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Analysis of Financial Time Series, Third Edition

Book Description

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described.

The author begins with basic characteristics of financial time series data before covering three main topics:

  • Analysis and application of univariate financial time series
  • The return series of multiple assets
  • Bayesian inference in finance methods

Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets.

The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

Table of Contents

  1. Cover
  2. Series Page
  3. Title Page
  4. Copyright
  5. Dedication
  6. Preface
  7. Preface to the Second Edition
  8. Preface to the First Edition
  9. Chapter 1: Financial Time Series and Their Characteristics
    1. 1.1 Asset Returns
    2. 1.2 Distributional Properties of Returns
    3. 1.3 Processes Considered
    4. Appendix: R Packages
  10. Chapter 2: Linear Time Series Analysis and Its Applications
    1. 2.1 Stationarity
    2. 2.2 Correlation and Autocorrelation Function
    3. 2.3 White Noise and Linear Time Series
    4. 2.4 Simple AR Models
    5. 2.5 Simple MA Models
    6. 2.6 Simple ARMA Models
    7. 2.7 Unit-Root Nonstationarity
    8. 2.8 Seasonal Models
    9. 2.9 Regression Models with Time Series Errors
    10. 2.10 Consistent Covariance Matrix Estimation
    11. 2.11 Long-Memory Models
    12. Appendix: Some SCA Commands
  11. Chapter 3: Conditional Heteroscedastic Models
    1. 3.1 Characteristics of Volatility
    2. 3.2 Structure of a Model
    3. 3.3 Model Building
    4. 3.4 The ARCH Model
    5. 3.5 The GARCH Model
    6. 3.6 The Integrated GARCH Model
    7. 3.7 The GARCH-M Model
    8. 3.8 The Exponential GARCH Model
    9. 3.9 The Threshold GARCH Model
    10. 3.10 The CHARMA Model
    11. 3.11 Random Coefficient Autoregressive Models
    12. 3.12 Stochastic Volatility Model
    13. 3.13 Long-Memory Stochastic Volatility Model
    14. 3.14 Application
    15. 3.15 Alternative Approaches
    16. 3.16 Kurtosis of GARCH Models
    17. Appendix: Some RATS Programs for Estimating Volatility Models
  12. Chapter 4: Nonlinear Models and Their Applications
    1. 4.1 Nonlinear Models
    2. 4.2 Nonlinearity Tests
    3. 4.3 Modeling
    4. 4.4 Forecasting
    5. 4.5 Application
    6. Appendix A: Some RATS Programs for Nonlinear Volatility Models
    7. Appendix B: R and S-Plus Commands for Neural Network
  13. Chapter 5: High-Frequency Data Analysis and Market Microstructure
    1. 5.1 Nonsynchronous Trading
    2. 5.2 Bid–Ask Spread
    3. 5.3 Empirical Characteristics of Transactions Data
    4. 5.4 Models for Price Changes
    5. 5.5 Duration Models
    6. 5.6 Nonlinear Duration Models
    7. 5.7 Bivariate Models for Price Change and Duration
    8. 5.8 Application
    9. Appendix A: Review of Some Probability Distributions
    10. Appendix B: Hazard Function
    11. Appendix C: Some RATS Programs for Duration Models
  14. Chapter 6: Continuous-Time Models and Their Applications
    1. 6.1 Options
    2. 6.2 Some Continuous-Time Stochastic Processes
    3. 6.3 Ito's Lemma
    4. 6.4 Distributions of Stock Prices and Log Returns
    5. 6.5 Derivation of Black–Scholes Differential Equation
    6. 6.6 Black–Scholes Pricing Formulas
    7. 6.7 Extension of Ito's Lemma
    8. 6.8 Stochastic Integral
    9. 6.9 Jump Diffusion Models
    10. 6.10 Estimation of Continuous-Time Models
    11. Appendix A: Integration of Black–Scholes Formula
    12. Appendix B: Approximation to Standard Normal Probability
  15. Chapter 7: Extreme Values, Quantiles, and Value at Risk
    1. 7.1 Value at Risk
    2. 7.2 RiskMetrics
    3. 7.3 Econometric Approach to VaR Calculation
    4. 7.4 Quantile Estimation
    5. 7.5 Extreme Value Theory
    6. 7.6 Extreme Value Approach to VaR
    7. 7.7 New Approach Based on the Extreme Value Theory
    8. 7.8 The Extremal Index
  16. Chapter 8: Multivariate Time Series Analysis and Its Applications
    1. 8.1 Weak Stationarity and Cross-Correlation Matrices
    2. 8.2 Vector Autoregressive Models
    3. 8.3 Vector Moving-Average Models
    4. 8.4 Vector ARMA Models
    5. 8.5 Unit-Root Nonstationarity and Cointegration
    6. 8.6 Cointegrated VAR Models
    7. 8.7 Threshold Cointegration and Arbitrage
    8. 8.8 Pairs Trading
    9. Appendix A: Review of Vectors and Matrices
    10. Appendix B: Multivariate Normal Distributions
    11. Appendix C: Some SCA Commands
  17. Chapter 9: Principal Component Analysis and Factor Models
    1. 9.1 A Factor Model
    2. 9.2 Macroeconometric Factor Models
    3. 9.3 Fundamental Factor Models
    4. 9.4 Principal Component Analysis
    5. 9.5 Statistical Factor Analysis
    6. 9.6 Asymptotic Principal Component Analysis
  18. Chapter 10: Multivariate Volatility Models and Their Applications
    1. 10.1 Exponentially Weighted Estimate
    2. 10.2 Some Multivariate GARCH Models
    3. 10.3 Reparameterization
    4. 10.4 GARCH Models for Bivariate Returns
    5. 10.5 Higher Dimensional Volatility Models
    6. 10.6 Factor–Volatility Models
    7. 10.7 Application
    8. 10.8 Multivariate t Distribution
    9. 10.9 Appendix: Some Remarks on Estimation
  19. Chapter 11: State-Space Models and Kalman Filter
    1. 11.1 Local Trend Model
    2. 11.2 Linear State-Space Models
    3. 11.3 Model Transformation
    4. 11.4 Kalman Filter and Smoothing
    5. 11.5 Missing Values
    6. 11.6 Forecasting
    7. 11.7 Application
  20. Chapter 12: Markov Chain Monte Carlo Methods with Applications
    1. 12.1 Markov Chain Simulation
    2. 12.2 Gibbs Sampling
    3. 12.3 Bayesian Inference
    4. 12.4 Alternative Algorithms
    5. 12.5 Linear Regression with Time Series Errors
    6. 12.6 Missing Values and Outliers
    7. 12.7 Stochastic Volatility Models
    8. 12.8 New Approach to SV Estimation
    9. 12.9 Markov Switching Models
    10. 12.10 Forecasting
    11. 12.11 Other Applications
  21. Index
  22. both