Analysis of financial time series:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, N.J.
Wiley
[2010]
|
| Ausgabe: | Third edition |
| Schriftenreihe: | Wiley series in probability and statistics
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxiii, 677 Seiten Illustrationen, Diagramme |
| ISBN: | 9780470414354 0470414359 |
Internformat
MARC
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| 020 | |a 9780470414354 |c hbk |9 978-0-470-41435-4 | ||
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| 100 | 1 | |a Tsay, Ruey S. |d 1951- |e Verfasser |0 (DE-588)13189160X |4 aut | |
| 245 | 1 | 0 | |a Analysis of financial time series |c Ruey S. Tsay, The University of Chicago, Booth School of Business, Chicago, IL |
| 250 | |a Third edition | ||
| 264 | 1 | |a Hoboken, N.J. |b Wiley |c [2010] | |
| 264 | 4 | |c © 2010 | |
| 300 | |a xxiii, 677 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Wiley series in probability and statistics | |
| 650 | 4 | |a Time-series analysis | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Risk management | |
| 650 | 4 | |a Econometrics | |
| 650 | 4 | |a Risk management | |
| 650 | 4 | |a Time-series analysis | |
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Datensatz im Suchindex
| DE-BY-862_location | 2000 |
|---|---|
| DE-BY-FWS_call_number | 2000/QH 237 T877 |
| DE-BY-FWS_katkey | 699048 |
| DE-BY-FWS_media_number | 083000520572 |
| _version_ | 1824553490491375616 |
| adam_text | Contents
Preface
xvii
Preface
to the Second Edition
xix
Preface to the First Edition
xxi
1
Financial Time Series and Their Characteristics
1
1.1
Asset Returns,
2
1.2
Distributional Properties of Returns,
7
1.2.1
Review of Statistical Distributions and Their Moments,
7
1.2.2
Distributions of Returns,
14
1.2.3
Multivariate Returns,
18
1.2.4
Likelihood Function of Returns,
19
1.2.5
Empirical Properties of Returns,
19
1.3
Processes Considered,
22
Appendix:
R
Packages,
24
Exercises,
25
References,
27
2
Linear Time
Seríes
Analysis and Its Applications
29
2.1
Stationarity,
30
2.2
Correlation and Autocorrelation Function,
30
2.3
White Noise and Linear Time Series,
36
2.4
Simple
AR
Models,
37
2.4.1
Properties of
AR
Models,
38
2.4.2
Identifying
AR
Models in Practice,
46
2.4.3
Goodness of Fit,
53
2.4.4
Forecasting,
54
vii
VIU
CONTENTS
2.5
Simple
MA
Models, 57
2.5.1
Properties of
MA
Models, 59
2.5.2
Identifying MA Order,
60
2.5.3
Estimation,
61
2.5.4
Forecasting Using MA Models,
62
2.6
Simple
ARMA
Models,
64
2.6.1
Properties of ARMA(
1,1)
Models,
64
2.6.2
General
ARMA
Models,
66
2.6.3
Identifying
ARMA
Models,
66
2.6.4
Forecasting Using an
ARMA
Model,
68
2.6.5
Three Model Representations for an
ARMA
Model,
69
2.7
Unit-Root Nonstationarity,
71
2.7.1
Random Walk,
72
2.7.2
Random Walk with Drift,
73
2.7.3
Trend-Stationary Time Series,
75
2.7.4
General Unit-Root Nonstationary Models,
75
2.7.5
Unit-Root Test,
76
2.8
Seasonal Models,
81
2.8.1
Seasonal Differencing,
82
2.8.2
Multiplicative Seasonal Models,
84
2.9
Regression Models with Time Series Errors,
90
2.10
Consistent Covariance Matrix Estimation,
97
2.11
Long-Memory Models,
101
Appendix: Some
SCA
Commands,
103
Exercises,
104
References,
107
3
Conditional Heteroscedastic Models
109
3.1
Characteristics of Volatility,
110
3.2
Structure of a Model,
111
3.3
Model Building,
113
3.3.1
Testing for ARCH Effect,
114
3.4
The ARCH Model,
115
3.4.1
Properties of ARCH Models,
117
3.4.2
Weaknesses of ARCH Models,
119
3.4.3
Building an ARCH Model,
119
3.4.4
Some Examples,
123
3.5
The GARCH Model,
131
3.5.1
An Illustrative Example,
134
CONTENTS
ІХ
3.5.2
Forecasting
Evaluation, 139
3.5.3
A Two-Pass Estimation Method,
140
3.6
The Integrated GARCH Model,
140
3.7
The GARCH-M Model,
142
3.8
The Exponential GARCH Model,
143
3.8.1
Alternative Model Form,
144
3.8.2
Illustrative Example,
145
3.8.3
Second Example,
145
3.8.4
Forecasting Using an EGARCH Model,
147
3.9
The Threshold GARCH Model,
149
3.10
The
CHARMA
Model,
150
3.10.1
Effects of Explanatory Variables,
152
3.11
Random Coefficient
Autoregressive
Models,
152
3.12
Stochastic Volatility Model,
153
3.13
Long-Memory Stochastic Volatility Model,
154
3.14
Application,
155
3.15
Alternative Approaches,
159
3.15.1
Use of High-Frequency Data,
159
3.15.2
Use of Daily Open, High, Low, and Close Prices,
162
3.16
Kurtosis of GARCH Models,
165
Appendix: Some RATS Programs for Estimating Volatility Models,
167
Exercises,
168
References,
171
4
Nonlinear Models and Their Applications
175
4.1
Nonlinear Models,
177
.1
Bilinear Model,
177
.2
Threshold
Autoregressive
(TAR) Model,
179
.3
Smooth Transition
AR
(STAR) Model,
184
.4
Markov Switching Model,
186
.5
Nonparametric Methods,
189
4.1.6
Functional Coefficient
AR
Model,
198
4.1.7
Nonlinear Additive
AR
Model,
198
4.1.8
Nonlinear State-Space Model,
199
4.1.9
Neural Networks,
199
4.2
Nonlinearity Tests,
205
4.2.1
Nonparametric Tests,
206
4.2.2
Parametric Tests,
209
4.2.3
Applications,
213
CONTENTS
4.3
Modeling,
214
4.4
Forecasting,
215
4.4.1
Parametric Bootstrap,
215
4.4.2
Forecasting Evaluation,
215
4.5
Application,
218
Appendix A: Some RATS Programs for Nonlinear Volatility Models,
222
Appendix
B: R
and S-Plus Commands for Neural Network,
223
Exercises,
224
References,
226
ffigh-Frequency Data Analysis and Market
Microstructure
231
5.1
Nonsynchronous Trading,
232
5.2
Bid-Ask Spread,
235
5.3
Empirical Characteristics of Transactions Data,
237
5.4
Models for Price Changes,
244
5.4.1
Ordered
Probit
Model,
245
5.4.2
Decomposition Model,
248
5.5
Duration Models,
253
5.5.1
The ACD Model,
255
5.5.2
Simulation,
257
5.5.3
Estimation,
260
5.6
Nonlinear Duration Models,
264
5.7
Divariate
Models for Price Change and Duration,
265
5.8
Application,
270
Appendix A: Review of Some Probability Distributions,
276
Appendix B: Hazard Function,
279
Appendix C: Some RATS Programs for Duration Models,
280
Exercises,
282
References,
284
Continuous-Time Models and Their Applications
287
6.1
Options,
288
6.2
Some Continuous-Time Stochastic Processes,
288
6.2.1
Wiener Process,
289
6.2.2
Generalized Wiener Process,
291
6.2.3
Ito
Process,
292
6.3
Ito
s
Lemma,
292
6.3.1
Review of Differentiation,
292
CONTENTS
ХІ
6.3.2
Stochastic
Differentiation, 293
6.3.3 An Application, 294
6.3.4
Estimation of
μ
and
σ,
295
6.4
Distributions of Stock Prices and Log Returns,
297
6.5
Derivation of Black-Scholes Differential Equation,
298
6.6
Black-Scholes Pricing Formulas,
300
6.6.1
Risk-Neutral World,
300
6.6.2
Formulas,
300
6.6.3
Lower Bounds of European Options,
304
6.6.4
Discussion,
305
6.7
Extension of Ito s Lemma,
309
6.8
Stochastic Integral,
310
6.9
Jump Diffusion Models,
311
6.9.1
Option Pricing under Jump Diffusion,
315
6.10
Estimation of Continuous-Time Models,
318
Appendix A: Integration of Black-Scholes Formula,
319
Appendix B: Approximation to Standard Normal Probability,
320
Exercises,
321
References,
322
7
Extreme Values,
Quantités,
and Value at Risk
325
7.1
Value at Risk,
326
7.2
RiskMetrics,
328
7.2.1
Discussion,
331
7.2.2
Multiple Positions,
332
7.2.3
Expected Shortfall,
332
7.3
Econometric Approach to VaR Calculation,
333
7.3.1
Multiple Periods,
336
7.3.2
Expected Shortfall under Conditional Normality,
338
7.4
Quantile Estimation,
338
7.4.1
Quantile and Order Statistics,
338
7.4.2
Quantile Regression,
341
7.5
Extreme Value Theory,
342
7.5.1
Review of Extreme Value Theory,
342
7.5.2
Empirical Estimation,
345
7.5.3
Application to Stock Returns,
348
7.6
Extreme Value Approach to VaR,
353
7.6.1
Discussion,
356
ХИ
CONTENTS
7.6.2 Multiperiod VaR, 357
7.6.3 Return Level, 358
7.7 New
Approach Based on the Extreme Value Theory,
359
7.7.1
Statistical Theory,
360
7.7.2
Mean Excess Function,
361
7.7.3
New Approach to Modeling Extreme Values,
363
7.7.4
VaR Calculation Based on the New Approach,
365
7.7.5
Alternative Parameterization,
367
7.7.6
Use of Explanatory Variables,
371
7.7.7
Model Checking,
372
7.7.8
An Illustration,
373
7.8
The Extremal Index,
377
7.8.1
The D(un) Condition,
378
7.8.2
Estimation of the Extremal Index,
381
7.8.3
Value at Risk for a Stationary Time Series,
384
Exercises,
384
References,
387
8
Multivaríate
Time Series Analysis and Its Applications
389
8.1
Weak Stationarity and Cross-Correlation Matrices,
390
8.1.1
Cross-Correlation Matrices,
390
8.1.2
Linear Dependence,
392
8.1.3
Sample Cross-Correlation Matrices,
392
8.1.4
Multivariate Portmanteau Tests,
397
8.2
Vector
Autoregressive
Models,
399
8.2.1
Reduced and Structural Forms,
399
8.2.2
Stationarity Condition and Moments of a VAR(l)
Model,
401
8.2.3
Vector AR(p) Models,
403
8.2.4
Building
a VARO)
Model,
405
8.2.5
Impulse Response Function,
413
8.3
Vector Moving-Average Models,
417
8.4
Vector
ARMA
Models,
422
8.4.1
Marginal Models of Components,
427
8.5
Unit-Root Nonstationarity and
Cointegration,
428
8.5.1
An Error Correction Form,
431
8.6
Cointegrated VAR Models,
432
8.6.1
Specification of the Deterministic Function,
434
CONTENTS Xiii
8.6.2
Maximum-Likelihood Estimation,
435
8.6.3
Cointegration Test,
436
8.6.4
Forecasting of Cointegrated
VAR
Models,
437
8.6.5
An Example,
438
8.7
Threshold Cointegration and Arbitrage,
442
8.7.1
Multivariate Threshold Model,
444
8.7.2
The Data,
445
8.7.3
Estimation,
445
8.8
Pairs Trading,
446
8.8.1
Theoretical Framework,
446
8.8.2
Trading Strategy,
448
8.8.3
Simple Illustration,
449
Appendix A: Review of Vectors and Matrices,
456
Appendix B: Multivariate Normal Distributions,
460
Appendix C: Some
SCA
Commands,
461
Exercises,
462
References,
464
9
Principal Component Analysis and Factor Models
467
9.1
A Factor Model,
468
9.2
Macroeconometric Factor Models,
470
9.2.1
Single-Factor Model,
470
9.2.2
Multifactor Models,
474
9.3
Fundamental Factor Models,
476
9.3.1
BARRA
Factor Model,
477
9.3.2
Fama-French Approach,
482
9.4
Principal Component Analysis,
483
9.4.1
Theory of PCA,
483
9.4.2
Empirical PCA,
485
9.5
Statistical Factor Analysis,
489
9.5.1
Estimation,
490
9.5.2
Factor Rotation,
492
9.5.3
Applications,
492
9.6
Asymptotic Principal Component Analysis,
498
9.6.1
Selecting the Number of Factors,
499
9.6.2
An Example,
500
Exercises,
501
References,
503
XIV
CONTENTS
10 Multivariate
Volatility
Models
and Their
Applications 505
10.1
Exponentially Weighted Estimate,
506
10.2
Some Multivariate GARCH Models,
510
10.2.1
Diagonal Vectorization
(VEC)
Model,
510
10.2.2
BEKK Model,
513
10.3
Reparameterization,
516
10.3.1
Use of Correlations,
516
10.3.2
Cholesky Decomposition,
517
10.4
GARCH Models for Bivariate Returns,
521
10.4.1
Constant-Correlation Models,
521
10.4.2
Time-Varying Correlation Models,
525
10.4.3
Dynamic Correlation Models,
531
10.5
Higher Dimensional Volatility Models,
537
10.6
Factor-Volatility Models,
543
10.7
Application,
546
10.8
Multivariate
t
Distribution,
548
Appendix: Some Remarks on Estimation,
549
Exercises,
554
References,
555
11
State-Space Models and
Kalman
Filter
557
11.1
Local Trend Model,
558
11.1.1
Statistical Inference,
561
11.1.2
Kalman
Filter,
562
11.1.3
Properties of Forecast Error,
564
11.1.4
State Smoothing,
566
11.1.5
Missing Values,
570
11.1.6
Effect of Initialization,
571
11.1.7
Estimation,
572
11.1.8
S-Plus Commands Used,
572
11.2
Linear State-Space Models,
576
11.3
Model Transformation,
577
11.3.1
CAPM with Time-Varying Coefficients,
577
11.3.2
ARMA
Models,
580
11.3.3
Linear Regression Model,
586
11.3.4
Linear Regression Models with
ARMA
Errors,
588
11.3.5
Scalar Unobserved Component Model,
590
CONTENTS
XV
11.4
Kalman
Filter and Smoothing,
591
11.4.1
Kalman
Filter,
591
11.4.2
State Estimation Error and Forecast Error,
594
11.4.3
State Smoothing,
595
11.4.4
Disturbance Smoothing,
597
11.5
Missing Values,
600
11.6
Forecasting,
601
11.7
Application,
602
Exercises,
609
References,
611
12
Markov Chain Monte Carlo Methods with Applications
613
12.1
Markov Chain Simulation,
614
12.2
Gibbs Sampling,
615
12.3
Bayesian Inference,
617
12.3.1
Posterior Distributions,
617
12.3.2
Conjugate Prior Distributions,
618
12.4
Alternative Algorithms,
622
12.4.1
Metropolis Algorithm,
622
12.4.2
Metropolis-Hasting Algorithm,
623
12.4.3
Griddy Gibbs,
623
12.5
Linear Regression with Time Series Errors,
624
12.6
Missing Values and Outliers,
628
12.6.1
Missing Values,
629
12.6.2
Outlier Detection,
632
12.7
Stochastic Volatility Models,
636
12.7.1
Estimation of Univariate Models,
637
12.7.2
Multivariate Stochastic Volatility Models,
643
12.8
New Approach to SV Estimation,
649
12.9
Markov Switching Models,
660
12.10
Forecasting,
666
12.11
Other Applications,
669
Exercises,
670
References,
671
Index
673
|
| any_adam_object | 1 |
| author | Tsay, Ruey S. 1951- |
| author_GND | (DE-588)13189160X |
| author_facet | Tsay, Ruey S. 1951- |
| author_role | aut |
| author_sort | Tsay, Ruey S. 1951- |
| author_variant | r s t rs rst |
| building | Verbundindex |
| bvnumber | BV036102004 |
| callnumber-first | H - Social Science |
| callnumber-label | HA30 |
| callnumber-raw | HA30.3 |
| callnumber-search | HA30.3 |
| callnumber-sort | HA 230.3 |
| callnumber-subject | HA - Statistics |
| classification_rvk | QH 237 SK 845 |
| classification_tum | WIR 017f WIR 651f MAT 634f |
| ctrlnum | (OCoLC)505018225 (DE-599)BVBBV036102004 |
| dewey-full | 332.01/51955 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.01/51955 |
| dewey-search | 332.01/51955 |
| dewey-sort | 3332.01 551955 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | Third edition |
| format | Book |
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| id | DE-604.BV036102004 |
| illustrated | Illustrated |
| indexdate | 2025-02-20T06:36:42Z |
| institution | BVB |
| isbn | 9780470414354 0470414359 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018992354 |
| oclc_num | 505018225 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM DE-384 DE-M382 DE-19 DE-BY-UBM DE-20 DE-355 DE-BY-UBR DE-703 DE-N2 DE-188 DE-11 DE-945 DE-521 DE-473 DE-BY-UBG DE-523 DE-92 DE-862 DE-BY-FWS |
| owner_facet | DE-91G DE-BY-TUM DE-384 DE-M382 DE-19 DE-BY-UBM DE-20 DE-355 DE-BY-UBR DE-703 DE-N2 DE-188 DE-11 DE-945 DE-521 DE-473 DE-BY-UBG DE-523 DE-92 DE-862 DE-BY-FWS |
| physical | xxiii, 677 Seiten Illustrationen, Diagramme |
| publishDate | 2010 |
| publishDateSearch | 2010 |
| publishDateSort | 2010 |
| publisher | Wiley |
| record_format | marc |
| series2 | Wiley series in probability and statistics |
| spellingShingle | Tsay, Ruey S. 1951- Analysis of financial time series Time-series analysis Econometrics Risk management Kreditmarkt (DE-588)4073788-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Risikomanagement (DE-588)4121590-4 gnd Ökonometrie (DE-588)4132280-0 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| subject_GND | (DE-588)4073788-3 (DE-588)4067486-1 (DE-588)4121590-4 (DE-588)4132280-0 (DE-588)4043212-9 |
| title | Analysis of financial time series |
| title_auth | Analysis of financial time series |
| title_exact_search | Analysis of financial time series |
| title_full | Analysis of financial time series Ruey S. Tsay, The University of Chicago, Booth School of Business, Chicago, IL |
| title_fullStr | Analysis of financial time series Ruey S. Tsay, The University of Chicago, Booth School of Business, Chicago, IL |
| title_full_unstemmed | Analysis of financial time series Ruey S. Tsay, The University of Chicago, Booth School of Business, Chicago, IL |
| title_short | Analysis of financial time series |
| title_sort | analysis of financial time series |
| topic | Time-series analysis Econometrics Risk management Kreditmarkt (DE-588)4073788-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Risikomanagement (DE-588)4121590-4 gnd Ökonometrie (DE-588)4132280-0 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd |
| topic_facet | Time-series analysis Econometrics Risk management Kreditmarkt Zeitreihenanalyse Risikomanagement Ökonometrie Ökonometrisches Modell |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018992354&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT tsayrueys analysisoffinancialtimeseries |
Inhaltsverzeichnis
Sonderstandort Fakultät
| Signatur: |
2000 QH 237 T877 |
|---|---|
| Exemplar 1 | nicht ausleihbar Checked out – Rückgabe bis: 31.12.2099 Vormerken |