Essentials of time series for financial applications / Massimo Guidolin ; Manuela Pedio.

Includes index.

Intro; Title page; Table of Contents; Copyright; List of Figures; List of Tables; Preface; Chapter 1. Linear Regression Model; Abstract; 1.1 Inference in Linear Regression Models; 1.2 Testing for Violations of the Linear Regression Framework; 1.3 Specifying the Regressors; 1.4 Issues With Heteroskedasticity and Autoc14orrelation of the Errors; 1.5 The Interpretation of Regression Results; References; Appendix 1.A; Appendix 1.B Principal Component Analysis; Chapter 2. Autoregressive Moving Average (ARMA) Models and Their Practical Applications; Abstract 2.1 Essential Concepts in Time Series Analysis2.2 Moving Average and Autoregressive Processes; 2.3 Selection and Estimation of AR, MA, and ARMA Models; 2.4 Forecasting ARMA Processes; References; Appendix 2.A; Chapter 3. Vector Autoregressive Moving Average (VARMA) Models; Abstract; 3.1 Foundations of Multivariate Time Series Analysis; 3.2 Introduction to Vector Autoregressive Analysis; 3.3 Structural Analysis With Vector Autoregressive Models; 3.4 Vector Moving Average and Vector Autoregressive Moving Average Models; References; Chapter 4. Unit Roots and Cointegration; Abstract 4.1 Defining Unit Root Processes4.2 The Spurious Regression Problem; 4.3 Unit Root Tests; 4.4 Cointegration and Error-Correction Models; References; Chapter 5. Single-Factor Conditionally Heteroskedastic Models, ARCH and GARCH; Abstract; 5.1 Stylized Facts and Preliminaries; 5.2 Simple Univariate Parametric Models; 5.3 Advanced Univariate Volatility Modeling; 5.4 Testing for ARCH; 5.5 Forecasting With GARCH Models; 5.6 Estimation of and Inference on GARCH Models; References; Appendix 5.A Nonparametric Kernel Density Estimation; Chapter 6. Multivariate GARCH and Conditional Correlation Models Abstract6.1 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; Chapter 7. Multifactor Heteroskedastic Models, Stoc60hastic Volatility; Abstract; 7.1 A Primer on the Kalman Filter; 7.2 Simple Stoc63hastic Volatility Models and their Estimation Using the Kalman Filter; 7.3 Extended, Second-Generation Stoc64hastic Volatility Models; 7.4 GARCH versus Stoc65hastic Volatility: Which One?; References Chapter 8. Models With Breaks, Recurrent Regime Switching, and NonlinearitiesAbstract; 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; 8.3 Threshold and Smooth Transition Regime Switching Models; References; Chapter 9. Markov Switching Models; Abstract; 9.1 Definitions and Classifications; 9.2 Understanding Markov Switching Dynamics Through Simulations; 9.3 Markov Switching Regressions; 9.4 Markov Chain Processes and Their Propertie.

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 risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus.