Forecasting financial time series using model averaging:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | German |
| Published: |
Rotterdam
Erasmus Univ. Rotterdam
2007
|
| Series: | Tinbergen Institute research series
415 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Zugl.: Rotterdam, Erasmus Univ., Diss., 2007 |
| Physical Description: | 192 S. graph. Darst. |
| ISBN: | 9789051709148 |
Staff View
MARC
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| 100 | 1 | |a Ravazzolo, Francesco |d 1979- |e Verfasser |0 (DE-588)133780864 |4 aut | |
| 245 | 1 | 0 | |a Forecasting financial time series using model averaging |c Francesco Ravazzolo |
| 264 | 1 | |a Rotterdam |b Erasmus Univ. Rotterdam |c 2007 | |
| 300 | |a 192 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 1 | |a Tinbergen Institute research series |v 415 | |
| 500 | |a Zugl.: Rotterdam, Erasmus Univ., Diss., 2007 | ||
| 650 | 7 | |a Finanzwirtschaft |2 swd | |
| 650 | 7 | |a Kapitalmarkt |2 swd | |
| 650 | 7 | |a Prognose |2 swd | |
| 650 | 7 | |a Zeitreihenanalyse |2 swd | |
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Record in the Search Index
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| adam_text | FORECASTING FINANCIAL TIME SERIES USING MODEL AVERAGING VOORSPELLEN VAN
FINANCIELE TIJDREEKSEN MET BEHULP VAN MODEL WEGINGEN THESIS TO OBTAIN
THE DEGREE OF DOCTOR FROM THE ERASMUS UNIVERSITY ROTTERDAM BY COMMAND OF
THE RECTOR MAGNIFICUS PROF.DR. S.W.J. LAMBERTS AND IN ACCORDANCE WITH
THE DECISION OF DOCTORAL BOARD. THE PUBLIC DEFENCE SHALL BE HELD ON
FRIDAY, 23 NOVEMBER 2007 AT 11.00 HRS BY FRANCESCO RAVAZZOLO BORN IN
PADOVA, ITALY ERASMUS UNIVERSITEIT ROTTERDAM CONTENTS PREFACE 1 LIST OF
TABLES 7 LIST OF FIGURES 9 1 INTRODUCTION 11 1.1 MOTIVATION 11 1.2
OUTLINE 13 2 PREDICTIVE GAINS FROM FORECAST COMBINATIONS USING TIME
VARYING MODEL WEIGHTS 19 2.1 INTRODUCTION 19 2.2 FORECAST COMBINATIONS
21 2.2.1 SIMPLE COMBINATION SCHEMES 23 2.2.2 ESTIMATED WEIGHT
COMBINATION SCHEMES 24 2.2.3 BAYESIAN MODEL AVERAGING 25 2.3 SIMULATION
EXERCISES 27 2.3.1 VARYING CORRELATIONS BETWEEN PREDICTORS 29 2.3.2
MISSPECIFICATION 33 2.3.3 STRUCTURAL CHANGE 35 2.3.4 FAT TAILS 36 2.3.5
SUMMARY OF FINDINGS 36 2.4 EMPIRICAL ILLUSTRATION 37 2.4.1 DATA AND
EVALUATION 38 2.4.2 EMPIRICAL RESULTS 41 2.5 CONCLUSION 46 2A COMPARISON
OF RECURSIVE LEAST SQUARES AND TIME VARYING MODEL WEIGHT COMBINATIONS 48
2B GRAPHICAL EXAMPLES 51 3 CONTENTS 2C PROPERTIES OF MULTIVARIATE NORMAL
DISTRIBUTION 52 2D PREDICTIVE DENSITIES AND MARGINAL LIKELIHOOD FOR
LINEAR MODELS . 54 2E ESTIMATION OF THE BAYESIAN TIME VARYING MODEL
WEIGHT COMBINATIONS .... 55 BAYESIAN MODEL AVERAGING IN THE PRESENCE OF
STRUCTURAL BREAKS 59 3.1 INTRODUCTION 59 3.2 METHODOLOGY 61 3.2.1 THE
MODEL : 61 3.2.2 PRIOR SPECIFICATION AND POSTERIOR SIMULATION 63 3.2.3
USING THE POSTERIOR RESULTS 65 3.3 MODEL UNCERTAINTY AND STRUCTURAL
BREAKS IN RETURN FORECASTING MODELS FOR THE S&P 500 67 3.3.1 DATA 67
3.3.2 PRIOR SPECIFICATION 68 3.3.3 FULL-SAMPLE ESTIMATION RESULTS 68 3.4
ACTIVE INVESTMENT STRATEGIES ALLOWING FOR MODEL UNCERTAINTY AND
STRUCTURAL BREAKS 78 3.4.1 A UTILITY-BASED PERFORMANCE MEASURE 78 3.4.2
EMPIRICAL RESULTS 81 3.5 CONCLUSION 92 PREDICTING THE TERM STRUCTURE OF
INTEREST RATES 95 4.1 INTRODUCTION 95 4.2 DATA 98 4.2.1 YIELD DATA 98
4.2.2 MACROECONOMIC DATA 100 4.3 MODELS 105 4.3.1 ADDING MACRO FACTORS
105 4.3.2 MODELS 106 4.4 FORECASTING ILL 4.4.1 FORECAST PROCEDURE ILL
4.4.2 FORECAST EVALUATION 112 4.4.3 FORECASTING RESULTS: INDIVIDUAL
MODELS 112 4.5 FORECAST COMBINATION 132 4.5.1 FORECAST COMBINATION:
SCHEMES 132 4.5.2 FORECAST COMBINATION RESULTS 135 4.6 CONCLUSION 140 4A
INDIVIDUAL MODELS 142 4A.1 AR MODEL * . 142 4A.2 VAR MODEL 142 4A.3
NELSON-SIEGEL MODEL 144 4A.4 AFFINE MODEL 146 4B BAYESIAN MODEL
AVERAGING 147 4C PRIOR SPECIFICATION 147 5 THE POWER OF WEATHER - - 151
5.1 INTRODUCTION 151 5.2 DAY-AHEAD POWER MARKETS 153 5.3 DATA 155 5.3.1
ELECTRICITY PRICES 155 5.3.2 WEATHER FORECASTS 157 5.4 FORECASTING
MODELS 160 5.4.1 MODEL 1: ARMA 161 5.4.2 MODEL 2: ARMAX 161 5.4.3 MODEL
3: ARMAXW 162 5.4.4 MODEL 4: ARMAX-GARCH 163 5.4.5 MODEL 5: ARMAXW-GARCH
163 5.4.6 MODEL 6: ARMAXW-GARCHW 164 5.5 EMPIRICAL RESULTS 164 5.5.1
IN-SAMPLE ANALYSIS: OSLO CASE 165 5.5.2 OUT-OF-SAMPLE ANALYSIS: OSLO
CASE 168 5.5.3 FURTHER APPLICATION: EASTERN DENMARK CASE 171 5.5.4 A
DIFFERENT STORY: THE NETHERLANDS CASE 172 5.6 CONCLUSION 173 6 SUMMARY
175 NEDERLANDSE SAMENVATTING (SUMMARY IN DUTCH) 179 BIBLIOGRAPHY 183
|
| adam_txt |
FORECASTING FINANCIAL TIME SERIES USING MODEL AVERAGING VOORSPELLEN VAN
FINANCIELE TIJDREEKSEN MET BEHULP VAN MODEL WEGINGEN THESIS TO OBTAIN
THE DEGREE OF DOCTOR FROM THE ERASMUS UNIVERSITY ROTTERDAM BY COMMAND OF
THE RECTOR MAGNIFICUS PROF.DR. S.W.J. LAMBERTS AND IN ACCORDANCE WITH
THE DECISION OF DOCTORAL BOARD. THE PUBLIC DEFENCE SHALL BE HELD ON
FRIDAY, 23 NOVEMBER 2007 AT 11.00 HRS BY FRANCESCO RAVAZZOLO BORN IN
PADOVA, ITALY ERASMUS UNIVERSITEIT ROTTERDAM CONTENTS PREFACE 1 LIST OF
TABLES 7 LIST OF FIGURES 9 1 INTRODUCTION 11 1.1 MOTIVATION 11 1.2
OUTLINE 13 2 PREDICTIVE GAINS FROM FORECAST COMBINATIONS USING TIME
VARYING MODEL WEIGHTS 19 2.1 INTRODUCTION 19 2.2 FORECAST COMBINATIONS
21 2.2.1 SIMPLE COMBINATION SCHEMES 23 2.2.2 ESTIMATED WEIGHT
COMBINATION SCHEMES 24 2.2.3 BAYESIAN MODEL AVERAGING 25 2.3 SIMULATION
EXERCISES 27 2.3.1 VARYING CORRELATIONS BETWEEN PREDICTORS 29 2.3.2
MISSPECIFICATION 33 2.3.3 STRUCTURAL CHANGE 35 2.3.4 FAT TAILS 36 2.3.5
SUMMARY OF FINDINGS 36 2.4 EMPIRICAL ILLUSTRATION 37 2.4.1 DATA AND
EVALUATION 38 2.4.2 EMPIRICAL RESULTS 41 2.5 CONCLUSION 46 2A COMPARISON
OF RECURSIVE LEAST SQUARES AND TIME VARYING MODEL WEIGHT COMBINATIONS 48
2B GRAPHICAL EXAMPLES 51 3 CONTENTS 2C PROPERTIES OF MULTIVARIATE NORMAL
DISTRIBUTION 52 2D PREDICTIVE DENSITIES AND MARGINAL LIKELIHOOD FOR
LINEAR MODELS "". 54 2E ESTIMATION OF THE BAYESIAN TIME VARYING MODEL
WEIGHT COMBINATIONS . 55 BAYESIAN MODEL AVERAGING IN THE PRESENCE OF
STRUCTURAL BREAKS 59 3.1 INTRODUCTION 59 3.2 METHODOLOGY 61 3.2.1 THE
MODEL : 61 3.2.2 PRIOR SPECIFICATION AND POSTERIOR SIMULATION 63 3.2.3
USING THE POSTERIOR RESULTS 65 3.3 MODEL UNCERTAINTY AND STRUCTURAL
BREAKS IN RETURN FORECASTING MODELS FOR THE S&P 500 67 3.3.1 DATA 67
3.3.2 PRIOR SPECIFICATION 68 3.3.3 FULL-SAMPLE ESTIMATION RESULTS 68 3.4
ACTIVE INVESTMENT STRATEGIES ALLOWING FOR MODEL UNCERTAINTY AND
STRUCTURAL BREAKS 78 3.4.1 A UTILITY-BASED PERFORMANCE MEASURE 78 3.4.2
EMPIRICAL RESULTS 81 3.5 CONCLUSION 92 PREDICTING THE TERM STRUCTURE OF
INTEREST RATES 95 4.1 INTRODUCTION 95 4.2 DATA 98 4.2.1 YIELD DATA 98
4.2.2 MACROECONOMIC DATA 100 4.3 MODELS 105 4.3.1 ADDING MACRO FACTORS
105 4.3.2 MODELS 106 4.4 FORECASTING ILL 4.4.1 FORECAST PROCEDURE ILL
4.4.2 FORECAST EVALUATION 112 4.4.3 FORECASTING RESULTS: INDIVIDUAL
MODELS 112 4.5 FORECAST COMBINATION 132 4.5.1 FORECAST COMBINATION:
SCHEMES 132 4.5.2 FORECAST COMBINATION RESULTS 135 4.6 CONCLUSION 140 4A
INDIVIDUAL MODELS 142 4A.1 AR MODEL * . 142 4A.2 VAR MODEL 142 4A.3
NELSON-SIEGEL MODEL 144 4A.4 AFFINE MODEL 146 4B BAYESIAN MODEL
AVERAGING 147 4C PRIOR SPECIFICATION 147 5 THE POWER OF WEATHER - -" 151
5.1 INTRODUCTION 151 5.2 DAY-AHEAD POWER MARKETS 153 5.3 DATA 155 5.3.1
ELECTRICITY PRICES 155 5.3.2 WEATHER FORECASTS 157 5.4 FORECASTING
MODELS 160 5.4.1 MODEL 1: ARMA 161 5.4.2 MODEL 2: ARMAX 161 5.4.3 MODEL
3: ARMAXW 162 5.4.4 MODEL 4: ARMAX-GARCH 163 5.4.5 MODEL 5: ARMAXW-GARCH
163 5.4.6 MODEL 6: ARMAXW-GARCHW 164 5.5 EMPIRICAL RESULTS 164 5.5.1
IN-SAMPLE ANALYSIS: OSLO CASE 165 5.5.2 OUT-OF-SAMPLE ANALYSIS: OSLO
CASE 168 5.5.3 FURTHER APPLICATION: EASTERN DENMARK CASE 171 5.5.4 A
DIFFERENT STORY: THE NETHERLANDS CASE 172 5.6 CONCLUSION 173 6 SUMMARY
175 NEDERLANDSE SAMENVATTING (SUMMARY IN DUTCH) 179 BIBLIOGRAPHY 183 |
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| index_date | 2024-07-02T19:23:14Z |
| indexdate | 2024-07-09T21:09:46Z |
| institution | BVB |
| isbn | 9789051709148 |
| language | German |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016250976 |
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| publishDate | 2007 |
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| publisher | Erasmus Univ. Rotterdam |
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| series | Tinbergen Institute research series |
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| spelling | Ravazzolo, Francesco 1979- Verfasser (DE-588)133780864 aut Forecasting financial time series using model averaging Francesco Ravazzolo Rotterdam Erasmus Univ. Rotterdam 2007 192 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Tinbergen Institute research series 415 Zugl.: Rotterdam, Erasmus Univ., Diss., 2007 Finanzwirtschaft swd Kapitalmarkt swd Prognose swd Zeitreihenanalyse swd (DE-588)4113937-9 Hochschulschrift gnd-content Tinbergen Institute research series 415 (DE-604)BV004252095 415 GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016250976&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Ravazzolo, Francesco 1979- Forecasting financial time series using model averaging Tinbergen Institute research series Finanzwirtschaft swd Kapitalmarkt swd Prognose swd Zeitreihenanalyse swd |
| subject_GND | (DE-588)4113937-9 |
| title | Forecasting financial time series using model averaging |
| title_auth | Forecasting financial time series using model averaging |
| title_exact_search | Forecasting financial time series using model averaging |
| title_exact_search_txtP | Forecasting financial time series using model averaging |
| title_full | Forecasting financial time series using model averaging Francesco Ravazzolo |
| title_fullStr | Forecasting financial time series using model averaging Francesco Ravazzolo |
| title_full_unstemmed | Forecasting financial time series using model averaging Francesco Ravazzolo |
| title_short | Forecasting financial time series using model averaging |
| title_sort | forecasting financial time series using model averaging |
| topic | Finanzwirtschaft swd Kapitalmarkt swd Prognose swd Zeitreihenanalyse swd |
| topic_facet | Finanzwirtschaft Kapitalmarkt Prognose Zeitreihenanalyse Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016250976&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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