)
Extreme Value Theory
301(10)
Review of Extreme Value Theory
301(3)
Empirical Estimation
304(3)
Application to Stock Returns
307(4)
Extreme Value Approach to VaR
311(7)
Discussion
314(2)
Multiperiod VaR
316(1)
VaR for a Short Position
316(1)
Return Level
317(1)
A New Approach Based on the Extreme Value Theory
318(21)
Statistical Theory
318(2)
Mean Excess Function
320(2)
A New Approach to Modeling Extreme Values
322(2)
VaR Calculation Based on the New Approach
324(1)
An Alternative Parameterization
325(3)
Use of Explanatory Variables
328(1)
Model Checking
329(1)
An Illustration
330(5)
Exercises
335(2)
References
337(2)
Multivariate Time Series Analysis and Its Applications
339(66)
Weak Stationarity and Cross-Correlation Matrices
340(9)
Cross-Correlation Matrices
340(1)
Linear Dependence
341(1)
Sample Cross-Correlation Matrices
342(4)
Multivariate Portmanteau Tests
346(3)
Vector Autoregressive Models
349(16)
Reduced and Structural Forms
349(2)
Stationarity Condition and Moments of a VAR(1) Model
351(2)
Vector AR(p) Models
353(1)
Building a VAR(p) Model
354(8)
Impulse Response Function
362(3)
Vector Moving-Average Models
365(6)
Vector ARMA Models
371(5)
Marginal Models of Components
375(1)
Unit-Root Nonstationarity and Cointegration
376(4)
An Error-Correction Form
379(1)
Cointegrated VAR Models
380(10)
Specification of the Deterministic Function
382(1)
Maximum Likelihood Estimation
383(1)
A Cointegration Test
384(1)
Forecasting of Cointegrated VAR Models
385(1)
An Example
385(5)
Threshold Cointegration and Arbitrage
390(15)
Multivariate Threshold Model
391(1)
The Data
392(1)
Estimation
393(2)
Appendix A: Review of Vectors and Matrices
395(4)
Appendix B: Multivariate Normal Distributions
399(1)
Appendix C: Some SCA Commands
400(1)
Exercises
401(1)
References
402(3)
Principal Component Analysis and Factor Models
405(38)
A Factor Model
406(1)
Macroeconometric Factor Models
407(7)
A Single-Factor Model
408(4)
Multifactor Models
412(2)
Fundamental Factor Models
414(7)
BARRA Factor Model
414(6)
Fama--French Approach
420(1)
Principal Component Analysis
421(5)
Theory of PCA
421(1)
Empirical PCA
422(4)
Statistical Factor Analysis
426(10)
Estimation
428(1)
Factor Rotation
429(1)
Applications
430(6)
Asymptotic Principal Component Analysis
436(7)
Selecting the Number of Factors
437(1)
An Example
437(3)
Exercises
440(1)
References
441(2)
Multivariate Volatility Models and Their Applications
443(47)
Exponentially Weighted Estimate
444(3)
Some Multivariate GARCH Models
447(7)
Diagonal VEC Model
447(4)
BEKK Model
451(3)
Reparameterization
454(5)
Use of Correlations
454(1)
Cholesky Decomposition
455(4)
GARCH Models for Bivariate Returns
459(12)
Constant-Correlation Models
459(5)
Time-Varying Correlation Models
464(6)
Some Recent Developments
470(1)
Higher Dimensional Volatility Models
471(6)
Factor--Volatility Models
477(3)
Application
480(2)
Multivariate t Distribution
482(8)
Appendix: Some Remarks on Estimation
483(5)
Exercises
488(1)
References
489(1)
State-Space Models and Kalman Filter
490(53)
Local Trend Model
490(18)
Statistical Inference
493(2)
Kalman Filter
495(1)
Properties of Forecast Error
496(2)
State Smoothing
498(3)
Missing Values
501(2)
Effect of Initialization
503(1)
Estimation
504(1)
S-Plus Commands Used
505(3)
Linear State-Space Models
508(1)
Model Transformation
509(14)
CAPM with Time-Varying Coefficients
510(2)
ARMA Models
512(6)
Linear Regression Model
518(1)
Linear Regression Models with ARMA Errors
519(2)
Scalar Unobserved Component Model
521(2)
Kalman Filter and Smoothing
523(8)
Kalman Filter
523(2)
State Estimation Error and Forecast Error
525(1)
State Smoothing
526(2)
Disturbance Smoothing
528(3)
Missing Values
531(1)
Forecasting
532(1)
Application
533(10)
Exercises
540(1)
References
541(2)
Markov Chain Monte Carlo Methods with Applications
543(58)
Markov Chain Simulation
544(1)
Gibbs Sampling
545(2)
Bayesian Inference
547(4)
Posterior Distributions
547(1)
Conjugate Prior Distributions
548(3)
Alternative Algorithms
551(2)
Metropolis Algorithm
551(1)
Metropolis--Hasting Algorithm
552(1)
Griddy Gibbs
552(1)
Linear Regression with Time Series Errors
553(5)
Missing Values and Outliers
558(7)
Missing Values
559(2)
Outlier Detection
561(4)
Stochastic Volatility Models
565(13)
Estimation of Univariate Models
566(5)
Multivariate Stochastic Volatility Models
571(7)
A New Approach to SV Estimation
578(10)
Markov Switching Models
588(6)
Forecasting
594(3)
Other Applications
597(4)
Exercises
597(1)
References
598(3)
Index 601

An electronic version of this book is available through VitalSource.

This book is viewable on PC, Mac, iPhone, iPad, iPod Touch, and most smartphones.

By purchasing, you will be able to view this book online, as well as download it, for the chosen number of days.

Digital License

You are licensing a digital product for a set duration. Durations are set forth in the product description, with "Lifetime" typically meaning five (5) years of online access and permanent download to a supported device. All licenses are non-transferable.

More details can be found here.

A downloadable version of this book is available through the eCampus Reader or compatible Adobe readers.

Applications are available on iOS, Android, PC, Mac, and Windows Mobile platforms.

Please view the compatibility matrix prior to purchase.