Essentials of time series for financial applications

Essentials of time series for financial applications

Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and...

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Detalles Bibliográficos
Otros Autores: Guidolin, Massimo, author (author), Pedio, Manuela, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, United Kingdom : Academic Press, An imprint of Elsevier [2018]
Edición:1st edition
Materias:
Time-series analysis.
Econometrics.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630463206719
Tabla de Contenidos:
  • Front Cover
  • Essentials of Time Series for Financial Applications
  • Copyright Page
  • Contents
  • List of Figures
  • List of Tables
  • Preface
  • 1 Linear Regression Model
  • 1.1 Inference in Linear Regression Models
  • 1.1.1 The Ordinary Least Squares Estimator
  • 1.1.2 Goodness of Fit Measures
  • 1.1.3 The Generalized Least Squared Estimator
  • 1.1.4 Maximum Likelihood Estimator
  • 1.1.5 Hypotheses Testing, Confidence Intervals, and Predictive Intervals
  • 1.1.6 Linear Regression Model With Stochastic Regressors
  • 1.1.7 Asymptotic Theory for Linear Regressions
  • 1.2 Testing for Violations of the Linear Regression Framework
  • 1.2.1 Linearity
  • 1.2.2 Structural Breaks and Parameter Stability Test
  • 1.3 Specifying the Regressors
  • 1.3.1 How to Select the Regressors
  • 1.3.2 Multicollinearity
  • 1.3.3 Measurement Errors in the Regressors
  • 1.4 Issues With Heteroskedasticity and Autocorrelation of the Errors
  • 1.4.1 Heteroskedastic Errors
  • 1.4.2 Autocorrelated Errors
  • 1.5 The Interpretation of Regression Results
  • References
  • Appendix 1.A
  • Appendix 1.B Principal Component Analysis
  • 2 Autoregressive Moving Average (ARMA) Models and Their Practical Applications
  • 2.1 Essential Concepts in Time Series Analysis
  • 2.1.1 Time Series and Their Properties
  • 2.1.2 Stationarity
  • 2.1.3 Sample Autocorrelations and Sample Partial Autocorrelations
  • 2.2 Moving Average and Autoregressive Processes
  • 2.2.1 Finite Order Moving Average Processes
  • 2.2.2 Autoregressive Processes
  • 2.2.3 Autoregressive Moving Average Processes
  • 2.3 Selection and Estimation of AR, MA, and ARMA Models
  • 2.3.1 The Selection of the Model and the Role of Information Criteria
  • 2.3.2 Estimation Methods
  • 2.3.3 Residual Diagnostics
  • 2.4 Forecasting ARMA Processes
  • 2.4.1 Standard Principles of Forecasting
  • 2.4.2 Forecasting an AR(p) Process.
  • 2.4.3 Forecasting the Future Value of an MA(q) Process
  • 2.4.4 Evaluating the Accuracy of a Forecast Function
  • References
  • Appendix 2.A
  • 3 Vector Autoregressive Moving Average (VARMA) Models
  • 3.1 Foundations of Multivariate Time Series Analysis
  • 3.1.1 Weak Stationarity of Multivariate Time Series
  • 3.1.2 Cross-Covariance and Cross-Correlation Matrices
  • 3.1.3 Sample Cross-Covariance and Cross-Correlation Matrices
  • 3.1.4 Multivariate Portmanteau Tests
  • 3.1.5 Multivariate White Noise Process
  • 3.2 Introduction to Vector Autoregressive Analysis
  • 3.2.1 From Structural to Reduced-Form Vector Autoregressive Models
  • 3.2.2 Stationarity Conditions and the Population Moments of a VAR(1) Process
  • 3.2.3 Generalization to a VAR(p) Model
  • 3.2.4 Estimation of a VAR(p) Model
  • 3.2.5 Specification of a Vector Autoregressive Model and Hypothesis Testing
  • 3.2.6 Forecasting With a Vector Autoregressive Model
  • 3.3 Structural Analysis With Vector Autoregressive Models
  • 3.3.1 Impulse Response Functions
  • 3.3.2 Variance Decompositions
  • 3.3.3 Granger Causality
  • 3.4 Vector Moving Average and Vector Autoregressive Moving Average Models
  • 3.4.1 Vector Moving Average Models
  • 3.4.2 Vector Autoregressive Moving Average Models
  • References
  • 4 Unit Roots and Cointegration
  • 4.1 Defining Unit Root Processes
  • 4.1.1 What Happens If One Incorrectly Detrends a Unit Root Series?
  • 4.1.2 What Happens If One Incorrectly Applies Differencing to (Deterministic) Trend-Stationary Series?
  • 4.1.3 What Happens If One Incorrectly Applies Differencing to a Stationary Series?
  • 4.1.4 What Happens If One Incorrectly Applies Differencing d+r Times to an I(d) Series?
  • 4.2 The Spurious Regression Problem
  • 4.3 Unit Root Tests
  • 4.3.1 Classical Dickey-Fuller Tests
  • 4.3.2 The Augmented Dickey-Fuller Test
  • 4.3.3 Other Unit Root Tests.
  • 4.3.4 Testing for Unit Roots in Moving-Average Processes
  • 4.4 Cointegration and Error-Correction Models
  • 4.4.1 The Relationship Between Cointegration and Economic Theory
  • 4.4.2 Definition of Cointegration
  • 4.4.3 Error-Correction Models
  • 4.4.4 Testing for Cointegration
  • References
  • 5 Single-Factor Conditionally Heteroskedastic Models, ARCH and GARCH
  • 5.1 Stylized Facts and Preliminaries
  • 5.1.1 The Stylized Facts of Conditional Heteroskedasticity
  • 5.2 Simple Univariate Parametric Models
  • 5.2.1 Rolling Window Forecasts
  • 5.2.2 Exponential Smoothing Variance Forecasts: RiskMetrics
  • 5.2.3 ARCH Models
  • 5.2.4 Comparing the Performance of Alternative Variance Forecast Models: Do We Need More Than ARCH?
  • 5.2.5 Generalized ARCH Models and Their Statistical Properties
  • 5.2.6 A Few Additional, Popular ARCH Models
  • 5.2.6.1 The Threshold GARCH (TARCH) Model
  • 5.2.6.2 The Power ARCH and NAGARCH Models
  • 5.2.6.3 The Component GARCH Model and the Differences Between Transitory and Permanent Variance Components
  • 5.2.6.4 The GARCH-In-Mean Model
  • 5.3 Advanced Univariate Volatility Modeling
  • 5.3.1 Non-Gaussian Marginal Innovations
  • 5.3.2 GARCH Models Augmented by Exogenous (Predetermined) Factors
  • 5.4 Testing for ARCH
  • 5.4.1 Lagrange Multiplier ARCH Tests
  • 5.4.2 News Impact Curves and Testing for Asymmetric ARCH
  • 5.5 Forecasting With GARCH Models
  • 5.5.1 Long-Horizon, Point Forecasts
  • 5.5.2 Forecasts of Variance for Sums of Returns or Shocks
  • 5.6 Estimation of and Inference on GARCH Models
  • 5.6.1 Maximum Likelihood Estimation
  • 5.6.2 The Properties of MLE
  • 5.6.3 Quasi MLE
  • 5.6.4 Misspecification Tests
  • 5.6.5 Sequential Estimation and QMLE
  • 5.6.6 Data Frequency in Estimation and Temporal Aggregation
  • References
  • Appendix 5.A Nonparametric Kernel Density Estimation.
  • 6 Multivariate GARCH and Conditional Correlation Models
  • 6.1 Introduction and Preliminaries
  • 6.2 Simple Models of Covariance Prediction
  • 6.3 Full, Multivariate GARCH Models
  • 6.4 Constant and Dynamic Conditional Correlation Models
  • 6.5 Factor GARCH Models
  • 6.6 Inference and Model Specification
  • References
  • 7 Multifactor Heteroskedastic Models, Stochastic Volatility
  • 7.1 A Primer on the Kalman Filter
  • 7.1.1 A Simple Univariate Example
  • 7.1.2 The General Case
  • 7.2 Simple Stochastic Volatility Models and their Estimation Using the Kalman Filter
  • 7.2.1 The Economics of Stochastic Volatility: The Normal Mixture Model
  • 7.2.2 One Benchmark Case: The Log-Normal Two-Factor Stochastic Volatility Model
  • 7.3 Extended, Second-Generation Stochastic Volatility Models
  • 7.4 GARCH versus Stochastic Volatility: Which One?
  • 7.4.1 Some GARCH Models Are (Asymptotically) Stochastic Volatility Models
  • 7.4.2 Stressing the Differences: What Have We Learned So Far?
  • References
  • 8 Models With Breaks, Recurrent Regime Switching, and Nonlinearities
  • 8.1 A Primer on the Key Features and Classification of Statistical Model of Instability
  • 8.2 Detecting and Exploiting Structural Change in Linear Models
  • 8.2.1 Chow Tests for Given Break Dates
  • 8.2.2 CUSUM and CUSUM Square Tests
  • 8.2.3 Andrews and Quandt's Single-Break Test
  • 8.2.4 Bai and Perron's Multiple, Endogenous Breaks Test
  • 8.2.5 Testing for Breaks When Testing for Unit Roots and Cointegration, and Vice Versa
  • 8.3 Threshold and Smooth Transition Regime Switching Models
  • 8.3.1 Threshold Regression and Autoregressive Models
  • 8.3.2 Smooth Transition Regression and Autoregressive Models
  • 8.3.3 Testing (Non-)Linearities
  • References
  • 9 Markov Switching Models
  • 9.1 Definitions and Classifications
  • 9.2 Understanding Markov Switching Dynamics Through Simulations.
  • 9.2.1 Markov Switching Models as Normal Mixtures and Density Approximation
  • 9.3 Markov Switching Regressions
  • 9.4 Markov Chain Processes and Their Properties
  • 9.5 Estimation and Inference for Markov Switching Models
  • 9.5.1 Maximum Likelihood Estimation and the Expectation-Maximization Algorithm
  • 9.5.2 Tests of Hypotheses
  • 9.5.3 Testing and Selecting the Number of Regimes and the Nuisance Parameters Problem
  • 9.6 Forecasting With Markov Switching Models
  • 9.7 Markov Switching ARCH and DCC Models
  • 9.8 Do Nonlinear and Markov Switching Models Work in Practice?
  • References
  • Appendix 9.A Some Notions Concerning Ergodic Markov Chains
  • Appendix 9.B State-Space Representation of an Markov Switching Model
  • Appendix 9.C First-Order Conditions for Maximum Likelihood Estimation of Markov Switching Models
  • 10 Realized Volatility and Covariance
  • 10.1 Measuring Realized Variance
  • 10.1.1 Quadratic Variation and Its Estimators
  • 10.1.2 Microstructure Noise and the Choice of the Sampling Frequency
  • 10.1.3 Other Bias-Adjusted Measures of Realized Volatility
  • 10.1.4 Jumps and Bipower Variation
  • 10.2 Forecasting Realized Variance
  • 10.2.1 Stylized Facts About Realized Variance
  • 10.2.2 Forecasting Realized Variance: Heterogeneous Autoregressions
  • 10.2.3 Range-Based Variance Forecasts
  • 10.3 Multivariate Applications
  • 10.3.1 Realized Covariance Matrix Estimation
  • 10.3.2 Range-Based Covariance Estimation
  • References
  • Appendix A: Mathematical and Statistical Appendix
  • A. Fundamental Statistical Definitions
  • A.1 Random Variables
  • A.2 Stochastic Processes
  • A.2.1 Convergence in Probability
  • A.2.2 Convergence in Distribution
  • A.3 Key Theorems Concerning Stochastic Processes
  • A.3.1 Law of Large Numbers
  • A.3.2 Lindeberg-Lévy's Central Limit Theorem
  • B. Matrix Algebra.
  • B.1 Rank of a Matrix, Eigenvalues, and Eigenvectors.

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