Applications of state space models in finance: an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Göttingen
Univ.-Verl. Göttingen
2009
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zusätzliches Online-Angebot unter http://www.sub.uni-goettingen.de |
| Beschreibung: | XXV, 202 S. graph. Darst. |
| ISBN: | 9783941875227 |
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Datensatz im Suchindex
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|---|---|
| adam_text | IMAGE 1
CONTENTS
LIST OF FIGURES XIII
LIST OF TABLES XV
NOTATION AND CONVENTIONS XVII
USED ABBREVIATIONS AND SYMBOLS XIX
PREFACE XXVII
ACKNOWLEDGEMENTS XXIX
1 INTRODUCTION 1
1.1 THE MODELING OF CHANGE 2
1.2 RESEARCH OBJECTIVES 3
1.3 ORGANIZATION OF THE THESIS 4
2 SOME STYLIZED FACTS OF WEEKLY SECTOR RETURN SERIES 7
2.1 THE DATA 8
2.2 EMPIRICAL PROPERTIES 10
2.2.1 THICK TAILS 11
2.2.2 VOLATILITY CLUSTERING 13
2.2.3 LEVERAGE EFFECTS 13
2.2.4 VOLATILITY CO-MOVEMENTS 13
2.3 IMPLICATIONS 14
3 LINEAR GAUSSIAN STATE SPACE MODELS AND THE KAIMAN FILTER 17
3.1 BASIC IDEAS OF STATE SPACE MODELING 17
3.2 THE STATE SPACE FORM OF A DYNAMIC SYSTEM 19
3.3 THE KAIMAN FILTER AND SMOOTHER 20
3.3.1 FILTERING 21
3.3.1.1 THE GENERAL FORM OF THE KAIMAN FILTER 21
3.3.1.2 THE STEADY-STATE KAIMAN FILTER 22
3.3.2 STATE SMOOTHING 22
3.3.3 DISTURBANCE SMOOTHING 23
3.3.3.1 DISTURBANCE SMOOTHING RECURSION 23
3.3.3.2 FAST STATE SMOOTHING 24
3.3.4 MISSING OBSERVATIONS 24
3.3.5 FORECASTING 25
3.3.6 INITIALIZATION OF FILTER AND SMOOTHER 25
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/999977679
DIGITALISIERT DURCH
IMAGE 2
VIII CONTENTS
3.3.7 THE KAIMAN FILTER WITH NON-GAUSSIAN ERRORS 27
3.4 MAXIMUM LIKELIHOOD ESTIMATION 27
3.4.1 THE LOGLIKELIHOOD FUNCTION 27
3.4.1.1 PREDICTION ERROR DECOMPOSITION 28
3.4.1.2 CONCENTRATED LOGLIKELIHOOD 29
3.4.2 NUMERICAL MAXIMIZATION 30
3.4.3 THE EM ALGORITHM 31
3.4.4 PARAMETER RESTRICTIONS 31
3.5 INTRODUCTION OF EXPLANATORY VARIABLES 32
3.5.1 INCORPORATION OF REGRESSION EFFECTS 32
3.5.2 TIME-VARYING PARAMETER MODELS 33
3.5.2.1 THE RANDOM COEFFICIENT MODEL 34
3.5.2.2 THE RANDOM WALK MODEL 34
3.5.2.3 THE MEAN REVERTING MODEL 35
3.5.2.4 THE MOVING MEAN REVERTING MODEL 36
3.5.3 INITIAL VALUES 36
3.6 MODEL DIAGNOSTICS 36
3.6.1 RESIDUALS 37
3.6.1.1 GENERALIZED RECURSIVE RESIDUALS 37
3.6.1.2 GENERALIZED LEAST SQUARES RESIDUALS 37
3.6.2 GOODNESS OF FIT 38
3.6.2.1 PREDICTION ERROR VARIANCE 38
3.6.2.2 COEFFICIENT OF DETERMINATION 39
3.6.2.3 INFORMATION CRITERIA 39
3.6.3 DIAGNOSTICS 39
3.7 ILLUSTRATION: HOW TO SPECIFY THE MMR MODEL FOR ESTIMATION USING SSF
PACK 40
4 MARKOV REGIME SWITCHING 43
4.1 BASIC CONCEPTS 44
4.1.1 INDEPENDENT MIXTURE DISTRIBUTIONS 44
4.1.2 MARKOV CHAINS 46
4.2 THE BASIC HIDDEN MARKOV MODEL 48
4.3 PARAMETER ESTIMATION 49
4.3.1 THE LIKELIHOOD FUNCTION 50
4.3.2 DIRECT NUMERICAL MAXIMIZATION 50
4.3.2.1 FORWARD-BACKWARD PROBABILITIES 51
4.3.2.2 RECURSIVE EVALUATION OF THE LOGLIKELIHOOD 51
4.3.3 STANDARD ERRORS OF ML ESTIMATES 52
4.4 FORECASTING AND DECODING 52
4.4.1 FORECAST DISTRIBUTIONS 53
4.4.2 DECODING 53
4.4.2.1 LOCAL DECODING 53
4.4.2.2 STATE PREDICTIONS 54
4.4.2.3 GLOBAL DECODING 54
IMAGE 3
CONTENTS IX
4.5 MODEL SELECTION AND VALIDATION 56
5 CONDITIONAL HETEROSKEDASTICITY MODELS 57
5.1 AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY 58
5.1.1 THE GARCH(P, Q) MODEL 59
5.1.1.1 STATISTICAL PROPERTIES 60
5.1.1.2 FORECASTING 61
5.1.2 NONLINEAR EXTENSIONS 61
5.1.2.1 EXPONENTIAL GARCH 62
5.1.2.2 GJR-GARCH 62
5.1.2.3 TESTING FOR ASYMMETRIC EFFECTS 63
5.1.3 NON-GAUSSIAN CONDITIONAL DENSITIES 64
5.1.4 PARAMETER ESTIMATION 65
5.2 STOCHASTIC VOLATILITY 65
5.2.1 THE BASIC STOCHASTIC VOLATILITY MODEL 66
5.2.1.1 LINEARIZED REPRESENTATION 67
5.2.1.2 STATISTICAL PROPERTIES 67
5.2.2 ALTERNATIVE ESTIMATION PROCEDURES 68
5.2.2.1 METHODS OF MOMENTS AND QUASI MAXIMUM LIKELIHOOD . . 68 5.2.2.2
MARKOV CHAIN MONTE CARLO 69
5.2.2.3 MONTE CARLO LIKELIHOOD 70
5.2.3 EFFICIENT MONTE CARLO LIKELIHOOD ESTIMATION 70
5.2.3.1 THE LIKELIHOOD FUNCTION 71
5.2.3.2 IMPORTANCE SAMPLING 71
5.2.3.3 FILTERING, SMOOTHING AND FORECASTING 73
5.2.4 EXTENSIONS 74
5.2.4.1 HEAVY-TAILED DISTRIBUTED ERRORS 74
5.2.4.2 ASYMMETRIC EFFECTS 74
5.3 MULTIVARIATE CONDITIONAL HETEROSKEDASTICITY 75
5.3.1 MULTIVARIATE GARCH 76
5.3.1.1 THE VECH MODEL 76
5.3.1.2 THE DIAGONAL VECH MODEL 77
5.3.1.3 THE BEKK MODEL 77
5.3.1.4 THE CONSTANT CONDITIONAL CORRELATION MODEL 78
5.3.1.5 THE DYNAMIC CONDITIONAL CORRELATION MODEL 79
5.3.2 MULTIVARIATE STOCHASTIC VOLATILITY 80
6 TIME-VARYING MARKET BETA RISK OF PAN-EUROPEAN SECTORS 83
6.1 THE UNCONDITIONAL BETA IN THE CAPM 84
6.2 MODELING CONDITIONAL BETAS 86
6.2.1 GARCH CONDITIONAL BETAS 88
6.2.2 STOCHASTIC VOLATILITY CONDITIONAL BETAS 92
6.2.3 KAIMAN FILTER BASED APPROACHES 94
6.2.3.1 THE RANDOM WALK MODEL 95
IMAGE 4
CONTENTS
6.2.3.2 THE MEAN REVERTING MODEL 96
6.2.3.3 THE MOVING MEAN REVERTING MODEL 100
6.2.3.4 THE GENERALIZED RANDOM WALK MODEL 100
6.2.4 MARKOV SWITCHING BASED APPROACHES 105
6.3 ANALYSIS OF EMPIRICAL RESULTS 110
6.3.1 COMPARISON OF CONDITIONAL BETA ESTIMATES 110
6.3.2 IN-SAMPLE FORECASTING ACCURACY 112
6.3.3 OUT-OF-SAMPLE FORECASTING ACCURACY 115
6.3.3.1 STEP I: OUT-OF-SAMPLE PERIOD OF 100 WEEKS TO COMPARE ALL
CONDITIONAL MODELING TECHNIQUES 116
6.3.3.2 STEP II: OUT-OF-SAMPLE PERIOD OF TEN YEARS TO IDENTIFY THE
OVERALL BEST MODELING APPROACH 119
6.4 CONCLUDING REMARKS 119
A KAIMAN FILTER BASED CONDITIONAL MULTIFACTOR PRICING MODEL 123 7.1
FACTOR MODELING 124
7.1.1 FACTOR TAXONOMY 126
7.1.1.1 MACROECONOMIC FACTORS 126
7.1.1.2 FUNDAMENTAL FACTORS 127
7.1.1.3 MOMENTUM AND REVERSAL 128
7.1.1.4 STATISTICAL FACTORS 128
7.1.2 NUMBER OF FACTORS 128
7.1.3 TIME-VARYING FACTOR LOADINGS 129
7.2 SPECIFICATION OF A CONDITIONAL MULTIFACTOR RISK MODEL 130
7.2.1 TIME SERIES REPRESENTATION 130
7.2.2 CROSS-SECTIONAL REGRESSIONS 131
7.2.2.1 THE FAMA-MACBETH APPROACH 131
7.2.2.2 ECONOMETRIC ISSUES 132
7.3 THE RISK FACTORS 132
7.3.1 MACROECONOMIC RISK VARIABLES 134
7.3.1.1 EUROPEAN TERM STRUCTURE 134
7.3.1.2 OIL PRICE 134
7.3.1.3 DOLLAR 134
7.3.2 FUNDAMENTAL RISK VARIABLES 135
7.3.2.1 SIZE 135
7.3.2.2 VALUE-GROWTH-SPREAD 135
7.3.3 THE MARKET FACTOR 136
7.3.4 SUMMARY STATISTICS 137
7.4 EMPIRICAL RESULTS 139
7.4.1 ESTIMATION OF FACTOR LOADINGS 142
7.4.2 OUT-OF-SAMPLE FORECASTING PERFORMANCE 146
7.4.3 PRACTICAL RELEVANCE OF TIME-VARIATION IN FACTOR LOADINGS 147
7.4.3.1 RISK PRICING 147
7.4.3.2 PORTFOLIO MANAGEMENT PERSPECTIVE 150
IMAGE 5
CONTENTS XI
7.5 CONCLUDING REMARKS 154
8 CONCLUSION AND OUTLOOK 155
A A BRIEF REVIEW OF ASSET PRICING THEORY 159
A.I THE DISCOUNT FACTOR VIEW OF ASSET PRICING 159
A.2 THE CONSUMPTION-BASED MODEL 160
A.3 ALTERNATIVE ASSET PRICING MODELS 160
B FIGURES 163
C TABLES 177
REFERENCES 191
|
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| spelling | Mergner, Sascha 1975- Verfasser (DE-588)135727626 aut Applications of state space models in finance an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios Sascha Mergner Göttingen Univ.-Verl. Göttingen 2009 XXV, 202 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Zusätzliches Online-Angebot unter http://www.sub.uni-goettingen.de Zugl.: Göttingen, Univ., Diss., 2008 u. d. T.: Mergner, Sascha: Applications of advanced time series models to analyze the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios Zustandsraum (DE-588)4132647-7 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Kreditmarkt (DE-588)4073788-3 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Kreditmarkt (DE-588)4073788-3 s Zeitreihenanalyse (DE-588)4067486-1 s Zustandsraum (DE-588)4132647-7 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021181912&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Mergner, Sascha 1975- Applications of state space models in finance an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios Zustandsraum (DE-588)4132647-7 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Kreditmarkt (DE-588)4073788-3 gnd |
| subject_GND | (DE-588)4132647-7 (DE-588)4067486-1 (DE-588)4073788-3 (DE-588)4113937-9 |
| title | Applications of state space models in finance an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios |
| title_auth | Applications of state space models in finance an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios |
| title_exact_search | Applications of state space models in finance an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios |
| title_full | Applications of state space models in finance an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios Sascha Mergner |
| title_fullStr | Applications of state space models in finance an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios Sascha Mergner |
| title_full_unstemmed | Applications of state space models in finance an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios Sascha Mergner |
| title_short | Applications of state space models in finance |
| title_sort | applications of state space models in finance an empirical analysis of the time varying relationship between macroeconomics fundamentals and pan european industry portfolios |
| title_sub | an empirical analysis of the time-varying relationship between macroeconomics, fundamentals and pan-European industry portfolios |
| topic | Zustandsraum (DE-588)4132647-7 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd Kreditmarkt (DE-588)4073788-3 gnd |
| topic_facet | Zustandsraum Zeitreihenanalyse Kreditmarkt Hochschulschrift |
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