Handbook of financial time series:
Gespeichert in:
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| Format: | Buch |
| Sprache: | Englisch |
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Springer
[2009]
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| Umfang: | xxix, 1050 Seiten Illustrationen, Diagramme |
| ISBN: | 9783540712961 9783662518373 |
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| 245 | 1 | 0 | |a Handbook of financial time series |c Torben G. Andersen, Richard A. Davis, Jens-Peter Kreiß, Thomas Mikosch, editors. Preamble by Nobel prize winner Robert F. Engle |
| 264 | 1 | |a Berlin ; Heidelberg |b Springer |c [2009] | |
| 264 | 4 | |c © 2009 | |
| 300 | |a xxix, 1050 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Finance |x Statistical methods | |
| 650 | 4 | |a Time-series analysis | |
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| 689 | 0 | |C b |5 DE-604 | |
| 700 | 1 | |a Andersen, Torben |0 (DE-588)128603259 |4 edt | |
| 700 | 1 | |a Davis, Richard A. |d 1952- |0 (DE-588)173920608 |4 edt | |
| 700 | 1 | |a Kreiß, Jens-Peter |d 1958- |0 (DE-588)110698444 |4 edt | |
| 700 | 1 | |a Mikosch, Thomas |d 1955- |0 (DE-588)141029412 |4 edt | |
| 700 | 1 | |a Engle, Robert F. |d 1942- |0 (DE-588)128388528 |4 wpr | |
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Datensatz im Suchindex
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| adam_text |
Contents
Foreword
.
v
List of Contributors
. xxv
Introduction
. 1
Torben
G.
Andersen, Richard A. Davis, Jens-Peter Kreiss and Thomas
Mikosch
References
. 13
Part I Recent Developments in GARCH Modeling
An Introduction to Univariate GARCH Models
. 17
Timo
Teräsvirta
1
Introduction
. 17
2
The ARCH Model
. 18
3
The Generalized ARCH Model
. 19
3.1
Why Generalized ARCH?
. 19
3.2
Families of univariate GARCH models
. 20
3.3
Nonlinear GARCH
. 23
3.4
Time-varying GARCH
. 26
3.5
Markov-switching ARCH and GARCH
. 27
3.6
Integrated and fractionally integrated GARCH
. 28
3.7
Semi- and nonparametric ARCH models
. 30
3.8
GARCH-in-mean model
. 30
3.9
Stylized facts and the first-order GARCH model
. 31
ч
4
Family
of Exponential GARCH Models
. 34
4.1
Definition and properties
. 34
4.2
Stylized facts and the first-order EGARCH model
. 35
4.3
Stochastic volatility
. 36
5
Comparing EGARCH with GARCH
. 37
6
Final Remarks and Further Reading
. 38
References
. 39
Stationarity, Mixing, Distributional Properties and Moments
of GARCH(p, ^-Processes
. 43
Alexander M. Lindner
1
Introduction
. 43
viii Contents
2
Stationary Solutions
. 44
2.1
Strict stationarity of
ARCH(l)
and GARCHCl,
1) . 45
2.2
Strict stationarity of GARCH^p, q)
. 49
2.3
Ergodicity
. 52
2.4
Weak stationarity
. 53
3
The ARCH(oo) Representation and the Conditional
Variance
. 54
4
Existence of Moments and the Autocovariance Function of
the Squared Process
. 55
4.1
Moments of
ARCHfl)
and GARCH(1,
1). 56
4.2
Moments of GARCrljp, q)
. 57
4.3
The autocorrelation function of the squares
. 60
5
Strong Mixing
. 62
6
Some Distributional Properties
. 64
7
Models Defined on the Non-Negative Integers
. 66
8
Conclusion
. 67
References
. 67
ARCH(oo) Models and Long Memory Properties
. 71
Liúdas Giraitis,
Remigijus Leipus and
Donatas Surgailis
1
Introduction
. 71
2
Stationary ARCHioo) Process
. 73
2.1
Volterra representations
. 73
2.2
Dependence structure, association, and central
limit theorem
. 75
2.3
Infinite variance and integrated ARCH(oo)
. 77
3
Linear ARCH and Bilinear Model
. 79
References
. 82
A Tour in the Asymptotic Theory of GARCH Estimation
. 85
Christian Francq and
Jean-Michel Zakoïan
1
Introduction
. 85
2
Least-Squares Estimation of ARCH Models
. 87
3
Quasi-Maximum Likelihood Estimation
. 89
3.1
Pure GARCH models
. 90
3.2
ARMA-GARCH models
. 94
4
Efficient Estimation
. 95
5
Alternative Estimators
. 99
5.1
Self-weighted
LSE
for the
ARMA
parameters
_ 100
5.2
Self-weighted QMLE
. 100
5.3
jLp-estimators
. 101
5.4
Least absolute deviations estimators
. 102
5.5
Whittle estimator
. 103
5.6
Moment estimators
. 104
6
Properties of Estimators when some GARCH Coefficients
are Equal to Zero
. 104
Contents ix
6.1
Fitting an ARCH(l) model to a white noise
. 105
6.2
On the need of additional assumptions
. 106
6.3
Asymptotic distribution of the QMLE on the
boundary
. 106
6.4
Application to hypothesis testing
. 107
7
Conclusion
. 109
References
. 109
Practical Issues in the Analysis of Univariate GARCH Models
113
Eric Zivot
1
Introduction
. 113
2
Some Stylized Facts of Asset Returns
. 114
3
The ARCH and GARCH Model
. 115
3.1
Conditional mean specification
. 118
3.2
Explanatory variables in the conditional variance
equation
. 119
3.3
The GARCH model and stylized facts of asset
returns
. 119
3.4
Temporal aggregation
. 121
4
Testing for ARCH/GARCH Effects
. 121
4.1
Testing for ARCH effects in daily and monthly
returns
. 122
5
Estimation of GARCH Models
. 123
5.1
Numerical accuracy of GARCH estimates
. 125
5.2
Quasi-maximum likelihood estimation
. 126
5.3
Model selection
. 126
5.4
Evaluation of estimated GARCH models
. 127
5.5
Estimation of GARCH models for daily and
monthly returns
. 127
6
GARCH Model Extensions
. 131
6.1
Asymmetric leverage effects and news impact
. 131
6.2
Non-Gaussian error distributions
. 135
7
Long Memory GARCH Models
. 137
7.1
Testing for long memory
. 139
7.2
Two component GARCH model
. 139
7.3
Integrated GARCH model
. 140
7.4
Long memory GARCH models for daily returns
. 141
8
GARCH Model Prediction
. 142
8.1
GARCH and forecasts for the conditional mean
. 142
8.2
Forecasts from the GARCH(1,1) model
. 143
8.3
Forecasts from asymmetric GARCH(1,1) models
. 144
8.4
Simulation-based forecasts
. 145
8.5
Forecasting the volatility of multiperiod returns
. 145
8.6
Evaluating volatility predictions
. 146
χ
Contents
8.7
Forecasting the volatility of Microsoft and the
S&P
500. 150
9
Final Remarks
. 151
References
. 151
Semiparametric and Nonparametric ARCH Modeling
. 157
Oliver
B. Linton
1
Introduction
. 157
2
The GARCH Model
. 157
3
The Nonparametric Approach
. 158
3.1
Error density
. 158
3.2
Functional form of volatility function
. 159
3.3
Relationship between mean and variance
. 162
3.4
Long memory
. 163
3.5
Locally stationary processes
. 164
3.6
Continuous time
. 164
4
Conclusion
. 165
References
. 165
Varying Coefficient GARCH Models
. 169
Pavel
Čížek
and Vladimir Spokoiny
1
Introduction
. 169
2
Conditional Heteroscedasticity Models
. 171
2.1
Model estimation
. 173
2.2
Test of homogeneity against a change-point
alternative
. 173
3
Adaptive Nonparametric Estimation
. 175
3.1
Adaptive choice of the interval of homogeneity
. 176
3.2
Parameters of the method and the implementation
details
. 176
4
Real-Data Application
. 179
4.1
Finite-sample critical values for the test of
homogeneity
. 179
4.2
Stock index S&P
500. 180
5
Conclusion
. 183
References
. 183
Extreme Value Theory for GARCH Processes
. 187
Richard A. Davis and Thomas Mikosch
1
The Model
. 187
2
Strict Stationarity and Mixing Properties
. 188
3
Embedding a GARCH Process in a Stochastic Recurrence
Equation
. 189
4
The Tails of a GARCH Process
. 190
5
Limit Theory for Extremes
. 194
5.1
Convergence of maxima
. 194
Contents xi
5.2
Convergence
of point processes
. 195
5.3
The behavior of the sample autocovariance function
197
References-
. 199
Multivariate GARCH Models
. 201
Annastiina Silvennoinen and
Timo
Teräsvirta
1
Introduction
. 201
2
Models
. 203
2.1
Models of the conditional covariance matrix
. 204
2.2
Factonmodels
. 207
2.3
Models of conditional variances and correlations
. . 210
2.4
Non-parametric and semiparametric approaches
. 215
3
Statistical Properties
. 218
4
Hyppthesis Testing in Multivariate GARCH Models
. 218
4.1
General misspecification tests
. 219
4.2 '
Tests for extensions of the CCC-GARCH model
. 221
5
An Application
. 222
6
Final Remarks
. 224
References
. 226
Part II Recent Developments in Stochastic Volatility Modeling
Stochastic Volatility: Origins and Overview
. 233
Neil Shephard and
Torben G.
Andersen
1
Introduction
. 233
2
The Origin of SV Models
. 235
3
Second Generation Model Building
. 240
3.1
Univariate models
. 240
3.2
Multivariate models
. 241
4
Inference Based on Return Data
. 242
4.1
Moment-based inference
. 242
4.2
Simulation-based inference
. 243
5
Options
. 246
5.1
Models
. 246
6
Realized Volatility
. 247
References
. 250
Probabilistic Properties of Stochastic Volatility Models
. 255
Richard A. Davis and Thomas Mikosch
1
The Model
. 255
2
Stationarity, Ergodicity and Strong Mixing
. 256
2.1
Strict stationarity
. 256
2.2
Ergodicity and strong mixing
. 257
3
The Covariance Structure
. 258
4
Moments and Tails
. 261
5
Asymptotic Theory for the Sample ACVF and ACF
. 263
xii
Contents
References
. 266
Moment—Based Estimation of Stochastic Volatility Models
. 269
Eric Renault
1
Introduction
. 270
2
The Use of a Regression Model to Analyze Fluctuations in
Variance
. 272
2.1
The linear regression model for conditional variance
272
2.2
The SR-SARV(p) model
. 274
2.3
The Exponential SARV model
. 277
2.4
Other parametric SARV models
. 279
3
Implications of SV Model Specification for Higher Order
Moments
. 281
3.1
Fat tails and variance of the variance
. 281
3.2
Skewness, feedback and leverage effects
. 284
4
Continuous Time Models
. 286
4.1
Measuring volatility
. 287
4.2
Moment-based estimation with realized volatility
. 288
4.3
Reduced form models of volatility
. 292
4.4
High frequency data with random times separating
successive observations
. 293
5
Simulation-Based Estimation
. 295
5.1
Simulation-based bias correction
. 296
5.2
Simulation-based indirect inference
. 298
5.3
Simulated method of moments
. 300
5.4
Indirect inference in presence of misspecification
. . 304
6
Concluding Remarks
. 305
References
. 307
Parameter Estimation and Practical Aspects of Modeling
Stochastic Volatility
. 313
Borus Jungbacker and Siem
Jan Koopman
1
Introduction
. 313
2
A Quasi-Likelihood Analysis Based on
Kalman
Filter
Methods
. 316
2.1
Kalman
filter for prediction and likelihood
evaluation
. 319
2.2
Smoothing methods for the conditional mean,
variance and mode
. 320
2.3
Practical considerations for analyzing the
linearized SV model
. 321
3
A Monte Carlo Likelihood Analysis
. 322
3.1
Construction of a proposal density
. 323
3.2
Sampling from the importance density and Monte
Carlo likelihood
. 325
4
Some Generalizations of SV Models
. 327
Contents xiii
4.1 Basic SV
model
. 327
4.2 Multiple
volatility factors.
328
4.3 Regression
and fixed effects .
329
4.4
Heavy-tailed innovations
. 330
4.5
Additive noise
. 331
4.6
Leverage effects
. 331
4.7
Stochastic volatility in mean
. 333
5
Empirical Illustrations
. 333
5.1
Standard
&
Poor's
500
stock index: volatility
estimation
. 334
5.2
Standard
h
Poor's
500
stock index: regression
effects
. 335
5.3
Daily changes in exchange rates: dollar-pound and
dollar-yen
. 337
6
Conclusions
. 340
Appendix
. 340
References
. 342
Stochastic Volatility Models with Long Memory
. 345
Clifford M. Hurvich and Philippe
Soulier
1
Introduction
. 345
2
Basic Properties of the LMSV Model
. 346
3
Parametric Estimation
. 347
4
Semiparametric Estimation
. 349
5
Generalizations of the LMSV Model
. 352
6
Applications of the LMSV Model
. 352
References
. 353
Extremes of Stochastic Volatility Models
. 355
Richard A. Davis and Thomas Mikosch
1
Introduction
. 355
2
The Tail Behavior of the Marginal Distribution
. 356
2.1
The light-tailed case
. 356
2.2
The heavy-tailed case
. 357
3
Point Process Convergence
. 358
3.1
Background
. 358
3.2
Application to stochastic volatility models
. 360
References
. 364
Multivariate Stochastic Volatility
. 365
Siddhartha Chib, Yasuhiro
Omori
and Manabu
Asai
1
Introduction
. 366
2
Basic MSV Model
. 369
2.1
No-leverage model
. 369
2.2
Leverage effects
. 373
2.3
Heavy-tailed measurement error models
. 377
xiv Contents
3
Factor MSV Model
. 379
3.1
Volatility
factor model
. 379
3.2
Mean
factor model
. 382
3.3
Bayesian analysis of mean
factor MSV model
. 384
4
Dynamic Correlation MSV
Model
. 388
4.1
Modeling by reparameterization
. 388
4.2
Matrix
exponential transformation.
390
4.3
Wishart
process .
391
5
Conclusion
. 396
References
. 397
Part III Topics in Continuous Time Processes
An Overview of Asset-Price Models
. 403
Peter J.
Brockwell
1
Introduction
. 404
2
Shortcomings of the BSM Model
. 409
3
A General Framework for Option Pricing
. 410
4
Some Non-Gaussian Models for Asset Prices
. 411
5
Further Models
. 415
References
. 416
Ornstein—Uhlenbeck Processes and Extensions
. 421
Ross A. Mailer,
Gernot Müller
and Alex Szimayer
1
Introduction
. 422
2
OU
Process Driven by Brownian Motion
. 422
3
Generalised
OU
Processes
. 424
3.1
Background on bivariate Levy processes
. 424
3.2
Levy
OU
processes
. 426
3.3
Self-decomposability, self-similarity, class L,
Lamperti transform
. 429
4
Discretisations
. 430
4.1
Autoregressive
representation, and perpetuities
. 430
4.2
Statistical issues: Estimation and hypothesis testing
431
4.3
Discretely sampled process
. 431
4.4
Approximating the COGARCH
. 432
5
Conclusion
. 435
References
. 435
Jump-Type Levy Processes
. 439
Ernst
Eberlein
1
Probabilistic Structure of Levy Processes
. 439
2
Distributional Description of Levy Processes
. 443
3
Financial Modeling
. 446
4
Examples of Levy Processes with Jumps
. 449
4.1
Poisson
and compound
Poisson
processes
. 449
Contents xv
4.2
Levy jump diffusion
. 450
4.3
Hyperbolic Levy processes
. 450
4.4
Generalized hyperbolic Levy processes
. 451
4.5
CGMY and variance gamma Levy processes
. 452
4.6
α
-Stable Levy processes
. 453
4.7
Meixner Levy processes
. 453
References
. 454
Levy—Driven Continuous—Time
ARMA
Processes
. 457
Peter J.
Brockwell
1
Introduction
. 458
2
Second-Order Levy-Driven CARMA Processes
. 460
3
Connections with Discrete-Time
ARMA
Processes
. 470
4
An Application to Stochastic Volatility Modelling
. 474
5
Continuous-Time GARCH Processes
. 476
6
Inference for CARMA Processes
. 478
References
. 479
Continuous Time Approximations to GARCH and Stochastic
Volatility Models
. 481
Alexander M. Lindner
1
Stochastic Volatility Models and Discrete GARCH
. 481
2
Continuous Time GARCH Approximations
. 482
2.1
Preserving the random recurrence equation property
483
2.2
The diffusion limit of Nelson
. 484
2.3
The COGARCH model
. 486
2.4
Weak GARCH processes
. 488
2.5
Stochastic delay equations
. 489
2.6
A continuous time GARCH model designed for
option pricing
. 490
3
Continuous Time Stochastic Volatility Approximations
. 491
3.1
Sampling a continuous time SV model at
equidistant times
. 491
3.2
Approximating a continuous time SV model
. 493
References
. 495
Maximum Likelihood and Gaussian Estimation of Continuous
Time Models in Finance
. 497
Peter C. B. Phillips and
Jun
Yu
1
Introduction
. 498
2
Exact ML Methods
. 499
2.1
ML based on the transition density
. 499
2.2
ML based on the continuous record likelihood
. 502
3
Approximate ML Methods Based on Transition Densities
. 503
3.1
The
Euler
approximation and refinements
. 504
3.2
Closed-form approximations
. 509
xvi Contents
3.3
Simulated infill ML methods
. 512
3.4
Other approaches
. 514
4
Approximate ML Methods Based on the Continuous
Record Likelihood and Realized Volatility
. 516
5
Monte Carlo Simulations
. 519
6
Estimation Bias Reduction Techniques
. 520
6.1
Jackknife estimation
. 521
6.2
Indirect inference estimation
. 522
7
Multivariate Continuous Time Models
. 524
8
Conclusions
. 527
References
. 527
Parametric Inference for Discretely Sampled Stochastic
Differential Equations
. 531
Michael S0rensen
1
Introduction
. 531
2
Asymptotics: Fixed Frequency
. 532
3
Likelihood Inference
. 536
4
Martingale Estimating Functions
. 538
5
Explicit Inference
. 543
6
High Frequency Asymptotics and Efficient Estimation
. 548
References
. 551
Realized Volatility
. 555
Torben
G.
Andersen and
Luca Benzoni
1
Introduction
. 556
2
Measuring Mean Return versus Return Volatility
. 557
3
Quadratic Return Variation and Realized Volatility
. 559
4
Conditional Return Variance and Realized Volatility
. 561
5
Jumps and Bipower Variation
. 563
6
Efficient Sampling versus
Microstructure
Noise
. 564
7
Empirical Applications
. 566
7.1
Early work
. 566
7.2
Volatility forecasting
. 567
7.3
The distributional implications of the no-arbitrage
condition
. 568
7.4
Multivariate quadratic variation measures
. 568
7.5
Realized volatility, model specification and
estimation
. 569
8
Possible Directions for Future Research
. 569
References
. 570
Contents xvii
Estimating Volatility in the Presence of Market
Microstructure
Noise: A Review of the Theory and Practical
Considerations
. 577
Yacine Ai't-Sahalia and Per A. Mykland
1
Introduction
. 577
2
Estimators
. 579
2.1
The parametric volatility case
. 579
2.2
The nonparametric stochastic volatility case
. 582
3
Refinements
. 585
3.1
Multi-scale realized volatility
. 585
3.2
Non-equally spaced observations
. 586
3.3
Serially-correlated noise
. 587
3.4
Noise correlated with the price signal
. 589
3.5
Small sample edgeworth expansions
. 591
3.6
Robustness to departures from the data generating
process assumptions
. 591
4
Computational and Practical Implementation
Considerations
. 592
4.1
Calendar, tick and transaction time sampling
. 592
4.2
Transactions or quotes
. 592
4.3
Selecting the number of subsamples in practice
. 593
4.4
High versus low liquidity assets
. 594
4.5
Robustness to data cleaning procedures
. 594
4.6
Smoothing by averaging
. 595
5
Conclusions
. 596
References
. 596
Option Pricing
. 599
Jan Kallsen
1
Introduction
. 599
2
Arbitrage Theory from a Market Perspective
. 600
3
Martingale Modelling
. 603
4
Arbitrage Theory from an Individual Perspective
. 605
5
Quadratic Hedging
. 606
6
Utility Indifference Pricing
. 607
References
. 611
An Overview of Interest Rate Theory
. 615
Tomas Björk
1
General Background
. 615
2
Interest Rates and the Bond Market
. 618
3
Factor Models
. 620
4
Modeling under the Objective Measure
Ρ
. 621
4.1
The market price of risk
. 622
5
Martingale Modeling
. 623
5.1 Affine
term structures
. 624
xviii
Contents
5.2
Short rate models
. 625
5.3
Inverting the yield curve
. 627
6
Forward Rate Models
. 629
6.1
The HJM drift condition
. 629
6.2
The Musiela parameterization
. 631
7
Change of Numeraire
. 632
7.1
Generalities
. 632
7.2
Forward measures
. 635
7.3
Option pricing
. 635
8
LIBOR
Market Models
. 638
8.1
Caps: definition and market practice
. 638
8.2
The
LIBOR
market model
. 640
8.3
Pricing caps in the
LIBOR
model
. 641
8.4
Terminal measure dynamics and existence
. 641
9
Potentials and Positive Interest
. 642
9.1
Generalities
. 642
9.2
The Flesaker-Hughston fractional model
. 644
9.3
Connections to the Riesz decomposition
. 646
9.4
Conditional variance potentials
. 647
9.5
The Rogers Markov potential approach
. 648
10
Notes
. 650
References
. 651
Extremes of Continuous—Time Processes
. 653
Vicky
Fasen
1
Introduction
. 653
2
Extreme Value Theory
. 654
2.1
Extremes of discrete-time processes
. 655
2.2
Extremes of continuous-time processes
. 656
2.3
Extensions
. 656
3
The Generalized Ornstein-Uhlenbeck (GOU)-Model
. 657
3.1
The Ornstein-Uhlenbeck process
. 658
3.2
The non-Ornstein-Uhlenbeck process
. 659
3.3
Comparison of the models
. 661
4
Tail Behavior of the Sample Maximum
. 661
5
Running sample Maxima and Extremal Index Function
. 663
6
Conclusion
. 664
References
. 665
Part IV Topics in Cointegration and Unit Roots
Cointegration: Overview and Development
. 671
Soren Johansen
1
Introduction
. 671
1.1
Two examples of cointegration
. 672
Contents xix
1.2
Three ways of modeling
cointegration
. 673
1.3
The model analyzed in this article
. 674
2
Integration,
Cointegration
and Granger's Representation
Theorem
. 675
2.1
Definition of integration and
cointegration
. 675
2.2
The Granger Representation Theorem
. 677
2.3
Interpretation of cointegrating coefficients
. 678
3
Interpretation of the
1(1)
Model for
Cointegration
. 680
3.1
The models H(r)
. 680
3.2
Normalization of parameters of the J(l) model.
. 681
3.3
Hypotheses on long-run coefficients
. 681
3.4
Hypotheses on adjustment coefficients
. 682
4
Likelihood Analysis of the
/(1)
Model
. 683
4.1
Checking the specifications of the model
. 683
4.2
Reduced rank regression
. 683
4.3
Maximum likelihood estimation in the
1(1)
model
and derivation of the rank test
. 684
5
Asymptotic Analysis
. 686
5.1
Asymptotic distribution of the rank test
. 686
5.2
Asymptotic distribution of the estimators
. 687
6
Further Topics in the Area of Cointegration
. 689
6.1
Rational expectations
. 689
6.2
The
1(2)
model
. 690
7
Concluding Remarks
. 691
References
. 692
Time Series with Roots on or Near the Unit Circle
. 695
Ngai Hang Chan
1
Introduction
. 695
2
Unit Root Models
. 696
2.1
First order
. 697
2.2
AR(p) models
. 699
2.3
Model selection
. 702
3
Miscellaneous Developments and Conclusion
. 704
References
. 705
Fractional Cointegration
. 709
Willa W.
Chen and Clifford M. Hurvich
1
Introduction
. 709
2
Type I and Type II Definitions of I{d)
. 710
2.1
Univariate series
. 710
2.2
Multivariate series
. 713
3
Models for Fractional Cointegration
. 715
3.1
Parametric models
. 716
4
Tapering
. 717
5
Semiparametric Estimation of the Cointegrating Vectors
. 718
xx Contents
6
Testing
for
Cointegration;
Determination of Cointegrating
Rank
. 723
References
. 724
Part V Special Topics
-
Risk
Different Kinds of Risk
. 729
Paul Embrechts,
Hansjörg Furrer
and Roger
Kaufmann
1
Introduction
. 729
2
Preliminaries
. 732
2.1
Risk measures
. 732
2.2
Risk factor mapping and loss portfolios
. 735
3
Credit Risk
. 736
3.1
Structural models
. 737
3.2
Reduced form models
. 737
3.3
Credit risk for regulatory reporting
. 738
4
Market Risk
. 738
4.1
Market risk models
. 739
4.2
Conditional versus unconditional modeling
. 740
4.3
Scaling of market risks
. 740
5
Operational Risk
. 742
6
Insurance Risk
. 744
6.1
Life insurance risk
. 744
6.2
Modeling parametric life insurance risk
. 745
6.3
Non-life insurance risk
. 747
7
Aggregation of Risks
. 748
8
Summary
. 749
References
. 750
Value-at-Risk Models
. 753
Peter
Christoffersen
1
Introduction and Stylized Facts
. 753
2
A Univariate Portfolio Risk Model
. 755
2.1
The dynamic conditional variance model
. 756
2.2
Univariate filtered historical simulation
. 757
2.3
Univariate extensions and alternatives
. 759
3
Multivariate, Base-Asset Return Methods
. 760
3.1
The dynamic conditional correlation model
. 761
3.2
Multivariate filtered historical simulation
. 761
3.3
Multivariate extensions and alternatives
. 763
4
Summary and Further Issues
. 764
References
. 764
Contents xxi
Copula—
Based Models for Financial Time Series
. 767
Andrew J.
Patton
1
Introduction
. 767
2
Copula-Based Models for Time Series
. 771
2.1
Copula-based models for multivariate time series
. 772
2.2
Copula-based models for univariate time series
. 773
2.3
Estimation and evaluation of copula—based models
for time series
. 775
3
Applications of Copulas in Finance and Economics
. 778
4
Conclusions and Areas for Future Research
. 780
References
. 781
Credit Risk Modeling
. 787
David
Lando
1
Introduction
. 787
2
Modeling the Probability of Default and Recovery
. 788
3
Two Modeling Frameworks
. 789
4
Credit Default Swap Spreads
. 792
5
Corporate Bond Spreads and Bond Returns
. 795
6
Credit Risk Correlation
. 795
References
. 797
Part V Special Topics
-
Time Series Methods
Evaluating Volatility and Correlation Forecasts
. 801
Andrew J.
Patton
and Kevin Sheppard
1
Introduction
. 801
1.1
Notation
. 803
2
Direct Evaluation of Volatility Forecasts
. 804
2.1
Forecast optimality tests for univariate volatility
forecasts
. 805
2.2
MZ regressions on transformations of 3f
. 806
2.3
Forecast optimality tests for multivariate volatility
forecasts
. 807
2.4
Improved MZ regressions using generalised least
squares
. 808
2.5
Simulation study
. 810
3
Direct Comparison of Volatility Forecasts
. 815
3.1
Pair-wise comparison of volatility forecasts
. 816
3.2
Comparison of many volatility forecasts
. 817
3.3
'Robust' loss functions for forecast comparison
. 818
3.4
Problems arising from 'non-robust' loss functions
. 819
3.5
Choosing a "robust" loss function
. 823
3.6
Robust loss functions for multivariate volatility
comparison
. 825
xxii
Contents
3.7
Direct comparison via encompassing tests
. 828
4
Indirect Evaluation of Volatility Forecasts
. 830
4.1
Portfolio optimisation
. 831
4.2
Tracking error minimisation
. 832
4.3
Other methods of indirect evaluation
. 833
5
Conclusion
. 835
References
. 835
Structural Breaks in Financial Time Series
. 839
Elena
Andreou
and Eric Ghysels
1
Introduction
. 839
2
Consequences of Structural Breaks in Financial Time Series
840
3
Methods for Detecting Structural Breaks
. 843
3.1
Assumptions
. 844
3.2
Historical and sequential partial-sums
change-point statistics
. 845
3.3
Multiple breaks tests
. 848
4
Change-Point Tests in Returns and Volatility
. 851
4.1
Tests based on empirical volatility processes
. 851
4.2
Empirical processes and the SV class of models
. 854
4.3
Tests based on parametric volatility models
. 858
4.4
Change-point tests in long memory
. 861
4.5
Change-point in the distribution
. 863
5
Conclusions
. 865
References
. 866
An Introduction to Regime Switching Time Series Models
. 871
Theis
Lange
and Anders Rahbek
1
Introduction
. 871
1.1
Markov and observation switching
. 872
2
Switching ARCH and CVAR
. 874
2.1
Switching ARCH and GARCH
. 875
2.2
Switching CVAR
. 877
3
Likelihood-Based Estimation
. 879
4
Hypothesis Testing
. 881
5
Conclusion
. 883
References
. 883
Model Selection
. 889
Hannes
Leeb and
Benedikt
M.
Pötscher
1
The Model Selection Problem
. 889
1.1
A general formulation
. 889
1.2
Model selection procedures
. 892
2
Properties of Model Selection Procedures and of
Post-Model-Selection Estimators
. 900
2.1
Selection probabilities and consistency
. 900
Contents xxiii
2.2
Risk properties of post-model-selection estimators
903
2.3
Distributional properties of post-model-selection
estimators
. 906
3
Model Selection in Large- or Infinite-Dimensional Models
. 908
4
Related Procedures Based on Shrinkage and Model
Averaging
. 915
5
Further Reading
. 916
References
. 916
Nonparametric Modeling in Financial Time Series
. 927
Jürgen Franke,
Jens-Peter Kreiss and Enno
Mammen
1
Introduction
. 927
2
Nonparametric Smoothing for Time Series
. 929
2.1
Density estimation via kernel smoothing
. 929
2.2
Kernel smoothing regression
. 932
2.3
Diffusions
. 935
3
Testing
. 937
4
Nonparametric Quantile Estimation
. 940
5
Advanced Nonparametric Modeling
. 942
6
Sieve Methods
. 944
References
. 947
Modelling Financial High Frequency Data Using Point
Processes
. 953
Luc Bauwens and
Nikolaus Hautsch
1
Introduction
. 953
2
Fundamental Concepts of Point Process Theory
. 954
2.1
Notation and definitions
. 955
2.2
Compensators, intensities, and hazard rates
. 955
2.3
Types and representations of point processes
. 956
2.4
The random time change theorem
. 959
3
Dynamic Duration Models
. 960
3.1
ACD models
. 960
3.2
Statistical inference
. 963
3.3
Other models
. 964
3.4
Applications
. 965
4
Dynamic Intensity Models
. 967
4.1
Hawkes processes
. 967
4.2
Autoregressive
intensity processes
. 969
4.3
Statistical inference
. 973
4.4
Applications
. 975
References
. 976
xxiv
Contents
Part V Special Topics
-
Simulation Based Methods
Resampling and
Subsampling
for Financial Time Series
. 983
Efstathios Paparoditis and Dimitris
N.
Politis
1
Introduction
. 983
2
Resampling the Time Series of Log-Returns
. 986
2.1
Parametric methods based on i.i.d. resampling of
residuals
. 986
2.2
Nonparametric methods based on i.i.d. resampling
of residuals
. 988
2.3
Markovian bootstrap
. 990
3
Resampling Statistics Based on the Time Series of
Log-Returns
. 992
3.1
Regression bootstrap
. 992
3.2
Wild bootstrap
. 993
3.3
Local bootstrap
. 994
4
Subsampling
and Self—Normalization
. 995
References
. 997
Markov Chain Monte Carlo
.1001
Michael Johannes and Nicholas Poison
1
Introduction
.1001
2
Overview of MCMC Methods
.1002
2.1
Clifford-Hammersley theorem
.1002
2.2
Constructing Markov chains
.1003
2.3
Convergence theory
.1007
3
Financial Time Series Examples
.1008
3.1
Geometric Brownian motion
.1008
3.2
Time-varying expected returns
.1009
3.3
Stochastic volatility models
.1010
4
Further Reading
.1011
References
.1012
Particle Filtering
.1015
Michael Johannes and Nicholas Poison
1
Introduction
.1015
2
A Motivating Example
.1017
3
Particle Filters
.1019
3.1
Exact particle filtering
.1021
3.2
SIR
.1024
3.3
Auxiliary particle filtering algorithms
.1026
4
Further Reading
.1027
References
.1028
Index
.1031 |
| any_adam_object | 1 |
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| id | DE-604.BV022717337 |
| illustrated | Illustrated |
| indexdate | 2025-02-18T11:01:32Z |
| institution | BVB |
| isbn | 9783540712961 9783662518373 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015923107 |
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| physical | xxix, 1050 Seiten Illustrationen, Diagramme |
| publishDate | 2009 |
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| spellingShingle | Handbook of financial time series Finance Statistical methods Time-series analysis Kreditmarkt (DE-588)4073788-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4073788-3 (DE-588)4067486-1 |
| title | Handbook of financial time series |
| title_auth | Handbook of financial time series |
| title_exact_search | Handbook of financial time series |
| title_full | Handbook of financial time series Torben G. Andersen, Richard A. Davis, Jens-Peter Kreiß, Thomas Mikosch, editors. Preamble by Nobel prize winner Robert F. Engle |
| title_fullStr | Handbook of financial time series Torben G. Andersen, Richard A. Davis, Jens-Peter Kreiß, Thomas Mikosch, editors. Preamble by Nobel prize winner Robert F. Engle |
| title_full_unstemmed | Handbook of financial time series Torben G. Andersen, Richard A. Davis, Jens-Peter Kreiß, Thomas Mikosch, editors. Preamble by Nobel prize winner Robert F. Engle |
| title_short | Handbook of financial time series |
| title_sort | handbook of financial time series |
| topic | Finance Statistical methods Time-series analysis Kreditmarkt (DE-588)4073788-3 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Finance Statistical methods Time-series analysis Kreditmarkt Zeitreihenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015923107&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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