Applied quantitative finance:
Gespeichert in:
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Format: | Buch |
Sprache: | English |
Veröffentlicht: |
Berlin [u.a.]
Springer
2009
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Ausgabe: | 2. ed. |
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Beschreibung: | XXVI, 447 S. zahlr. graph. Darst. 24 cm |
ISBN: | 9783540691778 |
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084 | |a WIR 651f |2 stub | ||
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245 | 1 | 0 | |a Applied quantitative finance |c Wolfgang K. Härdle ... (eds.) |
250 | |a 2. ed. | ||
264 | 1 | |a Berlin [u.a.] |b Springer |c 2009 | |
300 | |a XXVI, 447 S. |b zahlr. graph. Darst. |c 24 cm | ||
336 | |b txt |2 rdacontent | ||
337 | |b n |2 rdamedia | ||
338 | |b nc |2 rdacarrier | ||
650 | 4 | |a Mathematisches Modell | |
650 | 4 | |a Finance |x Mathematical models | |
650 | 4 | |a Risk |x Mathematical models | |
650 | 0 | 7 | |a Financial Engineering |0 (DE-588)4208404-0 |2 gnd |9 rswk-swf |
650 | 0 | 7 | |a Finanzmathematik |0 (DE-588)4017195-4 |2 gnd |9 rswk-swf |
689 | 0 | 0 | |a Financial Engineering |0 (DE-588)4208404-0 |D s |
689 | 0 | 1 | |a Finanzmathematik |0 (DE-588)4017195-4 |D s |
689 | 0 | |5 DE-604 | |
700 | 1 | |a Härdle, Wolfgang |d 1953- |0 (DE-588)110357116 |4 edt | |
776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-3-540-69179-2 |
856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016606894&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
999 | |a oai:aleph.bib-bvb.de:BVB01-016606894 |
Datensatz im Suchindex
_version_ | 1804137818833813504 |
---|---|
adam_text | Contents
Preface
to the
2nd
Edition
v
Preface to the
1st
Edition
vii
Contributors
xxi
Frequently Used Notation
xxv
I Value at Risk
1
1
Modeling Dependencies with Copulae
3
Wolfgang
Hardie,
Ostap Okhrin ami Yarema Oklirin
1.1
Introduction
............................ 3
1.2
Divariate
Copulae
......................... 4
1.2.1
Copula Families
...................... 6
1.2.2
Dependence Measures
.................. 9
1.3
Multivariate Copulae
....................... 11
1.3.1
Copula Families
...................... 13
1.3.2
Dependence Measures
.................. 15
1.4
Estimation Methods
....................... 17
1.5
Goodness-oi-Fit Tests for Copulae
................ 19
1.6
Simulation Methods
........................ 21
1.6.1
Conditional Inverse Method
............... 22
1.6.2
Marshal-Olkin Method
.................. 22
1.7
Applications to Finance
..................... 23
1.7.1
Asset Allocation
..................... 24
1.7.2
Value-at-Risk
....................... 25
1.7.3
Time Series Modeling
................... 26
1.8
Simulation Study and Empirical Results
............ 28
1.8.1
Simulation Study
..................... 28
1.8.2
Empirical Example
.................... 30
1.9
Summary
............................. 33
xii
Contents
2
Quantification of Spread Risk by Means of Historical Simulation
37
Christoph Frisch
and
Germax Knöchlcin
2.1
Introduction
............................ 37
2.2
Risk Categories
-
a Defìnition
of Terms
............. 37
2.3
Yield Spread Time Series
..................... 39
2.3.1
Data Analysis
....................... 40
2.3.2
Discussion of Results
................... 44
2.4
Historical Simulation and Value at Risk
............. 49
2.4.1
Risk Factor: Full Yield
.................. 49
2.4.2
Risk Factor: Benchmark
................. 52
2.4.3
Risk Factor: Spread over Benchmark Yield
....... 53
2.4.4
Conservative Approach
.................. 54
2.4.5
Simultaneous Simulation
................. 54
2.5
Mark-to-Model
Backtesting
................... 54
2.6
Vali
Estimation and
Backtesting
................ 55
2.7
P-P Plots
............................. 59
2.8
Q-Q Plots
............................. 60
2.9
Discussion of Simulation Results
................. 60
2.9.1
Risk Factor: Full Yield
.................. 60
2.9.2
Risk Factor: Benchmark
................. 61
2.9.3
Risk Factor: Spread over Benchmark Yield
....... 61
2.9.4
Conservative Approach
.................. 62
2.9.5
Simultaneous Simulation
................. 62
2.10
Internal Risk Models
....................... 63
3
A Copula-Based Model of the Term Structure of CDO Tranches
69
Umberto Cherubini,
Sabrina Mulinacci
and Silvia Romagnoh
3.1
Introduction
............................ 69
3.2
A Copula-Based Model of Basket Credit Losses Dynamics
. . 71
3.3
Stochastic Processes with Dependent Increments
........ 72
3.4
An Algorithm for the Propagation of Losses
.......... 75
3.5
Empirical Analysis
........................ 76
3.6
Concluding Remarks
....................... 80
4
Va
R
in High Dimensional Systems
-
a Conditional
Correlation Approach
83
Helmut Herwaxtz and Bruno Pedrinha
4.1
Introduction
............................ 83
4.2
Half-Vec Multivaxiate GARCH Modeb
............. 85
4.3
Correlation Models
........................ 86
4.3.1
Motivation
......................... 86
Contents xiii
4.3.2
Log-Likelihood Decomposition
.............. 87
4.3.3
Constant Conditional Correlation Model
........ 88
4.3.4
Dynamic Conditional Correlation Model
........ 89
4.3.5
Inference in the Correlation Models
.......... 90
4.3.6
Generalizations of the
DCC
Model
........... 92
4.4
Valuc-at-Risk
........................... 92
4.5
An Empirical Illustration
..................... 93
4.5.1
Equal and Value Weighted Portfolios
.......... 93
4.5.2
Estimation
Results
.................... 96
II Credit Risk
103
5
Rating Migrations
105
Steffi Hose, Stefan Huscheus and Robert
Warna
5.1
Rating
Transition
Probabilities
.................106
5.1.1
From Credit Events to Migration Counts
........ 106
5.1.2
Estimating Rating Transition Probabilities
....... 107
5.1.3
Dependent Migrations
.................. 108
5.1.4
Computational Aspects
..................
Ill
5.2
Analyzing the Time-Stability of Transition Probabilities
. . . .
Ill
5.2.1
Aggregation over Periods
.................
Ill
5.2.2
Testing the Time-Stability of Transition Probabilities
. 112
5.2.3
Example
..........................114
¿5.2.4
Computational Aspects
..................115
5.3
Multi-Period Transitions
.....................115
5.3.1
Homogeneous Markov Chain
...............116
5.3.2
Bootstrapping
Markov
Chains
..............117
5.3.3
Rating Transitions of German Bank Borrowers
.....118
5.3.4
Portfolio Migration
....................119
5.3.5
Computational Aspects
..................121
6
Cross- and Autocorrelation in Multi-Period Credit Portfolio
Models
125
Christoph K.J.
Wagner
6.1
Introduction
............................125
6.2
Tie
Modeis............................127
6.2.1
A Markov-Chain Credit Migration Model
........ 127
6.2.2
The Correlated-Default-Time Model
.......... 130
6.2.3
A Discrete Banner Model
................. 132
6.2.4
The Time-Changed Barrier
MoíM
............ 133
xiv
Contents
6.3
Inter-Temporal Dependency and Autocorrelation
.......135
6.4
Conclusion
.............................137
7
Risk Measurement with Spectral Capital Allocation
139
Ludgcr Overbeck and Maria
Sokolova
7.1
Introduction
............................139
7.2
Review of Coherent Risk Measures and Allocation
.......140
7.2.1
Coherent Risk Measures
................. 140
7.2.2
Spectral Risk Measures
.................. 143
7.2.3
Coherent Allocation Measures
.............. 144
7.2.4
Spectral Allocation Measures
.............. 145
7.3
Weight Function and Mixing Measure
.............. 146
7.4
Risk Aversion
........................... 146
7.5
Implementation
.......................... 147
7.5.1
Mixing Representation
.................. 148
7.5.2
Density Representation
.................. 149
7.6
Credit Portfolio Model
...................... 149
7.7
Examples
............................. 150
7.7.1
Weighting Scheme
....................150
7.7.2
Concrete Example
....................151
7.8
Summary
.............................158
8
Valuation and
Va
R
Computation for CDOs Using Stein s
Method
161
Nicole El Karoui, Ying Jiao, David Kurtz
8.1
Introduction
............................161
8.1.1
A Primer on CDO
....................161
8.1.2
Factor Models
.......................163
8.1.3
Numerical Algoritlmis
..................164
8.2
First Order Gauss-Poisson Approximations
...........165
8.2.1
Stem s Method
-
the Normal Case
............165
8.2.2
First-Order Gaussian Approximation
..........167
8.2.3
Stem s Method
-
the
Poisson
Case
............171
8.2.4
First-Order
Poisson
Approximation
...........172
8.3
Numerical Tests
..........................175
8.3.1
Validity Domain of the Approximations
.........175
8.3.2
Stochastic Recovery Rate
-
Gaussian Case
.......177
8.3.3
Sensitivity Analysis
....................179
8.4
Real Life Applications
......................180
8.4.1
Gaussian Approximation
.................180
8.4.2
Poisson
Approximation
..................181
Coutente
8.4.3
CDO
Valuation
......................182
8.4.4
Robustness of VaR Computation
............184
III Implied Volatility
191
9
Least Squares Kernel Smoothing of the Implied Volatility Smile
193
Matthias R. Fengler and Qihna Wang
9.1
Introduction
............................193
9.2
Least Squares Kernel Smoothing of the Smile
.........194
9.3
Application
............................197
9.3.1
Weighting Functions, Kernels, and Minimization
Scheme
..........................197
.9.3.2
Data Description and Empirical Demonstration
.... 198
9.4
Proońi
...............................203
10
Numerics of Implied Binomial Trees
209
Wolfgang
Hardie
and
AJena
Myšičková
10.1
Construction of the IBT
.....................210
10.1.1
The Derman and
Kani
Algorithm
............212
10.1.2
Compensation
.......................218
10.1.3
Baric and
Calcici
Algorithm
...............219
10.2
A Simulation and a Comparison of the SPDs
..........220
10.2.1
Simulation Using the DK Algorithm
.......... 221
10.2.2
Shnulation Using the BG Algorithm
........... 223
10.2.3
Comparison with the Monte-Carlo Simulation
..... 224
10.3
Example- Analysis of EUREXData
.............. 227
11
Application of Extended
Kalman
Filter to
SPD
Estimation
233
Zdeněk Hlávka
and
Marek
Svojik
11.1
Linear Model
...........................234
11.1.1
Linear Model for Call Option Prices
...........235
11.1.2
Estimation of State Price Density
............236
11.1.3
State-Space Model for Call Option Prices
........237
11.2
Extended
Kalman
Filter and Call Options
...........238
11.3
Empirical Results
.........................239
11.3.1
Extended
Kalman
Filtering in Practice
.........240
11.3.2 SPD
Estimation in
1995.................241
11.3.3 SPD
Estimation in
2003.................243
11.4
Conclusions
............................245
XVI
Contents
12
Stochastic Volatility Estimation Using Markov Chain Simulation
249
Nikolaus
Ilautsch and Yangguoyi
Ou
12.1
The Standard Stochastic Volatility Model
...........250
12.2
Extended SV Models
.......................252
12.2.1
Fat Tails and Jumps
...................252
12.2.2
The Relationship Between Volatility and Returns
. . . 254
12.2.3
The Long Memory SV Model
..............256
12.3
MCMC-Based Bayesian inference
................257
12.3.1
Bayes
Theorem and the MCMC Algorithm
......257
12.3.2
MGMG-Bascd Estimation of the Standard SV Model
. 261
12.4
Empirical Illustrations
......................264
12.4.1
The Data
.........................264
12.4.2
Estimation of SV Models
.................265
12.5
Appendix
.............................270
12.5.1
Derivation of the Conditional Posterior Distributions
. 270
13
Measuring and Modeling Risk Using High-Frequency Data
275
Wolfgang
Härdle, Nikolaus Hantsch
and
Uta Pigorsch
13.1
Introduction
............................275
13.2
Market
Microstructure
Effects
..................277
13.3
Stylized Facts of Realized Volatility
...............280
13.4
Realized Volatility Models
....................284
13.5
Time-Varying Betas
........................285
13.5.1
The Conditional CAPM
.................286
23.5.2
Realized Betas
......................287
13.6
Summary
.............................289
14
Valuation of Multidimensional
Bermudán
Options
295
Shih-Feng Huang and Meihui Quo
14.1
Introduction
............................295
14.2
Model Assumptions
........................296
14.3
Methodology
...........................298
14.4
Examples
.............................302
14.5
Conclusion
.............................308
Contents
xvii
IV Econometrics
311
15
Multivariate Volatility Models
313
Matthias R. Fengler and Helmut
//erwarte
15.1
Introduction
............................313
15.1.1
Model Speciucations
...................314
15.1.2
Estimation of the BEKK-Model
.............316
15.2
Ah Empirica]
Illustration
.....................317
15.2.1
Data Description
..................... 317
15.2.2
Estimating
Divariate
GARCE..............
318
15.2.3
Estimating the (Go)
Variance
Processes
......... 320
15.3
Forecasting Exchange Rate Densities
.............. 323
16
The Accuracy of Long-term Real Estate Valuations
327
Rainer
Schub, Markus Staiber, Martin Wersing and Axel Werwatz
16.1
Introduction
............................327
16.2 Implementation..........................328
16.2.1
Computation of the Valuations .............
329
16.2.2
Data
............................331
16.3
Empirical Results
.........................333
16.3.1
Characterization of the Test Market
...........333
16.3.2
Horse Race
........................337
16.4
Conclusion
.............................343
17
Locally Time Homogeneous Time Series Modelling
345
Mstislav Elagin and Vladimir Spokoiny
17.1
Introduction
............................345
17.2
Model and Setup
.........................346
17.2.1
Conditional Heteroskedastic Model
...........346
17.2.2
Parametric and Local Parametric Estimation and
Inference
..........................347
17.2.3
Nearly Parametric Case
.................348
17.3
Methods for the Estimation of Parameters
...........349
17.3.1
Sequence of Intervals
................... 349
17.3.2
Local Change Point Selection
.............. 349
17.3.3
Local Model Selection
.................. 350
17.3.4
Stagewise Aggregation
.................. 351
17.4
Critical Values and Other Parameters
.............. 352
17.5
AppMcations
............................ 354
17.5.1
Forecasting Performance for One and Multiple Steps
. 355
Contents
17.5.2
Value-at-Risk
.......................357
17.5.3
A Multiple Time Series Example
............359
18
Simulation Based Option Pricing
363
Denis Belomestny and
Grígorí
N.
Mustéin
18.1
Introduction
............................363
18.2
The Consumption Based Processes
...............365
18.2.1
The Snell Envelope
....................365
18.2.2
The Continuation Value, the Continuation and
Exercise Regions
.....................366
18.2.3
Equivalence of American Options to European Ones
with Consumption Processes
...............367
18.2.4
Upper and Lower Bounds Using Consumption
Processes
......................... 367
18.2.5
Bermudán
Options
.................... 368
18.3
The Main Procedure
....................... 369
28.3.2
Local Lower Bounds
................... 369
28.3.2
The Main Procedure for Constructing Upper Bounds
for the Initial Position (Global Upper Bounds)
.....370
28.3.3
Tie Main Procedure for Constructing Lower Bounds
for the
Initiai
Position (Global Lower Bounds)
..... 372
28.3.4
Kernéi ínterpoiation...................
373
28.4
Simulations
............................ 374
18.4.2
Bermudán
Max
Calis
on
d
Assets
............ 374
28.4.2
Bermudán
Basket-Put
.................. 375
18.5
Conclusions
............................377
19
High-Frequency Volatility and Liquidity
379
Nikolaus
Hautsch and V&hidin Jeleskovic
19.1
Introduction
............................379
29.2
The XMvariate MEM
.......................380
19.3
The Vector MEM
................. ........383
19.4
Statistical inference
........................385
29.5
ffigh-Erequency Volatility and Liquidity Dynamics
.......387
20
Statistical Process Control in Asset Management
399
Vasyl Golosaoy and
Wolfgang Schmid
20.1
Introduction
............................399
20.2
Review of Statistical Process Control Concepts
.........400
20.3
Applications of
SPC
in Asset Management
...........403
Contents xix
20.3.1 Monitoring Active
Portfolio
Managers .........404
20.3.2
Surveillance of the
Optimal
Portfolio Proportions
. . . 408
20.4
Summary
.............................414
21
Canonical Dynamics Mechanism of Monetary Policy and
Interest Rate
417
Jcnher Jeng, Wei-Fang Niu, Nan-Jyc Wang, and Shih-Shan Lin
21.1
Introduction
............................417
21.2
Statistical Technology
......................419
21.3
Principles of the Fed Funds Rate Decision-Making
.......424
21.3.1
Fairness of Inflation Gauge
................ 424
21.3.2
Neutral Interest Rate Based on Fair Gauge of inflation
425
21.3.3
Monetary Policy-Making as Tight-Accommodative Cy¬
cles Along Neutral Level as Dynamic Principal
..... 427
21.4
Response Curve Structure and
FOM
С
Behavioral Analysis
. . 428
21.4.1
Data Analysis and Regressive Results
..........428
21.4.2
The Structure of the FOMC s Response Curve
-
Model
Characteristics, Interpretations
.............429
21.4.3
The Dynamics of the FFR
-
Model Implications
.... 432
21.4.4
General Dynamic Mechanism for Long-Run Dependence
of Interest Rate and Inflation
..............437
27.5
Discussions and Conclusions
...................439
Index
443
|
adam_txt |
Contents
Preface
to the
2nd
Edition
v
Preface to the
1st
Edition
vii
Contributors
xxi
Frequently Used Notation
xxv
I Value at Risk
1
1
Modeling Dependencies with Copulae
3
Wolfgang
Hardie,
Ostap Okhrin ami Yarema Oklirin
1.1
Introduction
. 3
1.2
Divariate
Copulae
. 4
1.2.1
Copula Families
. 6
1.2.2
Dependence Measures
. 9
1.3
Multivariate Copulae
. 11
1.3.1
Copula Families
. 13
1.3.2
Dependence Measures
. 15
1.4
Estimation Methods
. 17
1.5
Goodness-oi-Fit Tests for Copulae
. 19
1.6
Simulation Methods
. 21
1.6.1
Conditional Inverse Method
. 22
1.6.2
Marshal-Olkin Method
. 22
1.7
Applications to Finance
. 23
1.7.1
Asset Allocation
. 24
1.7.2
Value-at-Risk
. 25
1.7.3
Time Series Modeling
. 26
1.8
Simulation Study and Empirical Results
. 28
1.8.1
Simulation Study
. 28
1.8.2
Empirical Example
. 30
1.9
Summary
. 33
xii
Contents
2
Quantification of Spread Risk by Means of Historical Simulation
37
Christoph Frisch
and
Germax Knöchlcin
2.1
Introduction
. 37
2.2
Risk Categories
-
a Defìnition
of Terms
. 37
2.3
Yield Spread Time Series
. 39
2.3.1
Data Analysis
. 40
2.3.2
Discussion of Results
. 44
2.4
Historical Simulation and Value at Risk
. 49
2.4.1
Risk Factor: Full Yield
. 49
2.4.2
Risk Factor: Benchmark
. 52
2.4.3
Risk Factor: Spread over Benchmark Yield
. 53
2.4.4
Conservative Approach
. 54
2.4.5
Simultaneous Simulation
. 54
2.5
Mark-to-Model
Backtesting
. 54
2.6
Vali
Estimation and
Backtesting
. 55
2.7
P-P Plots
. 59
2.8
Q-Q Plots
. 60
2.9
Discussion of Simulation Results
. 60
2.9.1
Risk Factor: Full Yield
. 60
2.9.2
Risk Factor: Benchmark
. 61
2.9.3
Risk Factor: Spread over Benchmark Yield
. 61
2.9.4
Conservative Approach
. 62
2.9.5
Simultaneous Simulation
. 62
2.10
Internal Risk Models
. 63
3
A Copula-Based Model of the Term Structure of CDO Tranches
69
Umberto Cherubini,
Sabrina Mulinacci
and Silvia Romagnoh
3.1
Introduction
. 69
3.2
A Copula-Based Model of Basket Credit Losses Dynamics
. . 71
3.3
Stochastic Processes with Dependent Increments
. 72
3.4
An Algorithm for the Propagation of Losses
. 75
3.5
Empirical Analysis
. 76
3.6
Concluding Remarks
. 80
4
Va
R
in High Dimensional Systems
-
a Conditional
Correlation Approach
83
Helmut Herwaxtz and Bruno Pedrinha
4.1
Introduction
. 83
4.2
Half-Vec Multivaxiate GARCH Modeb
. 85
4.3
Correlation Models
. 86
4.3.1
Motivation
. 86
Contents xiii
4.3.2
Log-Likelihood Decomposition
. 87
4.3.3
Constant Conditional Correlation Model
. 88
4.3.4
Dynamic Conditional Correlation Model
. 89
4.3.5
Inference in the Correlation Models
. 90
4.3.6
Generalizations of the
DCC
Model
. 92
4.4
Valuc-at-Risk
. 92
4.5
An Empirical Illustration
. 93
4.5.1
Equal and Value Weighted Portfolios
. 93
4.5.2
Estimation
Results
. 96
II Credit Risk
103
5
Rating Migrations
105
Steffi Hose, Stefan Huscheus and Robert
Warna
5.1
Rating
Transition
Probabilities
.106
5.1.1
From Credit Events to Migration Counts
. 106
5.1.2
Estimating Rating Transition Probabilities
. 107
5.1.3
Dependent Migrations
. 108
5.1.4
Computational Aspects
.
Ill
5.2
Analyzing the Time-Stability of Transition Probabilities
. . . .
Ill
5.2.1
Aggregation over Periods
.
Ill
5.2.2
Testing the Time-Stability of Transition Probabilities
. 112
5.2.3
Example
.114
¿5.2.4
Computational Aspects
.115
5.3
Multi-Period Transitions
.115
5.3.1
Homogeneous Markov Chain
.116
5.3.2
Bootstrapping
Markov
Chains
.117
5.3.3
Rating Transitions of German Bank Borrowers
.118
5.3.4
Portfolio Migration
.119
5.3.5
Computational Aspects
.121
6
Cross- and Autocorrelation in Multi-Period Credit Portfolio
Models
125
Christoph K.J.
Wagner
6.1
Introduction
.125
6.2
Tie
Modeis.127
6.2.1
A Markov-Chain Credit Migration Model
. 127
6.2.2
The Correlated-Default-Time Model
. 130
6.2.3
A Discrete Banner Model
. 132
6.2.4
The Time-Changed Barrier
MoíM
. 133
xiv
Contents
6.3
Inter-Temporal Dependency and Autocorrelation
.135
6.4
Conclusion
.137
7
Risk Measurement with Spectral Capital Allocation
139
Ludgcr Overbeck and Maria
Sokolova
7.1
Introduction
.139
7.2
Review of Coherent Risk Measures and Allocation
.140
7.2.1
Coherent Risk Measures
. 140
7.2.2
Spectral Risk Measures
. 143
7.2.3
Coherent Allocation Measures
. 144
7.2.4
Spectral Allocation Measures
. 145
7.3
Weight Function and Mixing Measure
. 146
7.4
Risk Aversion
. 146
7.5
Implementation
. 147
7.5.1
Mixing Representation
. 148
7.5.2
Density Representation
. 149
7.6
Credit Portfolio Model
. 149
7.7
Examples
. 150
7.7.1
Weighting Scheme
.150
7.7.2
Concrete Example
.151
7.8
Summary
.158
8
Valuation and
Va
R
Computation for CDOs Using Stein's
Method
161
Nicole El Karoui, Ying Jiao, David Kurtz
8.1
Introduction
.161
8.1.1
A Primer on CDO
.161
8.1.2
Factor Models
.163
8.1.3
Numerical Algoritlmis
.164
8.2
First Order Gauss-Poisson Approximations
.165
8.2.1
Stem's Method
-
the Normal Case
.165
8.2.2
First-Order Gaussian Approximation
.167
8.2.3
Stem's Method
-
the
Poisson
Case
.171
8.2.4
First-Order
Poisson
Approximation
.172
8.3
Numerical Tests
.175
8.3.1
Validity Domain of the Approximations
.175
8.3.2
Stochastic Recovery Rate
-
Gaussian Case
.177
8.3.3
Sensitivity Analysis
.179
8.4
Real Life Applications
'.180
8.4.1
Gaussian Approximation
.180
8.4.2
Poisson
Approximation
.181
Coutente
8.4.3
CDO
Valuation
.182
8.4.4
Robustness of VaR Computation
.184
III Implied Volatility
191
9
Least Squares Kernel Smoothing of the Implied Volatility Smile
193
Matthias R. Fengler and Qihna Wang
9.1
Introduction
.193
9.2
Least Squares Kernel Smoothing of the Smile
.194
9.3
Application
.197
9.3.1
Weighting Functions, Kernels, and Minimization
Scheme
.197
.9.3.2
Data Description and Empirical Demonstration
. 198
9.4
Proońi
.203
10
Numerics of Implied Binomial Trees
209
Wolfgang
Hardie
and
AJena
Myšičková
10.1
Construction of the IBT
.210
10.1.1
The Derman and
Kani
Algorithm
.212
10.1.2
Compensation
.218
10.1.3
Baric and
Calcici
Algorithm
.219
10.2
A Simulation and a Comparison of the SPDs
.220
10.2.1
Simulation Using the DK Algorithm
. 221
10.2.2
Shnulation Using the BG Algorithm
. 223
10.2.3
Comparison with the Monte-Carlo Simulation
. 224
10.3
Example- Analysis of EUREXData
. 227
11
Application of Extended
Kalman
Filter to
SPD
Estimation
233
Zdeněk Hlávka
and
Marek
Svojik
11.1
Linear Model
.234
11.1.1
Linear Model for Call Option Prices
.235
11.1.2
Estimation of State Price Density
.236
11.1.3
State-Space Model for Call Option Prices
.237
11.2
Extended
Kalman
Filter and Call Options
.238
11.3
Empirical Results
.239
11.3.1
Extended
Kalman
Filtering in Practice
.240
11.3.2 SPD
Estimation in
1995.241
11.3.3 SPD
Estimation in
2003.243
11.4
Conclusions
.245
XVI
Contents
12
Stochastic Volatility Estimation Using Markov Chain Simulation
249
Nikolaus
Ilautsch and Yangguoyi
Ou
12.1
The Standard Stochastic Volatility Model
.250
12.2
Extended SV Models
.252
12.2.1
Fat Tails and Jumps
.252
12.2.2
The Relationship Between Volatility and Returns
. . . 254
12.2.3
The Long Memory SV Model
.256
12.3
MCMC-Based Bayesian 'inference
.257
12.3.1
Bayes'
Theorem and the MCMC Algorithm
.257
12.3.2
MGMG-Bascd Estimation of the Standard SV Model
. 261
12.4
Empirical Illustrations
.264
12.4.1
The Data
.264
12.4.2
Estimation of SV Models
.265
12.5
Appendix
.270
12.5.1
Derivation of the Conditional Posterior Distributions
. 270
13
Measuring and Modeling Risk Using High-Frequency Data
275
Wolfgang
Härdle, Nikolaus Hantsch
and
Uta Pigorsch
13.1
Introduction
.275
13.2
Market
Microstructure
Effects
.277
13.3
Stylized Facts of Realized Volatility
.280
13.4
Realized Volatility Models
.284
13.5
Time-Varying Betas
.285
13.5.1
The Conditional CAPM
.286
23.5.2
Realized Betas
.287
13.6
Summary
.289
14
Valuation of Multidimensional
Bermudán
Options
295
Shih-Feng Huang and Meihui Quo
14.1
Introduction
.295
14.2
Model Assumptions
.296
14.3
Methodology
.298
14.4
Examples
.302
14.5
Conclusion
.308
Contents
xvii
IV Econometrics
311
15
Multivariate Volatility Models
313
Matthias R. Fengler and Helmut
//erwarte
15.1
Introduction
.313
15.1.1
Model Speciucations
.314
15.1.2
Estimation of the BEKK-Model
.316
15.2
Ah Empirica]
Illustration
.317
15.2.1
Data Description
. 317
15.2.2
Estimating
Divariate
GARCE.
318
15.2.3
Estimating the (Go)
Variance
Processes
. 320
15.3
Forecasting Exchange Rate Densities
. 323
16
The Accuracy of Long-term Real Estate Valuations
327
Rainer
Schub, Markus Staiber, Martin Wersing and Axel Werwatz
16.1
Introduction
.327
16.2 Implementation.328
16.2.1
Computation of the Valuations .
329
16.2.2
Data
.331
16.3
Empirical Results
.333
16.3.1
Characterization of the Test Market
.333
16.3.2
Horse Race
.337
16.4
Conclusion
.343
17
Locally Time Homogeneous Time Series Modelling
345
Mstislav Elagin and Vladimir Spokoiny
17.1
Introduction
.345
17.2
Model and Setup
.346
17.2.1
Conditional Heteroskedastic Model
.346
17.2.2
Parametric and Local Parametric Estimation and
Inference
.347
17.2.3
Nearly Parametric Case
.348
17.3
Methods for the Estimation of Parameters
.349
17.3.1
Sequence of Intervals
. 349
17.3.2
Local Change Point Selection
. 349
17.3.3
Local Model Selection
. 350
17.3.4
Stagewise Aggregation
. 351
17.4
Critical Values and Other Parameters
. 352
17.5
AppMcations
. 354
17.5.1
Forecasting Performance for One and Multiple Steps
. 355
Contents
17.5.2
Value-at-Risk
.357
17.5.3
A Multiple Time Series Example
.359
18
Simulation Based Option Pricing
363
Denis Belomestny and
Grígorí
N.
Mustéin
18.1
Introduction
.363
18.2
The Consumption Based Processes
.365
18.2.1
The Snell Envelope
.365
18.2.2
The Continuation Value, the Continuation and
Exercise Regions
.366
18.2.3
Equivalence of American Options to European Ones
with Consumption Processes
.367
18.2.4
Upper and Lower Bounds Using Consumption
Processes
. 367
18.2.5
Bermudán
Options
. 368
18.3
The Main Procedure
. 369
28.3.2
Local Lower Bounds
. 369
28.3.2
The Main Procedure for Constructing Upper Bounds
for the Initial Position (Global Upper Bounds)
.370
28.3.3
Tie Main Procedure for Constructing Lower Bounds
for the
Initiai
Position (Global Lower Bounds)
. 372
28.3.4
Kernéi ínterpoiation.
373
28.4
Simulations
. 374
18.4.2
Bermudán
Max
Calis
on
d
Assets
. 374
28.4.2
Bermudán
Basket-Put
. 375
18.5
Conclusions
.377
19
High-Frequency Volatility and Liquidity
379
Nikolaus
Hautsch and V&hidin Jeleskovic
19.1
Introduction
.379
29.2
The XMvariate MEM
.380
19.3
The Vector MEM
.'.383
19.4
Statistical inference
.385
29.5
ffigh-Erequency Volatility and Liquidity Dynamics
.387
20
Statistical Process Control in Asset Management
399
Vasyl Golosaoy and
Wolfgang Schmid
20.1
Introduction
.399
20.2
Review of Statistical Process Control Concepts
.400
20.3
Applications of
SPC
in Asset Management
.403
Contents xix
20.3.1 Monitoring Active
Portfolio
Managers .404
20.3.2
Surveillance of the
Optimal
Portfolio Proportions
. . . 408
20.4
Summary
.414
21
Canonical Dynamics Mechanism of Monetary Policy and
Interest Rate
417
Jcnher Jeng, Wei-Fang Niu, Nan-Jyc Wang, and Shih-Shan Lin
21.1
Introduction
.417
21.2
Statistical Technology
.419
21.3
Principles of the Fed Funds Rate Decision-Making
.424
21.3.1
Fairness of Inflation Gauge
. 424
21.3.2
Neutral Interest Rate Based on Fair Gauge of inflation
425
21.3.3
Monetary Policy-Making as Tight-Accommodative Cy¬
cles Along Neutral Level as Dynamic Principal
. 427
21.4
Response Curve Structure and
FOM
С
Behavioral Analysis
. . 428
21.4.1
Data Analysis and Regressive Results
.428
21.4.2
The Structure of the FOMC's Response Curve
-
Model
Characteristics, Interpretations
.429
21.4.3
The Dynamics of the FFR
-
Model Implications
. 432
21.4.4
General Dynamic Mechanism for Long-Run Dependence
of Interest Rate and Inflation
.437
27.5
Discussions and Conclusions
.439
Index
443 |
any_adam_object | 1 |
any_adam_object_boolean | 1 |
author2 | Härdle, Wolfgang 1953- |
author2_role | edt |
author2_variant | w h wh |
author_GND | (DE-588)110357116 |
author_facet | Härdle, Wolfgang 1953- |
building | Verbundindex |
bvnumber | BV023424512 |
callnumber-first | H - Social Science |
callnumber-label | HG106 |
callnumber-raw | HG106 |
callnumber-search | HG106 |
callnumber-sort | HG 3106 |
callnumber-subject | HG - Finance |
classification_rvk | QP 890 SK 980 |
classification_tum | WIR 651f |
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dewey-ones | 332 - Financial economics |
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dewey-sort | 3332.0151 |
dewey-tens | 330 - Economics |
discipline | Mathematik Wirtschaftswissenschaften |
discipline_str_mv | Mathematik Wirtschaftswissenschaften |
edition | 2. ed. |
format | Book |
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illustrated | Illustrated |
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language | English |
oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016606894 |
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spelling | Applied quantitative finance Wolfgang K. Härdle ... (eds.) 2. ed. Berlin [u.a.] Springer 2009 XXVI, 447 S. zahlr. graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Mathematisches Modell Finance Mathematical models Risk Mathematical models Financial Engineering (DE-588)4208404-0 gnd rswk-swf Finanzmathematik (DE-588)4017195-4 gnd rswk-swf Financial Engineering (DE-588)4208404-0 s Finanzmathematik (DE-588)4017195-4 s DE-604 Härdle, Wolfgang 1953- (DE-588)110357116 edt Erscheint auch als Online-Ausgabe 978-3-540-69179-2 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016606894&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
spellingShingle | Applied quantitative finance Mathematisches Modell Finance Mathematical models Risk Mathematical models Financial Engineering (DE-588)4208404-0 gnd Finanzmathematik (DE-588)4017195-4 gnd |
subject_GND | (DE-588)4208404-0 (DE-588)4017195-4 |
title | Applied quantitative finance |
title_auth | Applied quantitative finance |
title_exact_search | Applied quantitative finance |
title_exact_search_txtP | Applied quantitative finance |
title_full | Applied quantitative finance Wolfgang K. Härdle ... (eds.) |
title_fullStr | Applied quantitative finance Wolfgang K. Härdle ... (eds.) |
title_full_unstemmed | Applied quantitative finance Wolfgang K. Härdle ... (eds.) |
title_short | Applied quantitative finance |
title_sort | applied quantitative finance |
topic | Mathematisches Modell Finance Mathematical models Risk Mathematical models Financial Engineering (DE-588)4208404-0 gnd Finanzmathematik (DE-588)4017195-4 gnd |
topic_facet | Mathematisches Modell Finance Mathematical models Risk Mathematical models Financial Engineering Finanzmathematik |
url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016606894&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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