| Description |
1 online resource |
| Note |
Includes index. |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 Properties |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
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 and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. Provides practical, hands-on examples in time-series econometrics Presents a more application-oriented, less technical book on financial econometrics Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction Features examples worked out in EViews (9 or higher). |
| Subject |
Time-series analysis.
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Econometrics.
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Série chronologique. |
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Économétrie. |
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Econometrics |
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Time-series analysis |
| Added Author |
Pedio, Manuela, author.
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| Other Form: |
Print version: Guidolin, Massimo. Essentials of time series for financial applications. London, United Kingdom : Academic Press, an imprint of Elsevier, 2018 0128134097 9780128134092 (OCoLC)1007037050 |
| ISBN |
9780128134108 (electronic bk.) |
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0128134100 (electronic bk.) |
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