Financial econometrics using Stata:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
College Station, Texas
Stata Press LP
2016
|
| Ausgabe: | First edition |
| Schlagwörter: | |
| Links: | http://scans.hebis.de/HEBCGI/show.pl?39745399_toc.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029257434&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029257434&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Literaturverzeichnis Seite 261-265 |
| Umfang: | XIV, 272 Seiten Diagramme |
| ISBN: | 9781597182140 |
Internformat
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| 245 | 1 | 0 | |a Financial econometrics using Stata |c Simona Boffelli (University of Bergamo (Italy) and Centre for Econometric Analysis, Cass Business School, City University London (UK)), Giovanni Urga (Centre for Econometric Analysis, Cass Business School, City University London (UK) and University of Bergamo (Italy)) |
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Datensatz im Suchindex
| _version_ | 1819245324722503680 |
|---|---|
| adam_text | Contents
List of figures ix
Preface xiii
Notation and typography xv
1 Introduction to financial time series 1
1.1 The object of interest .............................................. 1
1.2 Approaching the dataset.............................................. 2
1.3 Normality............................................................ 8
1.4 Stationarity........................................................ 14
1.4.1 Stationarity tests.......................................... 17
1.5 Autocorrelation.................................................... 22
1.5.1 ACF......................................................... 22
1.5.2 PACF........................................................ 25
1.6 Heteroskedasticity ................................................. 26
1.7 Linear time series............................................... 30
1.8 Model selection..................................................... 31
l.A How to import data ............................................. 33
2 ARMA models 37
2.1 Autoregressive (AR) processes....................................... 37
2.1.1 AR(1)....................................................... 37
2.1.2 AR(p)....................................................... 46
2.2 Moving-average (MA) processes....................................... 47
2.2.1 MA(1)....................................................... 47
2.2.2 MA(q) ...................................................... 53
2.2.3 Invert ibility.............................................. 54
VI
Contents
2.3 Autoregressive moving-average (ARMA) processes.............. 54
2.3.1 ARMA(M).............................................. 54
2.3.2 ARMA(p,q)............................................ 58
2.3.3 ARIMA................................................ 58
2.3.4 ARMAX................................................ 58
2.4 Application of ARMA models.................................... 58
2.4.1 Model estimation..................................... 61
2.4.2 Postestimation ...................................... 70
2.4.3 Adding a dummy variable.............................. 75
2.4.4 Forecasting.......................................... 78
3 Modeling volatilities, ARCH models, and GARCH models 81
3.1 Introduction.................................................. 81
3.2 ARCH models................................................... 82
3.2.1 General options........................................ 85
ARCH................................................... 85
Distribution......................................... 86
3.2.2 Additional options..................................... 91
ARIMA.................................................. 91
The het() option ...................................... 92
The maximize-options options........................... 94
3.2.3 Postestimation ...................................... 95
3.3 ARCH(p)....................................................... 99
3.4 GARCH models................................................. 101
3.4.1 GARCH(p,q) ........................................... 101
3.4.2 GARCH in mean......................................... HO
3.4.3 Forecasting........................................... Ill
3.5 Asymmetric GARCH models.................................... 114
3.5.1 SAARCH ............................................... 116
3.5.2 TGARCH................................................ 116
3.5.3 GJR-GARCH............................................. 117
Contents vii
3.5.4 APARCH.................................................... 118
3.5.5 News impact curve........................................ 121
3.5.6 Forecasting comparison.................................... 123
3.6 Alternative GARCH models................................... 126
3.6.1 PARCH.................................................... 126
3.6.2 NGARCH................................................... 127
3.6.3 NGARCHK.................................................. 128
4 Multivariate GARCH models 131
4.1 Introduction............................................... 131
4.2 Multivariate GARCH......................................... 132
4.3 Direct generalizations of the univariate GARCH model of Bollerslev 134
4.3.1 Vech model............................................... 134
4.3.2 Diagonal vech model............................... . . 136
4.3.3 BEKK model................................................ 137
4.3.4 Empirical application..................................... 138
Data description ........................................ 138
Dvech model........................................ 142
4.4 Nonlinear combination of univariate GARCH—-common features . . 148
4.4.1 Constant conditional correlation (CCC) GARCH.............. 149
Empirical application.............................. 151
4.4.2 Dynamic conditional correlation (DCC) model............... 158
Dynamic conditional correlation Engle (DCCE) model ... 158
Empirical application.............................. 160
Dynamic conditional correlation Tse and Tsui (DCCT) ... 174
Prediction.............................................. 183
4.5 Final remarks.............................................. 186
5 Risk management 187
5.1 Introduction............................................... 187
5.2 Loss....................................................... 188
5.3 Risk measures.............................................. 189
viii Contents
5.4 VaR.............................................................. 190
5.4.1 VaR estimation.......................................... 191
5.4.2 Parametric approach..................................... 191
5.4.3 Historical simulation.................................... 200
5.4.4 Monte Carlo simulation .................................. 210
5.4.5 Expected shortfall........................................210
5.5 Backtesting procedures........................................... 217
5.5.1 Unilevel VaR tests....................................... 218
The unconditional coverage test.......................... 218
The independence test.................................... 221
The conditional coverage test............................ 223
The duration tests....................................... 224
G Contagion analysis 227
0.1 Introduction..................................................... 227
G.2 Contagion measurement............................................ 229
G.2.1 Cross-market correlation coefficients ................... 229
Empirical exercise....................................... 231
0.2.2 ARCH and GARCII models....................................230
Empirical exercise....................................... 238
Markov switching........................................ 2 13
G.2.3 Higher moments contagion................................. 251
Empirical exercise....................................... 252
Glossary of acronyms 259
References 201
Author index 207
Subject index 209
Contents
List of figures ix
Preface xiii
Notation and typography xv
1 Introduction to financial time series 1
1.1 The object of interest .............................................. 1
1.2 Approaching the dataset.............................................. 2
1.3 Normality............................................................ 8
1.4 Stationarity........................................................ 14
1.4.1 Stationarity tests.......................................... 17
1.5 Autocorrelation.................................................... 22
1.5.1 ACF......................................................... 22
1.5.2 PACF........................................................ 25
1.6 Heteroskedasticity ................................................. 26
1.7 Linear time series............................................... 30
1.8 Model selection..................................................... 31
l.A How to import data ............................................. 33
2 ARMA models 37
2.1 Autoregressive (AR) processes....................................... 37
2.1.1 AR(1)....................................................... 37
2.1.2 AR(p)....................................................... 46
2.2 Moving-average (MA) processes....................................... 47
2.2.1 MA(1)....................................................... 47
2.2.2 MA(q) ...................................................... 53
2.2.3 Invert ibility.............................................. 54
VI
Contents
2.3 Autoregressive moving-average (ARMA) processes.............. 54
2.3.1 ARMA(M).............................................. 54
2.3.2 ARMA(p,q)............................................ 58
2.3.3 ARIMA................................................ 58
2.3.4 ARMAX................................................ 58
2.4 Application of ARMA models.................................... 58
2.4.1 Model estimation..................................... 61
2.4.2 Postestimation ...................................... 70
2.4.3 Adding a dummy variable.............................. 75
2.4.4 Forecasting.......................................... 78
3 Modeling volatilities, ARCH models, and GARCH models 81
3.1 Introduction.................................................. 81
3.2 ARCH models................................................... 82
3.2.1 General options........................................ 85
ARCH................................................... 85
Distribution......................................... 86
3.2.2 Additional options..................................... 91
ARIMA.................................................. 91
The het() option ...................................... 92
The maximize-options options........................... 94
3.2.3 Postestimation ...................................... 95
3.3 ARCH(p)....................................................... 99
3.4 GARCH models................................................. 101
3.4.1 GARCH(p,q) ........................................... 101
3.4.2 GARCH in mean......................................... HO
3.4.3 Forecasting........................................... Ill
3.5 Asymmetric GARCH models.................................... 114
3.5.1 SAARCH ............................................... 116
3.5.2 TGARCH................................................ 116
3.5.3 GJR-GARCH............................................. 117
Contents vii
3.5.4 APARCH.................................................... 118
3.5.5 News impact curve........................................ 121
3.5.6 Forecasting comparison.................................... 123
3.6 Alternative GARCH models................................... 126
3.6.1 PARCH.................................................... 126
3.6.2 NGARCH................................................... 127
3.6.3 NGARCHK.................................................. 128
4 Multivariate GARCH models 131
4.1 Introduction............................................... 131
4.2 Multivariate GARCH......................................... 132
4.3 Direct generalizations of the univariate GARCH model of Bollerslev 134
4.3.1 Vech model............................................... 134
4.3.2 Diagonal vech model............................... . . 136
4.3.3 BEKK model................................................ 137
4.3.4 Empirical application..................................... 138
Data description ........................................ 138
Dvech model........................................ 142
4.4 Nonlinear combination of univariate GARCH—-common features . . 148
4.4.1 Constant conditional correlation (CCC) GARCH.............. 149
Empirical application.............................. 151
4.4.2 Dynamic conditional correlation (DCC) model............... 158
Dynamic conditional correlation Engle (DCCE) model ... 158
Empirical application.............................. 160
Dynamic conditional correlation Tse and Tsui (DCCT) ... 174
Prediction.............................................. 183
4.5 Final remarks.............................................. 186
5 Risk management 187
5.1 Introduction............................................... 187
5.2 Loss....................................................... 188
5.3 Risk measures.............................................. 189
viii Contents
5.4 VaR.............................................................. 190
5.4.1 VaR estimation.......................................... 191
5.4.2 Parametric approach..................................... 191
5.4.3 Historical simulation.................................... 200
5.4.4 Monte Carlo simulation .................................. 210
5.4.5 Expected shortfall........................................210
5.5 Backtesting procedures........................................... 217
5.5.1 Unilevel VaR tests....................................... 218
The unconditional coverage test.......................... 218
The independence test.................................... 221
The conditional coverage test............................ 223
The duration tests....................................... 224
G Contagion analysis 227
0.1 Introduction..................................................... 227
G.2 Contagion measurement............................................ 229
G.2.1 Cross-market correlation coefficients ................... 229
Empirical exercise....................................... 231
0.2.2 ARCH and GARCII models....................................230
Empirical exercise....................................... 238
Markov switching........................................ 2 13
G.2.3 Higher moments contagion................................. 251
Empirical exercise....................................... 252
Glossary of acronyms 259
References 201
Author index 207
Subject index 209
|
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| dewey-tens | 330 - Economics |
| discipline | Informatik Wirtschaftswissenschaften |
| edition | First edition |
| format | Book |
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| id | DE-604.BV043847005 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:46:41Z |
| institution | BVB |
| isbn | 9781597182140 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029257434 |
| oclc_num | 968684296 |
| open_access_boolean | |
| owner | DE-522 DE-1047 DE-355 DE-BY-UBR DE-945 DE-739 DE-92 DE-N2 |
| owner_facet | DE-522 DE-1047 DE-355 DE-BY-UBR DE-945 DE-739 DE-92 DE-N2 |
| physical | XIV, 272 Seiten Diagramme |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | Stata Press LP |
| record_format | marc |
| spellingShingle | Boffelli, Simona Urga, Giovanni Financial econometrics using Stata Ökonometrie (DE-588)4132280-0 gnd Stata (DE-588)4617285-3 gnd Anwendung (DE-588)4196864-5 gnd |
| subject_GND | (DE-588)4132280-0 (DE-588)4617285-3 (DE-588)4196864-5 |
| title | Financial econometrics using Stata |
| title_auth | Financial econometrics using Stata |
| title_exact_search | Financial econometrics using Stata |
| title_full | Financial econometrics using Stata Simona Boffelli (University of Bergamo (Italy) and Centre for Econometric Analysis, Cass Business School, City University London (UK)), Giovanni Urga (Centre for Econometric Analysis, Cass Business School, City University London (UK) and University of Bergamo (Italy)) |
| title_fullStr | Financial econometrics using Stata Simona Boffelli (University of Bergamo (Italy) and Centre for Econometric Analysis, Cass Business School, City University London (UK)), Giovanni Urga (Centre for Econometric Analysis, Cass Business School, City University London (UK) and University of Bergamo (Italy)) |
| title_full_unstemmed | Financial econometrics using Stata Simona Boffelli (University of Bergamo (Italy) and Centre for Econometric Analysis, Cass Business School, City University London (UK)), Giovanni Urga (Centre for Econometric Analysis, Cass Business School, City University London (UK) and University of Bergamo (Italy)) |
| title_short | Financial econometrics using Stata |
| title_sort | financial econometrics using stata |
| topic | Ökonometrie (DE-588)4132280-0 gnd Stata (DE-588)4617285-3 gnd Anwendung (DE-588)4196864-5 gnd |
| topic_facet | Ökonometrie Stata Anwendung |
| url | http://scans.hebis.de/HEBCGI/show.pl?39745399_toc.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029257434&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029257434&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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