Analysis of Financial Time Series

Name: Analysis of Financial Time Series
Price: 14730 ISK
Availability: InStock
Author: Ruey S. Tsay
ISBN: 9781118305751

Höfundur: Ruey S. Tsay (Útgáfa: 3)

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Útgefandi: Wiley Global Research (STMS)

ISBN: 9781118305751

Print ISBN: 9780470414354

Format: Page Fidelity (PDF)

Útgáfa: 3

Höfundar: Ruey S. Tsay

Tungumál: English

Útgefið: 2010-09-13

Prent takmörkun á líftíma: 100

Prent takmörkun: 10

Afritunar takmörkun: 2

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Efnisflokkar: Læknisfræði › Rannsóknir & Kenningar Stærðfræði › Margbreytugreining

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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