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.
Preface.Preface to the Second Edition.
Preface to the First Edition.
1 Financial Time Series and Their Characteristics.
1.1 Asset Returns.
1.2 Distributional Properties of Returns.
1.3 Processes Considered.
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.
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.
4 Nonlinear Models and Their Applications.
4.1 Nonlinear Models.
4.2 Nonlinearity Tests.
4.3 Modeling.
4.4 Forecasting.
4.5 Application.
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.
55.6 Nonlinear Duration Models.
5.7 Bivariate Models for Price Change and Duration.
5.8 Application.
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.
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.
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.
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.
99.5 Statistical Factor Analysis.
9.6 Asymptotic Principal Component Analysis.
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.
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.
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.
Other versions by this Author