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Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications 2008 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 206 pages, kõrgus x laius: 235x155 mm, kaal: 710 g, 27 Illustrations, black and white; XIV, 206 p. 27 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Economics and Mathematical Systems 612
  • Ilmumisaeg: 29-May-2008
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540786562
  • ISBN-13: 9783540786566
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Financial Risk Management with Bayesian Estimation of GARCH Models: Theory and Applications 2008 ed.Suurem pilt
  • Formaat: Paperback / softback, 206 pages, kõrgus x laius: 235x155 mm, kaal: 710 g, 27 Illustrations, black and white; XIV, 206 p. 27 illus., 1 Paperback / softback
  • Sari: Lecture Notes in Economics and Mathematical Systems 612
  • Ilmumisaeg: 29-May-2008
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540786562
  • ISBN-13: 9783540786566
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783540786566.html
Märksõnad:
This book presents in detail methodologies for the Bayesian estimation of sing- regime and regime-switching GARCH models. These models are widespread and essential tools in n ancial econometrics and have, until recently, mainly been estimated using the classical Maximum Likelihood technique. As this study aims to demonstrate, the Bayesian approach o ers an attractive alternative which enables small sample results, robust estimation, model discrimination and probabilistic statements on nonlinear functions of the model parameters. The author is indebted to numerous individuals for help in the preparation of this study. Primarily, I owe a great debt to Prof. Dr. Philippe J. Deschamps who inspired me to study Bayesian econometrics, suggested the subject, guided me under his supervision and encouraged my research. I would also like to thank Prof. Dr. Martin Wallmeier and my colleagues of the Department of Quantitative Economics, in particular Michael Beer, Roberto Cerratti and Gilles Kaltenrieder, for their useful comments and discussions. I am very indebted to my friends Carlos Ord as Criado, Julien A. Straubhaar, J er ^ ome Ph. A. Taillard and Mathieu Vuilleumier, for their support in the elds of economics, mathematics and statistics. Thanks also to my friend Kevin Barnes who helped with my English in this work. Finally, I am greatly indebted to my parents and grandparents for their support and encouragement while I was struggling with the writing of this thesis.

Arvustused

From the reviews:

This book provides an application of Bayesian methods to financial risk management. The book is well written, it provides a comprehensive list of references and its index allows very easy navigation among its different concepts. This book can be very useful to graduate students as well as researchers who work on quantitative risk management and/or financial econometrics. To sum up, the book is well organized and provides a thorough treatment of the Bayesian estimation of GARCH-like models and its application to risk management. (Yannick Malevergne, Mathematical Reviews, Issue 2010 b)

Summary XIII
1 Introduction 1
2 Bayesian Statistics and MCMC Methods 9
2.1 Bayesian inference
9
2.2 MCMC methods
10
2.2.1 The Gibbs sampler
11
2.2.2 The Metropolis-Hastings algorithm
12
2.2.3 Dealing with the MCMC output
13
3 Bayesian Estimation of the GARCH(1, 1) Model with Normal Innovations 17
3.1 The model and the priors
17
3.2 Simulating the joint posterior
18
3.2.1 Generating vector α
20
3.2.2 Generating parameter β
20
3.3 Empirical analysis
22
3.3.1 Model estimation
24
3.3.2 Sensitivity analysis
30
3.3.3 Model diagnostics
32
3.4 Illustrative applications
34
3.4.1 Persistence
34
3.4.2 Stationarity
36
4 Bayesian Estimation of the Linear Regression Model with Normal-GJR(1, 1) Errors 39
4.1 The model and the priors
40
4.2 Simulating the joint posterior
41
4.2.1 Generating vector γ
41
4.2.2 Generating the GJR parameters
42
Generating vector α
43
Generating parameter β
44
4.3 Empirical analysis
44
4.3.1 Model estimation
46
4.3.2 Sensitivity analysis
52
4.3.3 Model diagnostics
52
4.4 Illustrative applications
53
5 Bayesian Estimation of the Linear Regression Model with Student-t-GJR(1, 1) Errors 55
5.1 The model and the priors
56
5.2 Simulating the joint posterior
59
5.2.1 Generating vector γ
59
5.2.2 Generating the GJR parameters
60
Generating vector α
61
Generating parameter β
62
5.2.3 Generating vector
62
5.2.4 Generating parameter ν
63
5.3 Empirical analysis
64
5.3.1 Model estimation
64
5.3.2 Sensitivity analysis
70
5.3.3 Model diagnostics
70
5.4 Illustrative applications
71
6 Value at Risk and Decision Theory 73
6.1 Introduction
73
6.2 The concept of Value at Risk
76
6.2.1 The one-day ahead VaR under the GARCH(1, 1) dynamics
77
6.2.2 The s-day ahead VaR, under the GARCH(1, 1) dynamics
77
6.3 Decision theory
85
6.3.1 Bayes point estimate
85
6.3.2 The Linex loss function
86
6.3.3 The Monomial loss function
90
6.4 Empirical application: the VaR, term structure
91
6.4.1 Data set and estimation design
92
6.4.2 Bayesian estimation
94
6.4.3 The term structure of the VaR density
95
6.4.4 VaR. point estimates
96
6.4.5 Regulatory capital
100
6.4.6 Forecasting performance analysis
102
6.5 The Expected Shortfall risk measure
104
7 Bayesian Estimation of the Markov-Switching GJR(1, 1) Model with Student-t Innovations 109
7.1 The model and the priors
111
7.2 Simulating the joint posterior
115
7.2.1 Generating vector s
117
7.2.2 Generating matrix P
118
7.2.3 Generating the GJR, parameters
118
Generating vector α
120
Generating vector β
121
7.2.4 Generating vector
122
7.2.5 Generating parameter ν
122
7.3 An application to the Swiss Market Index
122
7.4 In-sample performance analysis
133
7.4.1 Model diagnostics
133
7.4.2 Deviance information criterion
134
7.4.3 Model likelihood
137
7.5 Forecasting performance analysis
144
7.6 One-day ahead VaR density
148
7.7 Maximum Likelihood estimation
152
8 Conclusion 155
A Recursive Transformations 161
A.1 The GARCH(1, 1) model with Normal innovations
161
A.2 The GJR(1, 1) model with Normal innovations
162
A.3 The GJR(1, 1) model with Student-t innovations
163
B Equivalent Specification 165
C Conditional Moments 171
Computational Details 179
Abbreviations and Notations 181
List of Tables 187
List of Figures 189
References 191
Index 201