Analysis of Financial Time Series, 2nd Edition

by Ruey S. Tsay (Univ. of Chicago, IL)

• ISBN13: 9780471690740
• ISBN10: 0471690740
• Edition: 2nd
• Format: Hardcover
• Copyright: 2005-08-01
• Publisher: Wiley-Interscience

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