Handbook of volatility models and their applications:
Gespeichert in:
| Weitere Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ [u.a.]
Wiley
2012
|
| Schriftenreihe: | Wiley handbooks in financial engineering and econometrics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XX, 543 S. graph. Darst. |
| ISBN: | 9780470872512 0470872519 |
Internformat
MARC
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| 245 | 1 | 0 | |a Handbook of volatility models and their applications |c ed. by Luc Bauwens ... |
| 246 | 1 | 3 | |a Volatility models and their applications |
| 264 | 1 | |a Hoboken, NJ [u.a.] |b Wiley |c 2012 | |
| 300 | |a XX, 543 S. |b graph. Darst. | ||
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| 490 | 0 | |a Wiley handbooks in financial engineering and econometrics | |
| 650 | 4 | |a Banks and banking / Econometric models | |
| 650 | 4 | |a Finance / Econometric models | |
| 650 | 4 | |a GARCH model | |
| 650 | 7 | |a BUSINESS & ECONOMICS / Finance |2 bisacsh | |
| 650 | 4 | |a Bank | |
| 650 | 4 | |a Wirtschaft | |
| 650 | 4 | |a Ökonometrisches Modell | |
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Datensatz im Suchindex
| _version_ | 1804149026902245376 |
|---|---|
| adam_text | Contents
preface
xvii
Contributors
xix
яіі
volatility
models 1
1.1
Introduction,
1
1.2
GARCH, 1
1.2.1
Univariate GARCH,
1
1.2.1.1
Structure of GARCH Models,
3
1.2.1.2
Early GARCH Models,
5
1.2.1.3
Probability Distributions for zt,
7
1.2.1.4
New GARCH Models,
9
1.2.1.5
Explanation of Volatility
Clustering,
15
1.2.1.6
Literature and Software,
16
1.2.1.7
Applications of Univariate
GARCH,
16
1.2.2
Multivariate GARCH,
18
1.2.2.1
Structure of MGARCH Models,
19
1.2.2.2
Conditional Correlations,
19
1.2.2.3
Factor Models,
23
1.3
Stochastic Volatility,
25
1.3.1
Leverage Effect,
26
1.3.2
Estimation,
27
1.3.3
Multivariate SV Models,
28
1.3.4
Model Selection,
30
1.3.5
Empirical Example: S&P
500, 31
1.3.6
Literature,
32
1.4
Realized Volatility,
33
1.4.1
Realized Variance,
33
1.4.1.1
Empirical Application,
40
1.4.2
Realized Covariance,
44
vi
Contents
1.4.2.1
Realized Quadratic Covariation,
44
1.4.2.2
Realized Bipower Covariation,
44
Acknowledgments,
45
PART ONE
Autoregressive
Conditional Heteroskedasticity and
Stochastic Volatility
NONLINEAR MODELS FOR
AUTOREGRESSIVE
CONDITIONAL
HETEROSKEDASTICITY
49
2.1
Introduction,
49
2.2
The Standard GARCH Model,
50
2.3
Predecessors to Nonlinear GARCH Models,
51
2.4
Nonlinear ARCH and GARCH Models,
52
2.4.1
Engle s Nonlinear GARCH Model,
52
2.4.2
Nonlinear ARCH Model,
53
2.4.3
Asymmetric Power GARCH Model,
53
2.4.4
Smooth Transition GARCH Model,
54
2.4.5
Double Threshold ARCH Model,
56
2.4.6
Neural Network ARCH and GARCH Models,
57
2.4.7
Time-Varying GARCH,
58
2.4.8
Families of GARCH Models and their
Probabilistic Properties,
59
2.5
Testing Standard GARCH Against Nonlinear GARCH,
60
2.5.1
Size and Sign Bias Tests,
60
2.5*2
Testing GARCH Against Smooth Transition
GARCH,
61
2.5.3
Testing GARCH Against Artificial Neural
Network GARCH,
62
2.6
Estimation of Parameters in Nonlinear GARCH
Models,
63
2.6.1
Smooth Transition GARCH,
63
2.6.2
Neural Network GARCH,
64
2.7
Forecasting with Nonlinear GARCH Models,
64
2.7.1
Smooth Transition GARCH,
64
2.7.2
Asymmetric Power GARCH,
66
2.8
Models Based on Multiplicative Decomposition of the
Variance,
67
2.9
Conclusion,
68
Acknowledgments,
69
Contents
vii
jj§j
Mixture and Regime-Switching GARCH
Models
71
3.1
Introduction,
71
3.2
Regime-Switching GARCH Models for Asset Returns,
73
3.2.1
The Regime-Switching Framework,
73
3.2.2
Modeling the Mixing Weights,
75
3.2.3
Regime-Switching GARCH Specifications,
78
3.3
Stationarity and Moment Structure,
81
3.3.1
Stationarity,
83
3.3.2
Moment Structure,
87
3.4
Regime Inference, Likelihood Function, and Volatility
Forecasting,
89
3.4.1
Determining the Number of Regimes,
92
3.4.2
Volatility Forecasts,
92
3.4.3
Application of MS-GARCH Models to Stock
Return Indices,
93
3.5
Application of Mixture GARCH Models to Density
Prediction and Value-at-Risk Estimation,
97
3.5.1
Value-at-Risk,
97
3.5.2
Data and Models,
98
3.5.3
Empirical Results,
99
3.6
Conclusion,
102
Acknowledgments,
102
Ifijr Forecasting High
Dimensional
COVARIANCE MATRICES
1
O3
4.1
Introduction,
103
4.2
Notation,
104
4.3
Rolling Window Forecasts,
104
4.3.1
Sample Covariance,
105
4.3.2
Observable Factor Covariance,
105
4.3.3
Statistical Factor Covariance,
106
4.3.4
Equicorrelation,
107
4.3.5
Shrinkage Estimators,
108
4.4
Dynamic Models,
109
4.4.1
Covariance Targeting Scalar
VEC,
109
4.4.2
Flexible Multivariate GARCH,
110
4.4.3
Conditional Correlation GARCH Models, 111
4.4.4
Orthogonal GARCH,
113
4.4.5
RiskMetrics,
114
4.4.6
Alternative Estimators for Multivariate GARCH
Models,
116
viii Contents
4.5
High Frequency Based Forecasts,
117
4.5-1
Realized Covariance,
118
4.5.2
Mixed-Frequency Factor Model Covariance,
119
4.5.3
Regularization and Blocking Covariance,
119
4.6
Forecast Evaluation,
123
4.6.1
Portfolio Constraints,
124
4.7
Conclusion,
125
Acknowledgments,
125
Mean, Volatility, and Skewness
Spillovers in Equity Markets
127
5.1
Introduction,
127
5.2
Data and Summary Statistics,
129
5.2.1
Data,
129
5.2.2
Time-Varying Skewness (Univariate
Analysis),
132
5.2.3
Spillover Models,
135
5.3
Empirical Results,
138
5.3.1
Parameter Estimates,
138
5.3.2
Spillover Effects in Variance and Skewness,
139
5.3.2.1
Variance Ratios,
139
5.3.2.2
Pattern and Size of Skewness
Spillovers,
141
5.4
Conclusion,
144
Acknowledgments,
145
relating stochastic volatility
Estimation Methods
147
6.1
Introduction,
147
6.2
Theory and Methodology,
149
6.2.1
Quasi-Maximum Likelihood Estimation,
150
6.2.2
Gaussian Mixture Sampling,
151
6.2.3
Simulated Method of Moments,
152
6.2.4
Methods Based on Importance Sampling,
153
6.2.4.1
Approximating in the Basic IS
Approach,
154
6.2.4.2
Improving on IS with IIS,
155
6.2.4.3
Alternative Efficiency Gains with
EIS, 156
Contents ix
6.2.5 Alternative
Sampling Methods:
SSS
and
MMS,
158
6.3
Comparison of Methods,
160
6.3.1
Setup of Data-Generating Process and Estimation
Procedures,
160
6.3.2
Parameter Estimates for the Simulation,
161
6.3.3
Precision of IS,
163
6.3.4
Precision of Bayesian Methods,
164
6.4
Estimating Volatility Models in Practice,
165
6.4.1
Describing Return Data of Goldman Sachs and
IBM Stock,
165
6.4.2
Estimating SV Models,
167
6.4.3
Extracting Underlying Volatility,
168
6.4.4
Relating the Returns in a Bivariate Model,
169
6.5
Conclusion,
172
multivariate stochastic volatility
Models
175
7.1
Introduction,
175
7.2
MSV Model,
176
7.2.1
Model,
176
7.2.1.1
Likelihood Function,
177
7.2.1.2
Prior Distribution,
178
7.2.1.3
Posterior Distribution,
179
7.2.2
Bayesian Estimation,
179
7.2.2.1
Generation of a,
179
7.2.2.2
Generation of
φ,
181
7.2.2.3
Generation of
Σ,
181
7.2.3
Multivariate-ŕ
Errors,
181
7.2.3.1
Generation of
v,
182
7.2.3.2
Generation of
λ,
183
7.3
Factor MSV Model,
183
7.3.1
Model,
183
7.3.1.1
Likelihood Function,
184
7.3.1.2
Prior and Posterior Distributions,
185
7.3.2
Bayesian Estimation,
185
7.3.2.1
Generation of
α, φ,
and
Σ,
186
7.3.2.2
Generation of/,
187
7.3.2.3
Generation of
λ,
187
7.3.2.4
Generation of
β,
188
Contents
7.3.2.5 Generation
of v,
188
7.4 Applications
to
Stock
Indices Returns,
188
7.4.1
S&P
500
Sector Indices,
188
7.4.2
MSV Model with Multivariate
t
Errors,
189
7.4.2.1
Prior Distributions,
189
7.4.2.2
Estimation Results,
189
7.4.3
Factor MSV Model,
192
7.4.3.1
Prior Distributions,
192
7.4.3.2
Estimation Results,
192
7.5
Conclusion,
195
7.6
Appendix: Sampling
α
in the MSV Model,
195
7.6.1
Single-Move Sampler,
195
7.6.2
Multi-move Sampler,
196
Model Selection and Testing of
Conditional and Stochastic Volatility
Models
199
8.1
Introduction,
199
8.1.1
Model Specifications,
200
8.2
Model Selection and Testing,
202
8.2.1
In-Sample Comparisons,
202
8.2.2
Out-of-Sample Comparisons,
206
8.2.2.1
Direct Model Evaluation,
206
8.2.2.2
Indirect Model Evaluation,
209
8.3
Empirical Example,
211
8.4
Conclusion,
221
Other Models and Methods
Multiplicative Error Models
225
9.1
Introduction,
225
9.2
Theory and Methodology,
226
9.2.1
Model Formulation,
226
9.2.1.1
Specifications for
μ,,
227
9.2.1.2
Specifications for et,
230
9.2.2
Inference,
230
9.2.2.1
Maximum Likelihood Inference,
230
9.2.2.2
Generalized Method of Moments
Inference,
233
9.3
MEMs for Realized Volatility,
235
9.4
MEM Extensions,
242
Contents
9.4.1
Component
Multiplicative
Error Model,
242
9.4.2
Vector Multiplicative Error Model,
243
9.5
Conclusion,
247
Locally Stationary Volatility
Modeling
249
10.1
Introduction,
249
10.2
Empirical Evidences,
251
10.2.1
Structural Breaks, Nonstationarity, and
Persistence,
251
10.2.2
Testing Stationarity,
253
10.3
Locally Stationary Processes and their Time-Varying
Autocovariance Function,
256
10.4
Locally Stationary Volatility Models,
260
10.4.1
Multiplicative Models,
260
10.4.2
Time-Varying ARCH Processes,
261
10.4.3
Adaptive Approaches,
264
10.5
Multivariate Models for Locally Stationary Volatility,
266
10.5.1
Multiplicative Models,
266
10.5.2
Adaptive Approaches,
267
10.6
Conclusions,
267
Acknowledgments,
268
Nonparametric and Semiparametric
volatility models: Specification,
Estimation, and Testing
269
11.1
Introduction,
269
11.2
Nonparametric and Semiparametric Univariate Volatility
Models,
271
11.2.1
Stationary Volatility Models,
271
11.2.1.1
The Simplest Nonparametric Volatility
Model,
271
11.2.1.2
Additive Nonparametric Volatility
Model,
273
11.2.1.3
Functional-Coefficient Volatility
Model,
276
11.2.1.4
Single-Index Volatility Model,
277
11.2.1.5
Stationary Semiparametric ARCH (oo
)
Models,
278
11.2.1.6
Semiparametric Combined Estimator
ofVolatility,
279
Contents
11.2.1.7 Semiparametric
Inference in
GARCH-in-Mean Models, 280
11.2.2 Nonstationary Univariate
Volatility
Models, 281
11.2.3
Specification of the Error Density,
282
11.2.4
Nonparametric Volatility Density Estimation,
283
11.3
Nonparametric and Semiparametric Multivariate Volatility
Models,
284
11.3.1
Modeling the Conditional Covariance Matrix
under Stationarity,
285
11.3.1.1 Hafner,
van
Dijk,
and
Frånses
Semiparametric Estimator,
285
11.3.1.2
Long,
Su,
and Ullah s Semiparametric
Estimator,
286
11.3.1.3
Test for the Correct Specification of
Parametric Conditional Covariance
Models,
286
11.3.2
Specification of the Error Density,
287
11.4
Empirical Analysis,
288
11.5
Conclusion,
291
Acknowledgments,
291
Copula-Based volatility Models
293
12.1
Introduction,
293
12.2
Definition and Properties of Copulas,
294
12.2.1
Sklar s Theorem,
295
12.2.2
Conditional Copula,
296
12.2.3
Some Commonly Used Bivariate Copulas,
296
12.2.4
Copula-Based Dependence Measures,
298
12.3
Estimation,
300
12.3.1
Exact Maximum Likelihood,
300
12.3.2
IFM,
301
12.3.3
Bivariate Static Copula Models,
301
12.4
Dynamic Copulas,
304
12.4.1
Early Approaches,
305
12.4.2
Dynamics Based on the
DCC
Model,
305
12.4.3
Alternative Methods,
307
12.5
Value-at-Risk,
308
12.6
Multivariate Static Copulas,
310
12.6.1
Multivariate Archimedean Copulas,
310
12.6.2
Vines,
313
12.7
Conclusion,
315
Contents xiii
PART THREE
Realized Volatility
Realized volatility: Theory and
Applications
319
13.1
Introduction,
319
13.2
Modeling Framework,
320
13.2.1
Efficient Price,
320
13.2.2
Measurement Error,
322
13.3
Issues in Handling Intraday Transaction Databases,
323
13.3.1
Which Price to Use?,
324
13.3.2
High Frequency Data Preprocessing,
326
13.3.3
How to and How Often to Sample?,
326
13.4
Realized Variance and Covariance,
329
13.4.1
Univariate Volatility Estimators,
329
13.4.1.1
Measurement Error,
330
13.4.2
Multivariate Volatility Estimators,
333
13.4.2.1
Measurement Error,
336
13.5
Modeling and Forecasting,
337
13.5.1
Time Series Models of
(co)
Volatility,
337
13.5.2
Forecast Comparison,
339
13.6
Asset Pricing,
340
13.6.1
Distribution of Returns Conditional on the
Volatility Measure,
340
13.6.2
Application to Factor Pricing Model,
341
13.6.3
Effects of Algorithmic Trading,
342
13.6.4
Application to Option Pricing,
342
13.7
Estimating Continuous Time Models,
344
Likelihood-Based Volatility
Estimators in the Presence of market
Microstructure
Noise
347
14.1
Introduction,
347
14.2
Volatility Estimation,
349
14.2.1
Constant Volatility and Gaussian Noise Case:
MLE,
349
14.2.2
Robustness to Non-Gaussian Noise,
351
14.2.3
Implementing Maximum Likelihood,
351
14.2.4
Robustness to Stochastic Volatility: QMLE,
352
14.2.5
Comparison with Other Estimators,
355
14.2.6
Random Sampling and Non-i.i.d. Noise,
356
14.3
Covariance Estimation,
356
xiv Contents
14.4
Empirical
Application:
Correlation between Stock and
Commodity Futures,
359
14.5
Conclusion,
360
Acknowledgments,
361
јКЦ
HAR
Modeling for Realized volatility
Forecasting
363
15.1
Introduction,
363
15.2
Stylized Facts on Realized Volatility,
365
15.3
Heterogeneity and Volatility Persistence,
366
15.3.1
Genuine Long Memory or Superposition of
Factors?,
369
15.4
HAR
Extensions,
370
15.4.1
Jump Measures and Their Volatility Impact,
370
15.4.2
Leverage Effects,
372
15.4.3
General Nonlinear Effects in Volatility,
373
15.5
Multivariate Models,
375
15.6
Applications,
378
15.7
Conclusion,
381
ІЩ
Forecasting Volatility with MIDAS
383
16.1
Introduction,
383
16.2
MIDAS Regression Models and Volatility Forecasting,
384
16.2.1
MIDAS Regressions,
384
16.2.2
Direct Versus Iterated Volatility Forecasting,
386
16.2.3
Variations on the Theme of MIDAS
Regressions,
389
16.2.4
Microstructure
Noise and MIDAS
Regressions,
390
16.3
Likelihood-Based Methods,
391
16.3.1
Risk-Return Trade-Off,
391
16.3.2
HYBRID GARCH Models,
393
16.3.3
GARCH-MIDAS Models,
398
16.4
Multivariate Models,
399
16.5
Conclusion,
401
Щ
Jumps
4ОЗ
17.1
Introduction,
403
17.1.1
Some Models Used in Finance and Our
Framework,
403
17.1.2
Simulated Models Used in This Chapter,
407
17.1.3
Realized Variance and Quadratic Variation,
409
Contents
17.1.4
Importance
of Disentangling,
410
17.1.5
Further Notation,
411
17.2
How to Disentangle: Estimators of Integrated Variance and
Integrated Covariance,
411
17.2.1
Bipower Variation,
413
17.2.2
Threshold Estimator,
416
17.2.3
Threshold Bipower Variation,
419
17.2.4
Other Methods,
421
17.2.4.1
Realized Quantile,
421
17.2.4.2
MinRVandMedRV,
422
17.2.4.3
Realized Outlyingness Weighted
Variation,
422
17.2.4.4
Range Bipower Variation,
423
17.2.4.5
Generalization of the Realized
Range,
424
17.2.4.6
Duration-Based Variation,
425
17.2.4.7
Irregularly Spaced Observations,
425
17-2.5
Comparative Implementation on Simulated
Data,
426
17.2.6
Noisy Data,
427
17.2.7
Multivariate Assets,
432
17.3
Testing for the Presence of Jumps,
433
17.3.1
Confidence Intervals,
434
17.3.2
Tests Based on
IV„ - RV„
or on
1 -
rVRV,,,
434
17.3.3
Tests Based on Normalized Returns,
436
17.3.4
PV-Based Tests,
439
17.3.4.1
Remarks,
440
17.3.5
Tests Based on Signature Plots,
441
17.3.6
Tests Based on Observation of Option Prices,
442
17.3.6.1
Remarks,
442
17.3-7
Indirect Test for the Presence of Jumps,
443
17.3.7.1
In the Presence of Noise,
443
17.3.8
Comparisons,
443
17.4
Conclusions,
444
Acknowledgments,
445
nonparametric tests for intraday
Jumps: Impact of Periodicity and
Microstructure
Noise
447
18.1
Introduction,
447
18.2
Model,
449
18.3
Price Jump Detection Method,
450
xvi Contents
18.3.1
Estimation
of the Noise Variance,
451
18.3.2
Robust Estimators of the Integrated Variance,
451
18.3.3
Periodicity Estimation,
452
18.3.4
Jump Test Statistics,
454
18.3.5
Critical Value,
454
18.4
Simulation Study,
455
18.4.1
Intraday Differences in the Value of the Test
Statistics,
455
18.4.2
Comparison of Size and Power,
457
18.4.3
Simulation Setup,
457
18.4.4
Results,
458
18.5
Comparison on NYSE Stock Prices,
460
18.6
Conclusion,
462
Volatility Forecasts Evaluation and
Comparison
465
19.1
Introduction,
465
19.2
Notation,
467
19.3
Single Forecast Evaluation,
468
19.4
Loss Functions and the Latent Variable Problem,
471
19.5
Pairwise Comparison,
474
19.6
Multiple Comparison,
477
19.7
Consistency of the Ordering and Inference on Forecast
Performances,
481
19.8
Conclusion,
485
Bibliography
487
Index
537
|
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| dewey-search | 332.01/5195 |
| dewey-sort | 3332.01 45195 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV040030422 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:16:29Z |
| institution | BVB |
| isbn | 9780470872512 0470872519 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-024887322 |
| oclc_num | 759177354 |
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| physical | XX, 543 S. graph. Darst. |
| publishDate | 2012 |
| publishDateSearch | 2012 |
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| publisher | Wiley |
| record_format | marc |
| series2 | Wiley handbooks in financial engineering and econometrics |
| spelling | Handbook of volatility models and their applications ed. by Luc Bauwens ... Volatility models and their applications Hoboken, NJ [u.a.] Wiley 2012 XX, 543 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Wiley handbooks in financial engineering and econometrics Banks and banking / Econometric models Finance / Econometric models GARCH model BUSINESS & ECONOMICS / Finance bisacsh Bank Wirtschaft Ökonometrisches Modell Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Volatilität (DE-588)4268390-7 gnd rswk-swf Finanzierung (DE-588)4017182-6 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Finanzierung (DE-588)4017182-6 s Zeitreihenanalyse (DE-588)4067486-1 s Volatilität (DE-588)4268390-7 s Mathematisches Modell (DE-588)4114528-8 s b DE-604 Bauwens, Luc 1952- (DE-588)17029837X edt Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024887322&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Handbook of volatility models and their applications Banks and banking / Econometric models Finance / Econometric models GARCH model BUSINESS & ECONOMICS / Finance bisacsh Bank Wirtschaft Ökonometrisches Modell Zeitreihenanalyse (DE-588)4067486-1 gnd Volatilität (DE-588)4268390-7 gnd Finanzierung (DE-588)4017182-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4268390-7 (DE-588)4017182-6 (DE-588)4114528-8 (DE-588)4143413-4 |
| title | Handbook of volatility models and their applications |
| title_alt | Volatility models and their applications |
| title_auth | Handbook of volatility models and their applications |
| title_exact_search | Handbook of volatility models and their applications |
| title_full | Handbook of volatility models and their applications ed. by Luc Bauwens ... |
| title_fullStr | Handbook of volatility models and their applications ed. by Luc Bauwens ... |
| title_full_unstemmed | Handbook of volatility models and their applications ed. by Luc Bauwens ... |
| title_short | Handbook of volatility models and their applications |
| title_sort | handbook of volatility models and their applications |
| topic | Banks and banking / Econometric models Finance / Econometric models GARCH model BUSINESS & ECONOMICS / Finance bisacsh Bank Wirtschaft Ökonometrisches Modell Zeitreihenanalyse (DE-588)4067486-1 gnd Volatilität (DE-588)4268390-7 gnd Finanzierung (DE-588)4017182-6 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Banks and banking / Econometric models Finance / Econometric models GARCH model BUSINESS & ECONOMICS / Finance Bank Wirtschaft Ökonometrisches Modell Zeitreihenanalyse Volatilität Finanzierung Mathematisches Modell Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024887322&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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