Efnisyfirlit

• Analysis of Financial Time Series
• Contents
• 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
    • 1.2.1 Review of Statistical Distributions and Their Moments
    • 1.2.2 Distributions of Returns
    • 1.2.3 Multivariate Returns
    • 1.2.4 Likelihood Function of Returns
    • 1.2.5 Empirical Properties of Returns
  • 1.3 Processes Considered
  • Appendix: R Packages
  • Exercises
  • References
• 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
    • 2.4.1 Properties of AR Models
    • 2.4.2 Identifying AR Models in Practice
    • 2.4.3 Goodness of Fit
    • 2.4.4 Forecasting
  • 2.5 Simple MA Models
    • 2.5.1 Properties of MA Models
    • 2.5.2 Identifying MA Order
    • 2.5.3 Estimation
    • 2.5.4 Forecasting Using MA Models
  • 2.6 Simple ARMA Models
    • 2.6.1 Properties of ARMA(1,1) Models
    • 2.6.2 General ARMA Models
    • 2.6.3 Identifying ARMA Models
    • 2.6.4 Forecasting Using an ARMA Model
    • 2.6.5 Three Model Representations for an ARMA Model
  • 2.7 Unit-Root Nonstationarity
    • 2.7.1 Random Walk
    • 2.7.2 Random Walk with Drift
    • 2.7.3 Trend-Stationary Time Series
    • 2.7.4 General Unit-Root Nonstationary Models
    • 2.7.5 Unit-Root Test
  • 2.8 Seasonal Models
    • 2.8.1 Seasonal Differencing
    • 2.8.2 Multiplicative 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
  • Exercises
  • References
• 3 Conditional Heteroscedastic Models
  • 3.1 Characteristics of Volatility
  • 3.2 Structure of a Model
  • 3.3 Model Building
    • 3.3.1 Testing for ARCH Effect
  • 3.4 The ARCH Model
    • 3.4.1 Properties of ARCH Models
    • 3.4.2 Weaknesses of ARCH Models
    • 3.4.3 Building an ARCH Model
    • 3.4.4 Some Examples
  • 3.5 The GARCH Model
    • 3.5.1 An Illustrative Example
    • 3.5.2 Forecasting Evaluation
    • 3.5.3 A Two-Pass Estimation Method
  • 3.6 The Integrated GARCH Model
  • 3.7 The GARCH-M Model
  • 3.8 The Exponential GARCH Model
    • 3.8.1 Alternative Model Form
    • 3.8.2 Illustrative Example
    • 3.8.3 Second Example
    • 3.8.4 Forecasting Using an EGARCH Model
  • 3.9 The Threshold GARCH Model
  • 3.10 The CHARMA Model
    • 3.10.1 Effects of Explanatory Variables
  • 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
    • 3.15.1 Use of High-Frequency Data
    • 3.15.2 Use of Daily Open, High, Low, and Close Prices
  • 3.16 Kurtosis of GARCH Models
  • Appendix: Some RATS Programs for Estimating Volatility Models
  • Exercises
  • References
• 4 Nonlinear Models and Their Applications
  • 4.1 Nonlinear Models
    • 4.1.1 Bilinear Model
    • 4.1.2 Threshold Autoregressive (TAR) Model
    • 4.1.3 Smooth Transition AR (STAR) Model
    • 4.1.4 Markov Switching Model
    • 4.1.5 Nonparametric Methods
    • 4.1.6 Functional Coefficient AR Model
    • 4.1.7 Nonlinear Additive AR Model
    • 4.1.8 Nonlinear State-Space Model
    • 4.1.9 Neural Networks
  • 4.2 Nonlinearity Tests
    • 4.2.1 Nonparametric Tests
    • 4.2.2 Parametric Tests
    • 4.2.3 Applications
  • 4.3 Modeling
  • 4.4 Forecasting
    • 4.4.1 Parametric Bootstrap
    • 4.4.2 Forecasting Evaluation
  • 4.5 Application
  • Appendix A: Some RATS Programs for Nonlinear Volatility Models
  • Appendix B: R and S-Plus Commands for Neural Network
  • Exercises
  • References
• 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
    • 5.4.1 Ordered Probit Model
    • 5.4.2 Decomposition Model
  • 5.5 Duration Models
    • 5.5.1 The ACD Model
    • 5.5.2 Simulation
    • 5.5.3 Estimation
  • 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
  • Exercises
  • References
• 6 Continuous-Time Models and Their Applications
  • 6.1 Options
  • 6.2 Some Continuous-Time Stochastic Processes
    • 6.2.1 Wiener Process
    • 6.2.2 Generalized Wiener Process
    • 6.2.3 Ito Process
  • 6.3 Ito’s Lemma
    • 6.3.1 Review of Differentiation
    • 6.3.2 Stochastic Differentiation
    • 6.3.3 An Application
    • 6.3.4 Estimation of µ and ó
  • 6.4 Distributions of Stock Prices and Log Returns
  • 6.5 Derivation of Black–Scholes Differential Equation
  • 6.6 Black–Scholes Pricing Formulas
    • 6.6.1 Risk-Neutral World
    • 6.6.2 Formulas
    • 6.6.3 Lower Bounds of European Options
    • 6.6.4 Discussion
  • 6.7 Extension of Ito’s Lemma
  • 6.8 Stochastic Integral
  • 6.9 Jump Diffusion Models
    • 6.9.1 Option Pricing under Jump Diffusion
  • 6.10 Estimation of Continuous-Time Models
  • Appendix A: Integration of Black–Scholes Formula
  • Appendix B: Approximation to Standard Normal Probability
  • Exercises
  • References
• 7 Extreme Values, Quantiles, and Value at Risk
  • 7.1 Value at Risk
  • 7.2 RiskMetrics
    • 7.2.1 Discussion
    • 7.2.2 Multiple Positions
    • 7.2.3 Expected Shortfall
  • 7.3 Econometric Approach to VaR Calculation
    • 7.3.1 Multiple Periods
    • 7.3.2 Expected Shortfall under Conditional Normality
  • 7.4 Quantile Estimation
    • 7.4.1 Quantile and Order Statistics
    • 7.4.2 Quantile Regression
  • 7.5 Extreme Value Theory
    • 7.5.1 Review of Extreme Value Theory
    • 7.5.2 Empirical Estimation
    • 7.5.3 Application to Stock Returns
  • 7.6 Extreme Value Approach to VaR
    • 7.6.1 Discussion
    • 7.6.2 Multiperiod VaR
    • 7.6.3 Return Level
  • 7.7 New Approach Based on the Extreme Value Theory
    • 7.7.1 Statistical Theory
    • 7.7.2 Mean Excess Function
    • 7.7.3 New Approach to Modeling Extreme Values
    • 7.7.4 VaR Calculation Based on the New Approach
    • 7.7.5 Alternative Parameterization
    • 7.7.6 Use of Explanatory Variables
    • 7.7.7 Model Checking
    • 7.7.8 An Illustration
  • 7.8 The Extremal Index
    • 7.8.1 The D(un) Condition
    • 7.8.2 Estimation of the Extremal Index
    • 7.8.3 Value at Risk for a Stationary Time Series
  • Exercises
  • References
• 8 Multivariate Time Series Analysis and Its Applications
  • 8.1 Weak Stationarity and Cross-Correlation Matrices
    • 8.1.1 Cross-Correlation Matrices
    • 8.1.2 Linear Dependence
    • 8.1.3 Sample Cross-Correlation Matrices
    • 8.1.4 Multivariate Portmanteau Tests
  • 8.2 Vector Autoregressive Models
    • 8.2.1 Reduced and Structural Forms
    • 8.2.2 Stationarity Condition and Moments of a VAR(1) Model
    • 8.2.3 Vector AR(p) Models
    • 8.2.4 Building a VAR(p) Model
    • 8.2.5 Impulse Response Function
  • 8.3 Vector Moving-Average Models
  • 8.4 Vector ARMA Models
    • 8.4.1 Marginal Models of Components
  • 8.5 Unit-Root Nonstationarity and Cointegration
    • 8.5.1 An Error Correction Form
  • 8.6 Cointegrated VAR Models
    • 8.6.1 Specification of the Deterministic Function
    • 8.6.2 Maximum-Likelihood Estimation
    • 8.6.3 Cointegration Test
    • 8.6.4 Forecasting of Cointegrated VAR Models
    • 8.6.5 An Example
  • 8.7 Threshold Cointegration and Arbitrage
    • 8.7.1 Multivariate Threshold Model
    • 8.7.2 The Data
    • 8.7.3 Estimation
  • 8.8 Pairs Trading
    • 8.8.1 Theoretical Framework
    • 8.8.2 Trading Strategy
    • 8.8.3 Simple Illustration
  • Appendix A: Review of Vectors and Matrices
  • Appendix B: Multivariate Normal Distributions
  • Appendix C: Some SCA Commands
  • Exercises
  • References
• 9 Principal Component Analysis and Factor Models
  • 9.1 A Factor Model
  • 9.2 Macroeconometric Factor Models
    • 9.2.1 Single-Factor Model
    • 9.2.2 Multifactor Models
  • 9.3 Fundamental Factor Models
    • 9.3.1 BARRA Factor Model
    • 9.3.2 Fama–French Approach
  • 9.4 Principal Component Analysis
    • 9.4.1 Theory of PCA
    • 9.4.2 Empirical PCA
  • 9.5 Statistical Factor Analysis
    • 9.5.1 Estimation
    • 9.5.2 Factor Rotation
    • 9.5.3 Applications
  • 9.6 Asymptotic Principal Component Analysis
    • 9.6.1 Selecting the Number of Factors
    • 9.6.2 An Example
  • Exercises
  • References
• 10 Multivariate Volatility Models and Their Applications
  • 10.1 Exponentially Weighted Estimate
  • 10.2 Some Multivariate GARCH Models
    • 10.2.1 Diagonal Vectorization (VEC) Model
    • 10.2.2 BEKK Model
  • 10.3 Reparameterization
    • 10.3.1 Use of Correlations
    • 10.3.2 Cholesky Decomposition
  • 10.4 GARCH Models for Bivariate Returns
    • 10.4.1 Constant-Correlation Models
    • 10.4.2 Time-Varying Correlation Models
    • 10.4.3 Dynamic Correlation Models
  • 10.5 Higher Dimensional Volatility Models
  • 10.6 Factor–Volatility Models
  • 10.7 Application
  • 10.8 Multivariate t Distribution
  • Appendix: Some Remarks on Estimation
  • Exercises
  • References
• 11 State-Space Models and Kalman Filter
  • 11.1 Local Trend Model
    • 11.1.1 Statistical Inference
    • 11.1.2 Kalman Filter
    • 11.1.3 Properties of Forecast Error
    • 11.1.4 State Smoothing
    • 11.1.5 Missing Values
    • 11.1.6 Effect of Initialization
    • 11.1.7 Estimation
    • 11.1.8 S-Plus Commands Used
  • 11.2 Linear State-Space Models
  • 11.3 Model Transformation
    • 11.3.1 CAPM with Time-Varying Coefficients
    • 11.3.2 ARMA Models
    • 11.3.3 Linear Regression Model
    • 11.3.4 Linear Regression Models with ARMA Errors
    • 11.3.5 Scalar Unobserved Component Model
  • 11.4 Kalman Filter and Smoothing
    • 11.4.1 Kalman Filter
    • 11.4.2 State Estimation Error and Forecast Error
    • 11.4.3 State Smoothing
    • 11.4.4 Disturbance Smoothing
  • 11.5 Missing Values
  • 11.6 Forecasting
  • 11.7 Application
  • Exercises
  • References
• 12 Markov Chain Monte Carlo Methods with Applications
  • 12.1 Markov Chain Simulation
  • 12.2 Gibbs Sampling
  • 12.3 Bayesian Inference
    • 12.3.1 Posterior Distributions
    • 12.3.2 Conjugate Prior Distributions
  • 12.4 Alternative Algorithms
    • 12.4.1 Metropolis Algorithm
    • 12.4.2 Metropolis–Hasting Algorithm
    • 12.4.3 Griddy Gibbs
  • 12.5 Linear Regression with Time Series Errors
  • 12.6 Missing Values and Outliers
    • 12.6.1 Missing Values
    • 12.6.2 Outlier Detection
  • 12.7 Stochastic Volatility Models
    • 12.7.1 Estimation of Univariate Models
    • 12.7.2 Multivariate Stochastic Volatility Models
  • 12.8 New Approach to SV Estimation
  • 12.9 Markov Switching Models
  • 12.10 Forecasting
  • 12.11 Other Applications
  • Exercises
  • References
• Index

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