Bayesian methods in finance
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| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
2008
|
| Schriftenreihe: | The Frank J. Fabozzi series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
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| 020 | |a 9780471920830 |c (hbk.) : £65.00 |9 978-0-471-92083-0 | ||
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| 245 | 1 | 0 | |a Bayesian methods in finance |c Svetlozar T. Rachev ... |
| 264 | 1 | |a Hoboken, NJ |b Wiley |c 2008 | |
| 300 | |a XVIII, 329 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a The Frank J. Fabozzi series | |
| 500 | |a Literaturverz. S. 298 - 309 | ||
| 650 | 4 | |a Lehrbuch / Textbook - 28 | |
| 650 | 4 | |a Schätztheorie / Bayes-Statistik / Portfolio-Management / Capital Asset Pricing Model | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 650 | 4 | |a Finance |x Mathematical models | |
| 650 | 0 | 7 | |a Finanzierung |0 (DE-588)4017182-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Portfoliomanagement |0 (DE-588)4115601-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Risikomanagement |0 (DE-588)4121590-4 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4173536-5 |a Patentschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |D s |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-016425266 | |
Datensatz im Suchindex
| DE-BY-TUM_call_number | 0102 WIR 160f 2008 A 7810 |
|---|---|
| DE-BY-TUM_katkey | 1648354 |
| DE-BY-TUM_location | 01 |
| DE-BY-TUM_media_number | 040010098505 |
| DE-BY-UBR_call_number | 40/QK 622 R119 |
| DE-BY-UBR_katkey | 4360165 |
| DE-BY-UBR_location | 40 |
| DE-BY-UBR_media_number | 069035831134 |
| _version_ | 1822720314793000960 |
| adam_text | Contents
Preface
xv
About the
Authops xvii
CHAPTER
1
Introduction
1
A Few Notes on Notation
3
Overview
4
CHAPTER
2
The Likelihood Function
6
The
Poisson
Distribution Likelihood Function
7
The Normal Distribution Likelihood Function
9
The
Bayes
Theorem
10
Bayes
Theorem and Model Selection
14
Bayes
Theorem and Classification
14
Bayesian Inference for the Binomial Probability-
í
5
Summary
21
CHAPTER
3
Prior Information
22
Informative Prior Elicitation
23
Noninformative
Prior Distributions
25
Conjugate Prior Distributions
27
Empirical Bayesian Analysis
28
Posterior Inference
30
Posterior Point Estimates
30
Bayesian Intervals
32
Bayesian Hypothesis Comparison
32
Bayesian Predictive Inference
34
VH
Viñ
CONTENTS
Illustration: Posterior Trade-off and the Normal Mean
Parameter
35
Summary
57
Appendix: Definitions of Some Univariate and Multivariate
Statistical Distributions
38
The Univariate Normal Distribution
39
The Univariate Student s
f
-Distribution
39
The Inverted
χ2
Distribution
39
The Multivariate Normal Distribution
40
The Multivariate Student s
ŕ-Distribution
40
The
Wishart
Distribution
41
The Inverted
Wishart
Distribution
41
CHAPTER
4
43
The Univariate Linear Regression Model
43
Bayesian Estimation of the Univariate Regression
Model
45
Illustration: The Univariate Linear Regression Model
53
The Multivariate Linear Regression Model
56
Diffuse Improper Prior
58
Summary
60
CHAPTERS
61
Monte Carlo Integration
61
Algorithms for Posterior Simulation
63
Rejection Sampling
64
Importance Sampling
65
Л1СМС
Methods
66
Linear Regression with Semiconjugate Prior
77
Approximation Methods: Logistic Regression
82
The Normal Approximation
84
The Laplace Approximation
89
Summary
90
CHAPTER
6
Bayesian Framework Fop Portfolio «location
92
Classical Portfolio Selection
94
Portfolio Selection Problem Formulations
95
Contents___________________________________________
¡χ
Mean-Variance Efficient Frontier
97
Illustration: Mean-Variance Optimal Portfolio
with Portfolio Constraints
99
Bayesian Portfolio Selection
101
Prior Scenario
1:
Mean and Covariance with Diffuse
(Improper) Priors
102
Prior Scenario
2:
Mean and Covariance with Proper
Priors
103
The Efficient Frontier and the Optimal Portfolio
105
Illustration: Bayesian Portfolio Selection
106
Shrinkage Estimators
108
Unequal Histories of Returns
110
Dependence of the Short Series on the Long Series
112
Bayesian Setup
112
Predictive Moments
113
Summary
116
CHAPTER
7
Prior Beliefs and Asset Pricing Models
118
Prior Beliefs and Asset Pricing Models
119
Preliminaries
119
Quantifying the Belief About Pricing Model Validity
121
Perturbed Model
121
Likelihood Function
122
Prior Distributions
123
Posterior Distributions
124
Predictive Distributions and Portfolio Selection
126
Prior Parameter Elicitation
127
Illustration: Incorporating Confidence about the
Validity of an Asset Pricing Model
128
Model Uncertainty
129
Bayesian Model Averaging
131
Illustration: Combining Inference from the
CÂPM
and
the
Fama
and French Three-Factor Model
134
Summary
135
Appendix A: Numerical Simulation of the Predictive
Distribution
135
Sampling from the Predictive Distribution
136
Appendix B: Likelihood Function of a Candidate Model
138
CONTENTS
CHAPTERS
The Black-litterman Portfolio Selection Framework
141
Preliminaries
142
Equilibrium Returns
142
Investor Views
144
Distributional Assumptions
144
Combining Market Equilibrium and Investor Views
146
The Choice of
τ
and
Ω
147
The Optimal Portfolio Allocation
148
Illustration: Black-Litterman Optimal Allocation
149
Incorporating Trading Strategies into the Black-Litterman
Model
153
Active Portfolio Management and the Black-Litterman
Model
154
Views on Alpha and the Black-Litterman Model
157
Translating a Qualitative View into a Forecast for
Alpha
158
Co variance Matrix Estimation
159
Summary
161
CHAPTER
9
Market Efficiency and Return Predictability
162
Tests of Mean-Variance Efficiency
164
Inefficiency Measures in Testing the
CÄPM 167
Distributional Assumptions and Posterior
Distributions
168
Efficiency under Investment Constraints
169
Illustration: The Inefficiency Measure, AR
170
Testing the APT
171
Distributional Assumptions, Posterior and Predictive
Distributions
172
Certainty Equivalent Returns
173
Return Predictability
175
Posterior and Predictive Inference
177
Solving the Portfolio Selection Problem
180
Illustration: Predictability and the Investment Horizon
182
Summary
183
Appendix: Vector
Autoregressive
Setup
183
Contents Xi
CHAPTER
10
Volatility Models 18S
Garch Models of Volatility
187
Stylized Facts about Returns
188
Modeling the Conditional Mean
189
Properties and Estimation of the
GARCHţljl)
Process
190
Stochastic Volatility Models
194
Stylized Facts about Returns
195
Estimation of the Simple SV Model
195
Illustration: Forecasting Value-at-Risk
198
An Arch-Type Model or a Stochastic Volatility Model?
200
Where Do Bayesian Methods Fit?
200
CHAPTER
11
202
Bayesian Estimation of the Simple GARCH(1,1) Model
203
Distributional Setup
204
Mixture of Normals Representation of the Student s
¿-Distribution
206
GARCH(1,1) Estimation Using the
Metropolis-Hastings Algorithm
208
Illustration: Student s
t GARCHťl,!)
Model
211
Markov Regime-switching GARCH Models
214
Preliminaries
215
Prior Distributional Assumptions
217
Estimation of the MS
GARCH(1,1
)
Model
218
Sampling Algorithm for the Parameters of the MS
GARCH(1,1)
Model
222
Illustration: Student s
í
MS GARCHCl,!) Model
222
Summary
225
Appendix: Griddy Gibbs Sampler
226
Drawing from the Conditional Posterior Distribution
of
v
227
CHAPTERS
Preliminaries of SV Model Estimation
230
Likelihood Function
231
The Single-Move MCMC Algorithm for SV Model
Estimation
232
Xii___________________________________________________________________
CONTENTS
Prior and Posterior Distributions
232
Conditional Distribution of the Unobserved Volatility
233
Simulation of the Unobserved Volatility
234
Illustration
236
The Multimove MCMC Algorithm for SV Model Estimation
237
Prior and Posterior Distributions
237
Block Simulation of the Unobserved Volatility
239
Sampling Scheme
241
Illustration
241
Jump Extension of the Simple SV Model
241
Volatility Forecasting and Return Prediction
243
Summary
244
Appendix:
Kalman
Filtering and Smoothing
244
The
Kalman
Filter Algorithm
244
The Smoothing Algorithm
246
CHAPTER
13
Advanced Techniques fop Bayesian Portfolio Selection
247
Distributional Return Assumptions Alternative to Normality
248
Mixtures of Normal Distributions
249
Asymmetric Student s ¿-Distributions
250
Stable
Distributions
251
Extreme Value Distributions
252
Skew-Normal Distributions
253
The Joint Modeling of Returns
254
Portfolio Selection in the Setting of Nonnormality:
Preliminaries
255
Maximization of Utility with Higher Moments
256
Coskewness
257
Utility with Higher Moments
258
Distributional Assumptions and Moments
259
Likelihood, Prior Assumptions, and Posterior
Distributions
259
Predictive Moments and Portfolio Selection
262
Illustration: HLLM s Approach
263
Extending The Black-Litterman Approach: Copula Opinion
Pooling
263
Market-Implied and Subjective Information
264
Views and View Distributions
265
Combining the Market and the Views:The Marginal
Posterior View Distributions
266
Contents
_________________________________________________________________xiii
Views Dependence StructuraThe Joint Posterior View
Distribution
267
Posterior Distribution of the Market Realizations
267
Portfolio Construction
268
Illustration: Meucci s Approach
269
Extending The Black-Litterman ApproacbStable
Distribution
270
Equilibrium Returns Under Nonnormality
270
Summary
272
APPENDIX A: Some Risk Measures Employed in Portfolio
Construction
273
APPENDIX B: CVaR Optimization
276
APPENDIX C: A Brief Overview of Copulas
277
CHAPTER
14
Multifactm» Equity Risk Models
280
Preliminaries
281
Statistical Factor Models
281
Macroeconomic Factor Models
282
Fundamental Factor Models
282
Risk Analysis Using a Multifactor Equity Model
283
Covariance Matrix Estimation
283
Risk Decomposition
285
Return Scenario Generation
287
Predicting the Factor and Stock-Specific Returns
288
Risk Analysis in a Scenario-Based Setting
288
Conditional Value-at-Risk Decomposition
289
Bayesian Methods for Multifactor Models
292
Cross-Sectional Regression Estimation
293
Posterior Simulations
293
Return Scenario Generation
294
Illustration
294
Summary
295
2S8
Index
311
|
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| discipline | Mathematik Wirtschaftswissenschaften |
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| genre_facet | Patentschrift |
| id | DE-604.BV023239697 |
| illustrated | Illustrated |
| indexdate | 2024-12-23T20:58:40Z |
| institution | BVB |
| isbn | 0471920835 9780471920830 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016425266 |
| oclc_num | 255598886 |
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| physical | XVIII, 329 S. graph. Darst. |
| publishDate | 2008 |
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| publisher | Wiley |
| record_format | marc |
| series2 | The Frank J. Fabozzi series |
| spellingShingle | Bayesian methods in finance Lehrbuch / Textbook - 28 Schätztheorie / Bayes-Statistik / Portfolio-Management / Capital Asset Pricing Model Mathematisches Modell Bayesian statistical decision theory Finance Mathematical models Finanzierung (DE-588)4017182-6 gnd Portfoliomanagement (DE-588)4115601-8 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Risikomanagement (DE-588)4121590-4 gnd |
| subject_GND | (DE-588)4017182-6 (DE-588)4115601-8 (DE-588)4204326-8 (DE-588)4121590-4 (DE-588)4173536-5 |
| title | Bayesian methods in finance |
| title_auth | Bayesian methods in finance |
| title_exact_search | Bayesian methods in finance |
| title_full | Bayesian methods in finance Svetlozar T. Rachev ... |
| title_fullStr | Bayesian methods in finance Svetlozar T. Rachev ... |
| title_full_unstemmed | Bayesian methods in finance Svetlozar T. Rachev ... |
| title_short | Bayesian methods in finance |
| title_sort | bayesian methods in finance |
| topic | Lehrbuch / Textbook - 28 Schätztheorie / Bayes-Statistik / Portfolio-Management / Capital Asset Pricing Model Mathematisches Modell Bayesian statistical decision theory Finance Mathematical models Finanzierung (DE-588)4017182-6 gnd Portfoliomanagement (DE-588)4115601-8 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Risikomanagement (DE-588)4121590-4 gnd |
| topic_facet | Lehrbuch / Textbook - 28 Schätztheorie / Bayes-Statistik / Portfolio-Management / Capital Asset Pricing Model Mathematisches Modell Bayesian statistical decision theory Finance Mathematical models Finanzierung Portfoliomanagement Bayes-Verfahren Risikomanagement Patentschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016425266&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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