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.
1. 3.2 Introduction to Vector Autoregressive Analysis - p. 82 2. 3.3 Structural Analysis With Vector Autoregressive Models - p. 100 3. 3.4 Vector Moving Average and Vector Autoregressive Moving Average Models - p. 110 4. References - p. 112 5. Chapter 4. Unit Roots and Cointegration - p. 113 6. Abstract - p. 113 7. 4.1 Defining Unit Root Processes - p. 113 8. 4.2 The Spurious Regression Problem - p. 121 9. 4.3 Unit Root Tests - p. 124 10. 4.4 Cointegration and Error-Correction Models - p. 133 11. References - p. 149 12. Chapter 5. Single-Factor Conditionally Heteroskedastic Models, ARCH and GARCH - p. 151 13. Abstract - p. 151 14. 5.1 Stylized Facts and Preliminaries - p. 151 15. 5.2 Simple Univariate Parametric Models - p. 157 16. 5.3 Advanced Univariate Volatility Modeling - p. 190 17. 5.4 Testing for ARCH - p. 198 18. 5.5 Forecasting With GARCH Models - p. 205 19. 5.6 Estimation of and Inference on GARCH Models - p. 210 20. References - p. 225 21. Appendix 5.A Nonparametric Kernel Density Estimation - p. 225 22. Chapter 6. Multivariate GARCH and Conditional Correlation Models - p. 229 23. Abstract - p. 229 24. 6.1 Introduction and Preliminaries - p. 229 25. 6.2 Simple Models of Covariance Prediction - p. 230 26. 6.3 Full, Multivariate GARCH Models - p. 238 27. 6.4 Constant and Dynamic Conditional Correlation Models - p. 250 28. 6.5 Factor GARCH Models - p. 257 29. 6.6 Inference and Model Specification - p. 264 30. References - p. 266 31. Chapter 7. Multifactor Heteroskedastic Models, Stochastic Volatility - p. 267 32. Abstract - p. 267 33. 7.1 A Primer on the Kalman Filter - p. 268 34. 7.2 Simple Stochastic Volatility Models and their Estimation Using the Kalman Filter - p. 271 35. 7.3 Extended, Second-Generation Stochastic Volatility Models - p. 281 36. 7.4 GARCH versus Stochastic Volatility: Which One? - p. 282 37. References - p. 285 38. Chapter 8. Models With Breaks, Recurrent Regime Switching, and Nonlinearities - p. 287 39. Abstract - p. 287 40. 8.1 A Primer on the Key Features and Classification of Statistical Model of Instability - p. 287 41. 8.2 Detecting and Exploiting Structural Change in Linear Models - p. 290 42. 8.3 Threshold and Smooth Transition Regime Switching Models - p. 307 43. References - p. 326 44. Chapter 9. Markov Switching Models - p. 329 45. Abstract - p. 329 46. 9.1 Definitions and Classifications - p. 329 47. 9.2 Understanding Markov Switching Dynamics Through Simulations - p. 337 48. 9.3 Markov Switching Regressions - p. 341 49. 9.4 Markov Chain Processes and Their Properties - p. 344 50. 9.5 Estimation and Inference for Markov Switching Models - p. 350 51. 9.6 Forecasting With Markov Switching Models - p. 365 52. 9.7 Markov Switching ARCH and DCC Models - p. 369 53. 9.8 Do Nonlinear and Markov Switching Models Work in Practice? - p. 372 54. References - p. 374 55. Appendix 9.A Some Notions Concerning Ergodic Markov Chains - p. 374 56. Appendix 9.B State-Space Representation of an Markov Switching Model - p. 377 57. Appendix 9.C First-Order Conditions for Maximum Likelihood Estimation of Markov Switching Models - p. 378 58. Chapter 10. Realized Volatility and Covariance - p. 381 59. Appendix A. Mathematical and Statistical Appendix - p. 399 60. Index - p. 409
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.
1. 3.2 Introduction to Vector Autoregressive Analysis - p. 82 2. 3.3 Structural Analysis With Vector Autoregressive Models - p. 100 3. 3.4 Vector Moving Average and Vector Autoregressive Moving Average Models - p. 110 4. References - p. 112 5. Chapter 4. Unit Roots and Cointegration - p. 113 6. Abstract - p. 113 7. 4.1 Defining Unit Root Processes - p. 113 8. 4.2 The Spurious Regression Problem - p. 121 9. 4.3 Unit Root Tests - p. 124 10. 4.4 Cointegration and Error-Correction Models - p. 133 11. References - p. 149 12. Chapter 5. Single-Factor Conditionally Heteroskedastic Models, ARCH and GARCH - p. 151 13. Abstract - p. 151 14. 5.1 Stylized Facts and Preliminaries - p. 151 15. 5.2 Simple Univariate Parametric Models - p. 157 16. 5.3 Advanced Univariate Volatility Modeling - p. 190 17. 5.4 Testing for ARCH - p. 198 18. 5.5 Forecasting With GARCH Models - p. 205 19. 5.6 Estimation of and Inference on GARCH Models - p. 210 20. References - p. 225 21. Appendix 5.A Nonparametric Kernel Density Estimation - p. 225 22. Chapter 6. Multivariate GARCH and Conditional Correlation Models - p. 229 23. Abstract - p. 229 24. 6.1 Introduction and Preliminaries - p. 229 25. 6.2 Simple Models of Covariance Prediction - p. 230 26. 6.3 Full, Multivariate GARCH Models - p. 238 27. 6.4 Constant and Dynamic Conditional Correlation Models - p. 250 28. 6.5 Factor GARCH Models - p. 257 29. 6.6 Inference and Model Specification - p. 264 30. References - p. 266 31. Chapter 7. Multifactor Heteroskedastic Models, Stochastic Volatility - p. 267 32. Abstract - p. 267 33. 7.1 A Primer on the Kalman Filter - p. 268 34. 7.2 Simple Stochastic Volatility Models and their Estimation Using the Kalman Filter - p. 271 35. 7.3 Extended, Second-Generation Stochastic Volatility Models - p. 281 36. 7.4 GARCH versus Stochastic Volatility: Which One? - p. 282 37. References - p. 285 38. Chapter 8. Models With Breaks, Recurrent Regime Switching, and Nonlinearities - p. 287 39. Abstract - p. 287 40. 8.1 A Primer on the Key Features and Classification of Statistical Model of Instability - p. 287 41. 8.2 Detecting and Exploiting Structural Change in Linear Models - p. 290 42. 8.3 Threshold and Smooth Transition Regime Switching Models - p. 307 43. References - p. 326 44. Chapter 9. Markov Switching Models - p. 329 45. Abstract - p. 329 46. 9.1 Definitions and Classifications - p. 329 47. 9.2 Understanding Markov Switching Dynamics Through Simulations - p. 337 48. 9.3 Markov Switching Regressions - p. 341 49. 9.4 Markov Chain Processes and Their Properties - p. 344 50. 9.5 Estimation and Inference for Markov Switching Models - p. 350 51. 9.6 Forecasting With Markov Switching Models - p. 365 52. 9.7 Markov Switching ARCH and DCC Models - p. 369 53. 9.8 Do Nonlinear and Markov Switching Models Work in Practice? - p. 372 54. References - p. 374 55. Appendix 9.A Some Notions Concerning Ergodic Markov Chains - p. 374 56. Appendix 9.B State-Space Representation of an Markov Switching Model - p. 377 57. Appendix 9.C First-Order Conditions for Maximum Likelihood Estimation of Markov Switching Models - p. 378 58. Chapter 10. Realized Volatility and Covariance - p. 381 59. Appendix A. Mathematical and Statistical Appendix - p. 399 60. Index - p. 409
