Essentials of Time Series for Financial Applications

Essentials of Time Series for Financial Applications

by Massimo Guidolin, Manuela Pedio
Released May 2018
Publisher(s): Academic Press
ISBN: 9780128134108

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Book description

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)

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. List of Figures
  6. List of Tables
  7. Preface
  8. Chapter 1. Linear Regression Model
    1. Abstract
    2. 1.1 Inference in Linear Regression Models
    3. 1.2 Testing for Violations of the Linear Regression Framework
    4. 1.3 Specifying the Regressors
    5. 1.4 Issues With Heteroskedasticity and Autocorrelation of the Errors
    6. 1.5 The Interpretation of Regression Results
    7. References
    8. Appendix 1.A
    9. Appendix 1.B Principal Component Analysis
  9. Chapter 2. Autoregressive Moving Average (ARMA) Models and Their Practical Applications
    1. Abstract
    2. 2.1 Essential Concepts in Time Series Analysis
    3. 2.2 Moving Average and Autoregressive Processes
    4. 2.3 Selection and Estimation of AR, MA, and ARMA Models
    5. 2.4 Forecasting ARMA Processes
    6. References
    7. Appendix 2.A
  10. Chapter 3. Vector Autoregressive Moving Average (VARMA) Models
    1. Abstract
    2. 3.1 Foundations of Multivariate Time Series Analysis
    3. 3.2 Introduction to Vector Autoregressive Analysis
    4. 3.3 Structural Analysis With Vector Autoregressive Models
    5. 3.4 Vector Moving Average and Vector Autoregressive Moving Average Models
    6. References
  11. Chapter 4. Unit Roots and Cointegration
    1. Abstract
    2. 4.1 Defining Unit Root Processes
    3. 4.2 The Spurious Regression Problem
    4. 4.3 Unit Root Tests
    5. 4.4 Cointegration and Error-Correction Models
    6. References
  12. Chapter 5. Single-Factor Conditionally Heteroskedastic Models, ARCH and GARCH
    1. Abstract
    2. 5.1 Stylized Facts and Preliminaries
    3. 5.2 Simple Univariate Parametric Models
    4. 5.3 Advanced Univariate Volatility Modeling
    5. 5.4 Testing for ARCH
    6. 5.5 Forecasting With GARCH Models
    7. 5.6 Estimation of and Inference on GARCH Models
    8. References
    9. Appendix 5.A Nonparametric Kernel Density Estimation
  13. Chapter 6. Multivariate GARCH and Conditional Correlation Models
    1. Abstract
    2. 6.1 Introduction and Preliminaries
    3. 6.2 Simple Models of Covariance Prediction
    4. 6.3 Full, Multivariate GARCH Models
    5. 6.4 Constant and Dynamic Conditional Correlation Models
    6. 6.5 Factor GARCH Models
    7. 6.6 Inference and Model Specification
    8. References
  14. Chapter 7. Multifactor Heteroskedastic Models, Stochastic Volatility
    1. Abstract
    2. 7.1 A Primer on the Kalman Filter
    3. 7.2 Simple Stochastic Volatility Models and their Estimation Using the Kalman Filter
    4. 7.3 Extended, Second-Generation Stochastic Volatility Models
    5. 7.4 GARCH versus Stochastic Volatility: Which One?
    6. References
  15. Chapter 8. Models With Breaks, Recurrent Regime Switching, and Nonlinearities
    1. Abstract
    2. 8.1 A Primer on the Key Features and Classification of Statistical Model of Instability
    3. 8.2 Detecting and Exploiting Structural Change in Linear Models
    4. 8.3 Threshold and Smooth Transition Regime Switching Models
    5. References
  16. Chapter 9. Markov Switching Models
    1. Abstract
    2. 9.1 Definitions and Classifications
    3. 9.2 Understanding Markov Switching Dynamics Through Simulations
    4. 9.3 Markov Switching Regressions
    5. 9.4 Markov Chain Processes and Their Properties
    6. 9.5 Estimation and Inference for Markov Switching Models
    7. 9.6 Forecasting With Markov Switching Models
    8. 9.7 Markov Switching ARCH and DCC Models
    9. 9.8 Do Nonlinear and Markov Switching Models Work in Practice?
    10. References
    11. Appendix 9.A Some Notions Concerning Ergodic Markov Chains
    12. Appendix 9.B State-Space Representation of an Markov Switching Model
    13. Appendix 9.C First-Order Conditions for Maximum Likelihood Estimation of Markov Switching Models
  17. Chapter 10. Realized Volatility and Covariance
    1. Abstract
    2. 10.1 Measuring Realized Variance
    3. 10.2 Forecasting Realized Variance
    4. 10.3 Multivariate Applications
    5. References
  18. Appendix A. Mathematical and Statistical Appendix
    1. A Fundamental Statistical Definitions
    2. B Matrix Algebra
    3. C Uncorrelatedness and Independence
    4. D Bootstrapping
  19. Index

Product information

  • Title: Essentials of Time Series for Financial Applications
  • Author(s): Massimo Guidolin, Manuela Pedio
  • Release date: May 2018
  • Publisher(s): Academic Press
  • ISBN: 9780128134108