ARCH models for financial applications:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester
Wiley
2010
|
| Ausgabe: | 1. publ. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes index |
| Beschreibung: | XX, 538 S. graph. Darst. 1 CD-ROM (12 cm) |
| ISBN: | 9780470066300 047006630X |
Internformat
MARC
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| 245 | 1 | 0 | |a ARCH models for financial applications |c Evdokia Xekalaki ; Stavros Degiannakis |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Chichester |b Wiley |c 2010 | |
| 300 | |a XX, 538 S. |b graph. Darst. |e 1 CD-ROM (12 cm) | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes index | ||
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Finance / Mathematical models | |
| 650 | 4 | |a Autoregression (Statistics) | |
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| 700 | 1 | |a Degiannakis, Stavros |e Verfasser |0 (DE-588)136733158 |4 aut | |
| 856 | 4 | 2 | |m HBZ Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020330974&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
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Datensatz im Suchindex
| _version_ | 1804142949521424384 |
|---|---|
| adam_text | Titel: ARCH models for financial applications
Autor: Xekalaki, Evdokia
Jahr: 2010
Contents
Preface xi
Notation xv
1 What is an ARCH process? 1
1.1 Introduction 1
1.2 The autoregressive conditionally heteroscedastic process 8
1.3 The leverage effect 13
1.4 The non-trading period effect 15
1.5 The non-synchronous trading effect 15
1.6 The relationship between conditional variance and
conditional mean 16
1.6.1 The ARCH in mean model 16
1.6.2 Volatility and serial correlation 18
2 ARCH volatility specifications 19
2.1 Model specifications 19
2.2 Methods of estimation 23
2.2.1 Maximum likelihood estimation 23
2.2.2 Numerical estimation algorithms 25
2.2.3 Quasi-maximum likelihood estimation 28
2.2.4 Other estimation methods 29
2.3 Estimating the GARCH model with EViews 6:
an empirical example 31
2.4 Asymmetric conditional volatility specifications 42
2.5 Simulating ARCH models using EViews 49
2.6 Estimating asymmetric ARCH models with G@RCH 4.2
OxMetrics: an empirical example 55
2.7 Misspecification tests 66
2.7.1 The Box-Pierce and Ljung-Box Q statistics 66
2.7.2 Tse s residual based diagnostic test for
conditional heteroscedasticity 67
2.7.3 Engle s Lagrange multiplier test 67
2.7.4 Engle and Ng s sign bias tests 68
2.7.5 The Breusch-Pagan, Godfrey, Glejser, Harvey and White tests 69
viii CONTENTS
2.7.6 The Wald, likelihood ratio and Lagrange multiplier tests 69
2.8 Other ARCH volatility specifications 70
2.8.1 Regime-switching ARCH models 70
2.8.2 Extended ARCH models 72
2.9 Other methods of volatility modelling 76
2.10 Interpretation of the ARCH process 82
Appendix 86
3 Fractionally integrated ARCH models 107
3.1 Fractionally integrated ARCH model specifications 107
3.2 Estimating fractionally integrated ARCH models using
G@RCH 4.2 OxMetrics: an empirical example 111
3.3 A more detailed investigation of the normality of the
standardized residuals: goodness-of-fit tests 122
3.3.1 EDF tests 123
3.3.2 Chi-square tests 124
3.3.3 QQ plots 125
3.3.4 Goodness-of-fit tests using EViews and G@RCH 126
Appendix 129
4 Volatility forecasting: an empirical example using EViews 6 143
4.1 One-step-ahead volatility forecasting 143
4.2 Ten-step-ahead volatility forecasting 150
Appendix 154
5 Other distributional assumptions 163
5.1 Non-normally distributed standardized innovations 163
5.2 Estimating ARCH models with non-normally distributed
standardized innovations using G@RCH 4.2 OxMetrics:
an empirical example 168
5.3 Estimating ARCH models with non-normally distributed
standardized innovations using EViews 6: an empirical example 174
5.4 Estimating ARCH models with non-normally distributed
standardized innovations using EViews 6: the logl object 176
Appendix 182
6 Volatility forecasting: an empirical example using G@RCH Ox 185
Appendix 195
7 Intraday realized volatility models 217
7.1 Realized volatility 217
7.2 Intraday volatility models 220
7.3 Intraday realized volatility and ARFIMAX models
in G@RCH 4.2 OxMetrics: an empirical example 223
7.3.1 Descriptive statistics 223
CONTENTS ix
7.3.2 In-sample analysis 228
7.3.3 Out-of-sample analysis 232
8 Applications in value-at-risk, expected shortfall and options pricing 239
8.1 One-day-ahead value-at-risk forecasting 239
8.1.1 Value-at-risk 239
8.1.2 Parametric value-at-risk modelling 240
8.1.3 Intraday data and value-at-risk modelling 242
8.1.4 Non-parametric and semi-parametric value-at-risk modelling 244
8.1.5 Back-testing value-at-risk 245
8.1.6 Value-at-risk loss functions 248
8.2 One-day-ahead expected shortfall forecasting 248
8.2.1 Historical simulation and filtered historical simulation
for expected shortfall 251
8.2.2 Loss functions for expected shortfall 251
8.3 FTSE100 index: one-step-ahead value-at-risk and expected
shortfall forecasting 252
8.4 Multi-period value-at-risk and expected shortfall forecasting 258
8.5 ARCH volatility forecasts in Black-Scholes option pricing 260
8.5.1 Options 261
8.5.2 Assessing the performance of volatility forecasting methods 269
8.5.3 Black-Scholes option pricing using a set of ARCH processes 270
8.5.4 Trading straddles based on a set of ARCH processes 271
8.5.5 Discussion 279
8.6 ARCH option pricing formulas 281
8.6.1 Computation of Duan s ARCH option prices: an example 286
Appendix 288
9 Implied volatility indices and ARCH models 341
9.1 Implied volatility 341
9.2 The VIX index 342
9.3 The implied volatility index as an explanatory variable 344
9.4 ARFIMAX model for implied volatility index 349
Appendix 352
10 ARCH model evaluation and selection 357
10.1 Evaluation of ARCH models 358
10.1.1 Model evaluation viewed in terms of information criteria 359
10.1.2 Model evaluation viewed in terms of statistical
loss functions 360
10.1.3 Consistent ranking 367
10.1.4 Simulation, estimation and evaluation 377
10.1.5 Point, interval and density forecasts 383
10.1.6 Model evaluation viewed in terms of loss functions based
on the use of volatility forecasts 384
x CONTENTS
10.2 Selection of ARCH models 386
10.2.1 The Diebold-Mariano test 386
10.2.2 The Harvey-Leybourne-Newbold test 389
10.2.3 The Morgan-Granger-Newbold test 389
10.2.4 White s reality check for data snooping 390
10.2.5 Hansen s superior predictive ability test 390
10.2.6 The standardized prediction error criterion 393
10.2.7 Forecast encompassing tests 400
10.3 Application of loss functions as methods of model selection 401
10.3.1 Applying the SPEC model selection method 401
10.3.2 Applying loss functions as methods of model selection 402
10.3.3 Median values of loss functions as methods
of model selection 407
10.4 The SPA test for VaR and expected shortfall 408
Appendix 410
11 Multivariate ARCH models 445
11.1 Model Specifications 446
11.1.1 Symmetric model specifications 446
11.1.2 Asymmetric and long-memory model specifications 453
11.2 Maximum likelihood estimation 454
11.3 Estimating multivariate ARCH models using EViews 6 456
11.4 Estimating multivariate ARCH models using G@RCH 5.0 465
11.5 Evaluation of multivariate ARCH models 473
Appendix 475
References 479
Author Index 521
Subject Index 533
|
| any_adam_object | 1 |
| author | Xekalaki, Evdokia Degiannakis, Stavros |
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| edition | 1. publ. |
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| id | DE-604.BV036459041 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T22:39:54Z |
| institution | BVB |
| isbn | 9780470066300 047006630X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020330974 |
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| owner_facet | DE-945 DE-1047 DE-20 |
| physical | XX, 538 S. graph. Darst. 1 CD-ROM (12 cm) |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Wiley |
| record_format | marc |
| spelling | Xekalaki, Evdokia Verfasser (DE-588)137511167 aut ARCH models for financial applications Evdokia Xekalaki ; Stavros Degiannakis 1. publ. Chichester Wiley 2010 XX, 538 S. graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Includes index Mathematisches Modell Finance / Mathematical models Autoregression (Statistics) Finanzmathematik (DE-588)4017195-4 gnd rswk-swf ARCH-Prozess (DE-588)4346437-3 gnd rswk-swf ARCH-Prozess (DE-588)4346437-3 s Finanzmathematik (DE-588)4017195-4 s b DE-604 Degiannakis, Stavros Verfasser (DE-588)136733158 aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020330974&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Xekalaki, Evdokia Degiannakis, Stavros ARCH models for financial applications Mathematisches Modell Finance / Mathematical models Autoregression (Statistics) Finanzmathematik (DE-588)4017195-4 gnd ARCH-Prozess (DE-588)4346437-3 gnd |
| subject_GND | (DE-588)4017195-4 (DE-588)4346437-3 |
| title | ARCH models for financial applications |
| title_auth | ARCH models for financial applications |
| title_exact_search | ARCH models for financial applications |
| title_full | ARCH models for financial applications Evdokia Xekalaki ; Stavros Degiannakis |
| title_fullStr | ARCH models for financial applications Evdokia Xekalaki ; Stavros Degiannakis |
| title_full_unstemmed | ARCH models for financial applications Evdokia Xekalaki ; Stavros Degiannakis |
| title_short | ARCH models for financial applications |
| title_sort | arch models for financial applications |
| topic | Mathematisches Modell Finance / Mathematical models Autoregression (Statistics) Finanzmathematik (DE-588)4017195-4 gnd ARCH-Prozess (DE-588)4346437-3 gnd |
| topic_facet | Mathematisches Modell Finance / Mathematical models Autoregression (Statistics) Finanzmathematik ARCH-Prozess |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020330974&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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