Home Page Icon
Home Page
Table of Contents for
Series Page
Close
Series Page
by RUEY S. TSAY
Analysis of Financial Time Series, Third Edition
Cover
Series Page
Title Page
Copyright
Dedication
Preface
Preface to the Second Edition
Preface to the First Edition
Chapter 1: Financial Time Series and Their Characteristics
1.1 Asset Returns
1.2 Distributional Properties of Returns
1.3 Processes Considered
Appendix: R Packages
Chapter 2: Linear Time Series Analysis and Its Applications
2.1 Stationarity
2.2 Correlation and Autocorrelation Function
2.3 White Noise and Linear Time Series
2.4 Simple AR Models
2.5 Simple MA Models
2.6 Simple ARMA Models
2.7 Unit-Root Nonstationarity
2.8 Seasonal Models
2.9 Regression Models with Time Series Errors
2.10 Consistent Covariance Matrix Estimation
2.11 Long-Memory Models
Appendix: Some SCA Commands
Chapter 3: Conditional Heteroscedastic Models
3.1 Characteristics of Volatility
3.2 Structure of a Model
3.3 Model Building
3.4 The ARCH Model
3.5 The GARCH Model
3.6 The Integrated GARCH Model
3.7 The GARCH-M Model
3.8 The Exponential GARCH Model
3.9 The Threshold GARCH Model
3.10 The CHARMA Model
3.11 Random Coefficient Autoregressive Models
3.12 Stochastic Volatility Model
3.13 Long-Memory Stochastic Volatility Model
3.14 Application
3.15 Alternative Approaches
3.16 Kurtosis of GARCH Models
Appendix: Some RATS Programs for Estimating Volatility Models
Chapter 4: Nonlinear Models and Their Applications
4.1 Nonlinear Models
4.2 Nonlinearity Tests
4.3 Modeling
4.4 Forecasting
4.5 Application
Appendix A: Some RATS Programs for Nonlinear Volatility Models
Appendix B: R and S-Plus Commands for Neural Network
Chapter 5: High-Frequency Data Analysis and Market Microstructure
5.1 Nonsynchronous Trading
5.2 Bid–Ask Spread
5.3 Empirical Characteristics of Transactions Data
5.4 Models for Price Changes
5.5 Duration Models
5.6 Nonlinear Duration Models
5.7 Bivariate Models for Price Change and Duration
5.8 Application
Appendix A: Review of Some Probability Distributions
Appendix B: Hazard Function
Appendix C: Some RATS Programs for Duration Models
Chapter 6: Continuous-Time Models and Their Applications
6.1 Options
6.2 Some Continuous-Time Stochastic Processes
6.3 Ito's Lemma
6.4 Distributions of Stock Prices and Log Returns
6.5 Derivation of Black–Scholes Differential Equation
6.6 Black–Scholes Pricing Formulas
6.7 Extension of Ito's Lemma
6.8 Stochastic Integral
6.9 Jump Diffusion Models
6.10 Estimation of Continuous-Time Models
Appendix A: Integration of Black–Scholes Formula
Appendix B: Approximation to Standard Normal Probability
Chapter 7: Extreme Values, Quantiles, and Value at Risk
7.1 Value at Risk
7.2 RiskMetrics
7.3 Econometric Approach to VaR Calculation
7.4 Quantile Estimation
7.5 Extreme Value Theory
7.6 Extreme Value Approach to VaR
7.7 New Approach Based on the Extreme Value Theory
7.8 The Extremal Index
Chapter 8: Multivariate Time Series Analysis and Its Applications
8.1 Weak Stationarity and Cross-Correlation Matrices
8.2 Vector Autoregressive Models
8.3 Vector Moving-Average Models
8.4 Vector ARMA Models
8.5 Unit-Root Nonstationarity and Cointegration
8.6 Cointegrated VAR Models
8.7 Threshold Cointegration and Arbitrage
8.8 Pairs Trading
Appendix A: Review of Vectors and Matrices
Appendix B: Multivariate Normal Distributions
Appendix C: Some SCA Commands
Chapter 9: Principal Component Analysis and Factor Models
9.1 A Factor Model
9.2 Macroeconometric Factor Models
9.3 Fundamental Factor Models
9.4 Principal Component Analysis
9.5 Statistical Factor Analysis
9.6 Asymptotic Principal Component Analysis
Chapter 10: Multivariate Volatility Models and Their Applications
10.1 Exponentially Weighted Estimate
10.2 Some Multivariate GARCH Models
10.3 Reparameterization
10.4 GARCH Models for Bivariate Returns
10.5 Higher Dimensional Volatility Models
10.6 Factor–Volatility Models
10.7 Application
10.8 Multivariate t Distribution
10.9 Appendix: Some Remarks on Estimation
Chapter 11: State-Space Models and Kalman Filter
11.1 Local Trend Model
11.2 Linear State-Space Models
11.3 Model Transformation
11.4 Kalman Filter and Smoothing
11.5 Missing Values
11.6 Forecasting
11.7 Application
Chapter 12: Markov Chain Monte Carlo Methods with Applications
12.1 Markov Chain Simulation
12.2 Gibbs Sampling
12.3 Bayesian Inference
12.4 Alternative Algorithms
12.5 Linear Regression with Time Series Errors
12.6 Missing Values and Outliers
12.7 Stochastic Volatility Models
12.8 New Approach to SV Estimation
12.9 Markov Switching Models
12.10 Forecasting
12.11 Other Applications
Index
Search in book...
Toggle Font Controls
Playlists
Add To
Create new playlist
Name your new playlist
Playlist description (optional)
Cancel
Create playlist
Sign In
Email address
Password
Forgot Password?
Create account
Login
or
Continue with Facebook
Continue with Google
Sign Up
Full Name
Email address
Confirm Email Address
Password
Login
Create account
or
Continue with Facebook
Continue with Google
Prev
Previous Chapter
Cover
Next
Next Chapter
Title Page
Add Highlight
No Comment
..................Content has been hidden....................
You can't read the all page of ebook, please click
here
login for view all page.
Day Mode
Cloud Mode
Night Mode
Reset