Essentials of time series for financial applications:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Academic Press, an imprint of Elsevier
[2018]
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030468635&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Includes index |
| Umfang: | xvi, 417 Seiten Diagramme |
| ISBN: | 9780128134092 |
Internformat
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| 100 | 1 | |a Guidolin, Massimo |d 1968- |e Verfasser |0 (DE-588)129674087 |4 aut | |
| 245 | 1 | 0 | |a Essentials of time series for financial applications |c Massimo Guidolin (Professor of Finance, Bocconi University and Research Fellow, BAFFI-CAREFIN Centre), Manuela Pedio (Teaching Fellow, Bocconi University and Fellow, BAFFI-CAREFIN Centre) |
| 264 | 1 | |a London |b Academic Press, an imprint of Elsevier |c [2018] | |
| 300 | |a xvi, 417 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
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Datensatz im Suchindex
| _version_ | 1822792431885615104 |
|---|---|
| adam_text |
Contents
List of Figures ix 2.1.2 Stationary 42
List of Tables xiii 2.1.3 Sample Autocorrelations
Preface XV and Sample Partial Autocorrelations 45
2.2 Moving Average and Autoregressive
Processes 49
2.2.1 Finite Order Moving Average
1. Linear Regression Model 1 Processes 49
1.1 inference in Linear Regression Models 1 2.2.2 Autoregressive Processes 52
1.1.1 The Ordinary Least Squares Estimator 3 2.2.3 Autoregressive Moving Average
1.1.2 Goodness of Fit Measures 5 Processes 58
1.1.3 The Generalized Least Squared 2.3 Selection and Estimation of AR, MA,
Estimator 7 and ARMA Models 61
1.1.4 Maximum Likelihood Estimator 9 2.3.1 The Selection of the Model
1.1.5 Hypotheses Testing, Confidence and the Role of Information
Intervals, and Predictive Intervals 10 Criteria 61
1.1.6 Linear Regression Model With 2.3.2 Estimation Methods 65
Stochastic Regressors 15 2.3.3 Residual Diagnostics 67
1.1.7 Asymptotic Theory for Linear 2.4 Forecasting ARMA Processes 71
Regressions 16 2.4.1 Standard Principles of Forecasting 71
1.2 Testing for Violations of the Linear 2.4.2 Forecasting an AR(p) Process 71
Regression Framework 18 2.4.3 Forecasting the Future Value
1.2.1 Linearity 18 of an MA(q) Process 72
1.2.2 Structural Breaks and Parameter 2.4.4 Evaluating the Accuracy
Stability Test 23 of a Forecast Function 73
1.3 Specifying the Regressors 25 References 75
1.3.1 How to Select the Regressors 26 Appendix 2.A 75
1.3.2 Multicollinearity 29
1.3.3 Measurement Errors in the Regressors 31
1.4 Issues With Heteroskedasticity and
Autocorrelation of the Errors 32
1,4.1 Heteroskedastic Errors 32 3. Vector Autoregressive Moving
1.4.2 Autocorrelated Errors 34 Average (VARMA) Models 77
1.5 The Interpretation of Regression Results 34 3.1 Foundations of Multivariate Time Series
References 36 Analysis 77
Appendix 1.A 36 3.1.1 Weak Stationary of Multivariate
Appendix 1.B Principal Component Analysis 38 Time Series 77
3.1.2 Cross-Covariance
and Cross-Correlation Matrices 78
3.1.3 Sample Cross-Covariance
2. Autoregressive Moving Average and Cross-Correlation Matrices 79
(ARMA) Models and Their Practical 3.1.4 Multivariate Portmanteau Tests 81
Applications 41 3.1.5 Multivariate White Noise Process 82
2.1 Essential Concepts in Time Series Analysis 41 3.2 Introduction to Vector Autoregressive
2.1.1 Time Series and Their Properties 41 Analysis 82
V
vi Contents
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 Cointegratlon
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 A What Happens If One Incorrectly
Applies Differencing d -F r Times
to an 1(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
82 Heteroskedastic Models, ARCH
and GARCH 151
86 5.1 Stylized Facts and Preliminaries 151
89 5.1.1 The Stylized Facts of Conditional
91 Heteroskedasticity 153
5.2 Simple Univariate Parametric Models 157
5.2.1 Rolling Window Forecasts 157
94 5.2.2 Exponential Smoothing Variance
Forecasts: RiskMetrics 160
97 5.2.3 ARCH Models 161
5.2,4 Comparing the Performance
100 of Alternative Variance Forecast
100 Models: Do We Need More Than
105 ARCH? 168
108 5.2.5 Generalized ARCH Models
and Their Statistical Properties 171
110 5.2.6 A Few Additional, Popular ARCH
110 Models 181
5.3 Advanced Univariate Volatility Modeling 190
111 5.3.1 Non-Gaussian Marginal
112 Innovations 190
5.3.2 GARCH Models Augmented by
Exogenous (Predetermined) Factors 197
5.4 Testing for ARCH 198
113 5.4.1 Lagrange Multiplier ARCH Tests 199
113 5.4.2 News Impact Curves and Testing
for Asymmetric ARCH 202
118 5.5 Forecasting With GARCH Models 205
5.5.1 Long-Horizon, Point Forecasts 206
5.5.2 Forecasts of Variance for Sums
of Returns or Shocks 208
119 5.6 Estimation of and Inference on GARCH
Models 210
5.6.1 Maximum Likelihood Estimation 212
120 5.6.2 The Properties of MLE 214
5.6.3 Quasi MLE 218
5.6.4 Misspecification Tests 221
121 5.6.5 Sequential Estimation and QMLE 221
121 5.6.6 Data Frequency in Estimation
124 and Temporal Aggregation 223
124 References 225
126 Appendix 5.A Nonparametric Kernel 226
131 Density Estimation
132 6. Multivariate GARCH and
133 Conditional Correlation Models 229
133 6.1 Introduction and Preliminaries 229
6.2 Simple Models of Covariance
134 Prediction 230
135 6.3 Full, Multivariate GARCH Models 238
138 6.4 Constant and Dynamic Conditional
149 Correlation Models 250
Contents
6.5 Factor GARCH Models 257
6.6 Inference and Model Specification 264
References 266
7. Multifactor Heteroskedastic Models,
Stochastic Volatility 267
7.1 A Primer on the Kalman Filter 268
7.1.1 A Simple Univariate Example 268
7.1.2 The General Case 270
7.2 Simple Stochastic Volatility Models
and their Estimation Using the Kalman
Filter 271
7.2.1 The Economics of Stochastic
Volatility; The Normal Mixture
Model 271
7.2.2 One Benchmark Case:
The Log-Normal Two-Factor
Stochastic Volatility Model 272
7.3 Extended, Second-Generation Stochastic
Volatility Models 281
7.4 GARCH versus Stochastic Volatility:
Which One? 282
7.4.1 Some GARCH Models Are
(Asymptotically) Stochastic
Volatility Models 282
7.4.2 Stressing the Differences:
What Have We Learned So Far? 284
References 285
8. Models With Breaks, Recurrent
Regime Switching, and
Nonlinearities 287
8.1 A Primer on the Key Features
and Classification of Statistical Model
of Instability 287
8.2 Detecting and Exploiting Structural
Change in Linear Models 290
8.2.1 Chow Tests for Given Break Dates 291
8.2.2 CUSUM and CUSUM Square Tests 293
8.2.3 Andrews and Quandt's Single-Break
Test 294
8.2.4 Bai and Perron's Multiple,
Endogenous Breaks Test 297
8.2.5 Testing for Breaks When Testing
for Unit Roots and Cointegration,
and Vice Versa 302
8.3 Threshold and Smooth Transition Regime
Switching Models 307
8.3.1 Threshold Regression
and Autoregressive Models 308
8.3.2 Smooth Transition Regression
and Autoregressive Models 316
vii
8.3.3 Testing (Non-)Linearities 322
References 326
9. Markov Switching Models 329
9.1 Definitions and Classifications 329
9.2 Understanding Markov Switching
Dynamics Through Simulations 337
9.2.1 Markov Switching Models
as Normal Mixtures and Density
Approximation 340
9.3 Markov Switching Regressions 341
9.4 Markov Chain Processes and Their
Properties 344
9.5 Estimation and Inference for Markov
Switching Models 350
9.5.1 Maximum Likelihood Estimation
and the Expectation-Maximization
Algorithm 350
9.5.2 Tests of Hypotheses 359
9.5.3 Testing and Selecting the Number
of Regimes and the Nuisance
Parameters Problem 362
9.6 Forecasting With Markov Switching
Models 365
9.7 Markov Switching ARCH and DCC
Models 369
9.8 Do Nonlinear and Markov Switching
Models Work in Practice? 372
References 374
Appendix 9.A Some Notions Concerning
Ergodic Markov Chains 376
Appendix 9.B State-Space Representation
of an Markov Switching Model 377
Appendix 9.C First-Order Conditions
for Maximum Likelihood Estimation
of Markov Switching Models 378
10. Realized Volatility and Covariance 381
10.1 Measuring Realized Variance 381
10.1.1 Quadratic Variation
and Its Estimators 381
10.1.2 Microstructure Noise
and the Choice of the Sampling
Frequency 383
10.1.3 Other Bias-Adjusted Measures
of Realized Volatility 385
10.1.4 Jumps and Bipower Variation 387
10.2 Forecasting Realized Variance 388
10.2.1 Stylized Facts About Realized
Variance 388
10.2.2 Forecasting Realized Variance:
Heterogeneous Autoregressions 390
viii Contents
10.2.3 Range-Based Variance Appendix A: Mathematical and Statistical
Forecasts 392 Appendix 399
10.3 Multivariate Applications 395
10.3.1 Realized Covariance Matrix Index 409
Estimation 395
10.3.2 Range-Based Covariance
Estimation 396
References 396 |
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| id | DE-604.BV045077598 |
| illustrated | Not Illustrated |
| indexdate | 2025-01-31T19:05:26Z |
| institution | BVB |
| isbn | 9780128134092 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030468635 |
| oclc_num | 1045433381 |
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| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | xvi, 417 Seiten Diagramme |
| publishDate | 2018 |
| publishDateSearch | 2018 |
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| publisher | Academic Press, an imprint of Elsevier |
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| spelling | Guidolin, Massimo 1968- Verfasser (DE-588)129674087 aut Essentials of time series for financial applications Massimo Guidolin (Professor of Finance, Bocconi University and Research Fellow, BAFFI-CAREFIN Centre), Manuela Pedio (Teaching Fellow, Bocconi University and Fellow, BAFFI-CAREFIN Centre) London Academic Press, an imprint of Elsevier [2018] xvi, 417 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Includes index Ökonometrie (DE-588)4132280-0 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 s Zeitreihenanalyse (DE-588)4067486-1 s Ökonometrie (DE-588)4132280-0 s DE-604 Pedio, Manuela Sonstige (DE-588)1088566987 oth Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030468635&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Guidolin, Massimo 1968- Essentials of time series for financial applications Ökonometrie (DE-588)4132280-0 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Finanzmathematik (DE-588)4017195-4 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4067486-1 (DE-588)4017195-4 |
| title | Essentials of time series for financial applications |
| title_auth | Essentials of time series for financial applications |
| title_exact_search | Essentials of time series for financial applications |
| title_full | Essentials of time series for financial applications Massimo Guidolin (Professor of Finance, Bocconi University and Research Fellow, BAFFI-CAREFIN Centre), Manuela Pedio (Teaching Fellow, Bocconi University and Fellow, BAFFI-CAREFIN Centre) |
| title_fullStr | Essentials of time series for financial applications Massimo Guidolin (Professor of Finance, Bocconi University and Research Fellow, BAFFI-CAREFIN Centre), Manuela Pedio (Teaching Fellow, Bocconi University and Fellow, BAFFI-CAREFIN Centre) |
| title_full_unstemmed | Essentials of time series for financial applications Massimo Guidolin (Professor of Finance, Bocconi University and Research Fellow, BAFFI-CAREFIN Centre), Manuela Pedio (Teaching Fellow, Bocconi University and Fellow, BAFFI-CAREFIN Centre) |
| title_short | Essentials of time series for financial applications |
| title_sort | essentials of time series for financial applications |
| topic | Ökonometrie (DE-588)4132280-0 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Finanzmathematik (DE-588)4017195-4 gnd |
| topic_facet | Ökonometrie Zeitreihenanalyse Finanzmathematik |
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