Mortality modeling: machine learning and mortality shocks:
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| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
Stuttgart
Fraunhofer Verlag
[2022]
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis Inhaltsverzeichnis |
| Physical Description: | xviii, 178 Seiten Diagramme 21 cm |
| ISBN: | 9783839618318 3839618312 |
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MARC
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| 100 | 1 | |a Schnürch, Simon |e Verfasser |0 (DE-588)1273780213 |4 aut | |
| 245 | 1 | 0 | |a Mortality modeling: machine learning and mortality shocks |c Simon Schnürch |
| 264 | 1 | |a Stuttgart |b Fraunhofer Verlag |c [2022] | |
| 300 | |a xviii, 178 Seiten |b Diagramme |c 21 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 502 | |b Dissertation |c Technische Universität Kaiserslautern |d 2022 | ||
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| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 653 | |a Mortality modeling | ||
| 653 | |a Machine learning | ||
| 653 | |a Mortality shocks | ||
| 653 | |a Lee-Carter model | ||
| 653 | |a Neural networks | ||
| 653 | |a Versicherungsmathematiker/Aktuare | ||
| 653 | |a Demographen | ||
| 653 | |a Statistiker | ||
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Record in the Search Index
| _version_ | 1813169996073795584 |
|---|---|
| adam_text |
CONTENTS
ABSTRACT
.
III
ZUSAMMENFASSUNG
.
V
DANKSAGUNG
.
VII
FREQUENTLY
USED
NOTATION
.
XIII
LIST
OF
ACRONYMS
AND
ABBREVIATIONS
.
XVII
1
INTRODUCTION
.
1
1.1
OUTLINE
OF
THIS
THESIS
.
4
1.2
PRELIMINARIES
.
5
2
STOCHASTIC
MORTALITY
MODELS:
A
REVIEW
.
7
2.1
SINGLE-POPULATION
MORTALITY
MODELS
.
7
2.1.1
THE
LEE-CARTER
MODEL
.
7
2.1.2
THE
CAIRNS-BLAKC-DOWD
MODEL
.
9
2.1.3
MODEL
EXTENSIONS:
COHORT
EFFECTS
AND
EXTERNAL
INFORMATION
.
10
2.1.4
OTHER
MODELING
APPROACHES
.
12
2.2
MULTI-POPULATION
MORTALITY
MODELS
.
12
2.2.1
THE
INDIVIDUAL
LEE-CARTER
MODEL
.
12
2.2.2
THE
AUGMENTED
COMMON
FACTOR
MODEL
.
14
2.2.3
THE
COMMON
AGE
EFFECT
MODEL
.
15
2.2.4
APPLYING
CLUSTERING
METHODS
.
17
2.2.5
MODELS
BASED
ON
REFERENCE
POPULATIONS
.
18
2.2.6
OTHER
MODELING
APPROACHES
.
19
2.3
MODEL
CALIBRATION
.
20
2.3.1
POISSON
MAXIMUM
LIKELIHOOD
ESTIMATION
.
20
2.3.2
THE
BAYESIAN
INFORMATION
CRITERION
.
21
2.4
FORECASTS
AND
UNCERTAINTY
.
22
2.4.1
POINT
FORECASTS
.
22
2.4.2
PREDICTION
UNCERTAINTY
.
22
2.4.3
INTERVAL
FORECASTS
.
24
2.4.4
EVALUATING
FORECASTING
PERFORMANCE
.
25
2.5
MACHINE
LEARNING
APPLICATIONS
IN
MORTALITY
MODELING
.
27
3
CLUSTERING-BASED
EXTENSIONS
OF
THE
COMMON
AGE
EFFECT
MODEL
.
29
3.1
ALGORITHMS
.
30
3.1.1
K-MEANS
CLUSTERING
.
30
3.1.2
AUGMENTED
COMMON
FACTOR
CLUSTERING
.
32
3.1.3
LIKELIHOOD-RATIO-BASED
CLUSTERING
.
35
3.1.4
FUZZY
MAXIMUM
LIKELIHOOD
CLUSTERING
.
39
3.1.5
CALCULATING
THE
NUMBER
OF
FREE
PARAMETERS
.
51
3.2
EMPIRICAL
RESULTS
.
51
3.2.1
CLUSTERING
RESULTS
.
52
3.2.2
GOODNESS
OF
FIT
.
58
3.2.3
FORECASTING
PERFORMANCE
.
60
3.3
CONCLUSION
.
63
3.
A
RESULTS
FOR
THE
AGE
GROUP
18
TO
52
.
65
3.B
ROBUSTNESS
CHECK:
OUT-OF-SAMPLE
RESULTS
.
70
4
FORECASTING
MORTALITY
WITH
NEURAL
NETWORKS
.
73
4.1
NEURAL
NETWORK
ARCHITECTURES
.
75
4.1.1
FEED-FORWARD
NEURAL
NETWORKS
.
75
4.1.2
HYPERPARAMETER
SELECTION
AND
MODEL
TRAINING
.
76
4.1.3
RECURRENT
NEURAL
NETWORKS
.
79
4.1.4
CONVOLUTIONAL
NEURAL
NETWORKS
.
80
4.2
PREDICTION
UNCERTAINTY
.
87
4.2.1
LITERATURE
REVIEW
.
88
4.2.2
OUR
APPROACH
.
90
4.3
EMPIRICAL
RESULTS
.
91
4.3.1
GOODNESS
OF
FIT
.
93
4.3.2
FORECASTING
PERFORMANCE
.
94
4.3.3
PREDICTION
UNCERTAINTY
.
98
4.3.4
ROBUSTNESS
CHECK
.
100
4.3.5
LONG-TERM
FORECASTS
AND
ANNUITY
VALUES
.
103
4.4
CONCLUSION
.
106
4
.
A
HYPERPARAMETERS
OF
THE
NEURAL
NETWORKS
.
108
5
COVID-19-TYPE
MORTALITY
SHOCKS:
IMPACT
AND
MODEL
ADJUSTMENTS
.
ILL
5.1
DATA
.
113
5.2
MEASURING
THE
IMPACT
OF
MORTALITY
SHOCKS
.
114
5.2.1
MORTALITY
STATISTICS
.
114
5.2.2
MORTALITY
FORECASTS
AND
INSURANCE
CONTRACTS
.
116
5.2.3
IMPACT
OF
A
MORTALITY
SHIFT
ON
PARAMETERS
AND
FORECASTS
.
118
5.3
EMPIRICAL
RESULTS:
IMPACT
OF
THE
COVID-19
PANDEMIC
.
122
5.3.1
MORTALITY
DEVELOPMENT
IN
2020
.
122
5.3.2
MORTALITY
SHOCK
IN
THE
JUMP-OFF
YEAR
.
127
5.3.3
MORTALITY
SHOCK
BEFORE
THE
JUMP-OFF
YEAR
.
128
5.3.4
ROBUSTNESS
CHECK:
USING
A
DIFFERENT
CALIBRATION
METHOD
.
130
5.3.5
ROBUSTNESS
CHECK:
THE
CAIRNS-BLAKE-DOWD
MODEL
.
131
5.4
ADJUSTING
MODELS
FOR
MORTALITY
SHOCKS
.
131
5.4.1
FORECASTING
THE
PERIOD
EFFECT
WITH
AN
ARIMA
MODEL
.
132
5.4.2
DEALING
WITH
MORTALITY
SHOCKS
IN
AN
ARIMA
SETTING
.
133
5.4.3
A
MODIFIED
SHOCK
MODEL
CALIBRATION
PROCEDURE
.
137
5.5
EMPIRICAL
RESULTS:
EVALUATION
OF
MODEL
ADJUSTMENTS
.
137
5.5.1
CHECKING
THE
NORMAL
DISTRIBUTION
ASSUMPTION
.
137
5.5.2
EVALUATING
OUR
SHOCK
MODEL
CALIBRATION
PROCEDURE
.
139
5.5.3
MODEL
PARAMETERS
.
139
5.5.4
GOODNESS
OF
FIT
.
141
5.5.5
BACKTEST
.
143
5.5.6
LIFE
INSURANCE
AND
ANNUITY
VALUES
.
144
5.5.7
MODEL
PERFORMANCE
UNDER
DIFFERENT
FUTURE
SCENARIOS
.
146
5.6
CONCLUSION
.
148
5.
A
RECALIBRATION
CONSISTENCY
OF
THE
LC
MODEL
.
150
5.B
ADDITIONAL
FIGURES
FOR
SECTION
5.3
.
151
5.C
DEFINING
SCENARIOS
OF
FUTURE
MORTALITY
.
156
5.D
ANNUITY
VALUES
.
158
6
OUTLOOK
.159
BIBLIOGRAPHY
.
161
SCIENTIFIC
CAREER
.
175
WISSENSCHAFTLICHER
WERDEGANG
.
177 |
| adam_txt |
CONTENTS
ABSTRACT
.
III
ZUSAMMENFASSUNG
.
V
DANKSAGUNG
.
VII
FREQUENTLY
USED
NOTATION
.
XIII
LIST
OF
ACRONYMS
AND
ABBREVIATIONS
.
XVII
1
INTRODUCTION
.
1
1.1
OUTLINE
OF
THIS
THESIS
.
4
1.2
PRELIMINARIES
.
5
2
STOCHASTIC
MORTALITY
MODELS:
A
REVIEW
.
7
2.1
SINGLE-POPULATION
MORTALITY
MODELS
.
7
2.1.1
THE
LEE-CARTER
MODEL
.
7
2.1.2
THE
CAIRNS-BLAKC-DOWD
MODEL
.
9
2.1.3
MODEL
EXTENSIONS:
COHORT
EFFECTS
AND
EXTERNAL
INFORMATION
.
10
2.1.4
OTHER
MODELING
APPROACHES
.
12
2.2
MULTI-POPULATION
MORTALITY
MODELS
.
12
2.2.1
THE
INDIVIDUAL
LEE-CARTER
MODEL
.
12
2.2.2
THE
AUGMENTED
COMMON
FACTOR
MODEL
.
14
2.2.3
THE
COMMON
AGE
EFFECT
MODEL
.
15
2.2.4
APPLYING
CLUSTERING
METHODS
.
17
2.2.5
MODELS
BASED
ON
REFERENCE
POPULATIONS
.
18
2.2.6
OTHER
MODELING
APPROACHES
.
19
2.3
MODEL
CALIBRATION
.
20
2.3.1
POISSON
MAXIMUM
LIKELIHOOD
ESTIMATION
.
20
2.3.2
THE
BAYESIAN
INFORMATION
CRITERION
.
21
2.4
FORECASTS
AND
UNCERTAINTY
.
22
2.4.1
POINT
FORECASTS
.
22
2.4.2
PREDICTION
UNCERTAINTY
.
22
2.4.3
INTERVAL
FORECASTS
.
24
2.4.4
EVALUATING
FORECASTING
PERFORMANCE
.
25
2.5
MACHINE
LEARNING
APPLICATIONS
IN
MORTALITY
MODELING
.
27
3
CLUSTERING-BASED
EXTENSIONS
OF
THE
COMMON
AGE
EFFECT
MODEL
.
29
3.1
ALGORITHMS
.
30
3.1.1
K-MEANS
CLUSTERING
.
30
3.1.2
AUGMENTED
COMMON
FACTOR
CLUSTERING
.
32
3.1.3
LIKELIHOOD-RATIO-BASED
CLUSTERING
.
35
3.1.4
FUZZY
MAXIMUM
LIKELIHOOD
CLUSTERING
.
39
3.1.5
CALCULATING
THE
NUMBER
OF
FREE
PARAMETERS
.
51
3.2
EMPIRICAL
RESULTS
.
51
3.2.1
CLUSTERING
RESULTS
.
52
3.2.2
GOODNESS
OF
FIT
.
58
3.2.3
FORECASTING
PERFORMANCE
.
60
3.3
CONCLUSION
.
63
3.
A
RESULTS
FOR
THE
AGE
GROUP
18
TO
52
.
65
3.B
ROBUSTNESS
CHECK:
OUT-OF-SAMPLE
RESULTS
.
70
4
FORECASTING
MORTALITY
WITH
NEURAL
NETWORKS
.
73
4.1
NEURAL
NETWORK
ARCHITECTURES
.
75
4.1.1
FEED-FORWARD
NEURAL
NETWORKS
.
75
4.1.2
HYPERPARAMETER
SELECTION
AND
MODEL
TRAINING
.
76
4.1.3
RECURRENT
NEURAL
NETWORKS
.
79
4.1.4
CONVOLUTIONAL
NEURAL
NETWORKS
.
80
4.2
PREDICTION
UNCERTAINTY
.
87
4.2.1
LITERATURE
REVIEW
.
88
4.2.2
OUR
APPROACH
.
90
4.3
EMPIRICAL
RESULTS
.
91
4.3.1
GOODNESS
OF
FIT
.
93
4.3.2
FORECASTING
PERFORMANCE
.
94
4.3.3
PREDICTION
UNCERTAINTY
.
98
4.3.4
ROBUSTNESS
CHECK
.
100
4.3.5
LONG-TERM
FORECASTS
AND
ANNUITY
VALUES
.
103
4.4
CONCLUSION
.
106
4
.
A
HYPERPARAMETERS
OF
THE
NEURAL
NETWORKS
.
108
5
COVID-19-TYPE
MORTALITY
SHOCKS:
IMPACT
AND
MODEL
ADJUSTMENTS
.
ILL
5.1
DATA
.
113
5.2
MEASURING
THE
IMPACT
OF
MORTALITY
SHOCKS
.
114
5.2.1
MORTALITY
STATISTICS
.
114
5.2.2
MORTALITY
FORECASTS
AND
INSURANCE
CONTRACTS
.
116
5.2.3
IMPACT
OF
A
MORTALITY
SHIFT
ON
PARAMETERS
AND
FORECASTS
.
118
5.3
EMPIRICAL
RESULTS:
IMPACT
OF
THE
COVID-19
PANDEMIC
.
122
5.3.1
MORTALITY
DEVELOPMENT
IN
2020
.
122
5.3.2
MORTALITY
SHOCK
IN
THE
JUMP-OFF
YEAR
.
127
5.3.3
MORTALITY
SHOCK
BEFORE
THE
JUMP-OFF
YEAR
.
128
5.3.4
ROBUSTNESS
CHECK:
USING
A
DIFFERENT
CALIBRATION
METHOD
.
130
5.3.5
ROBUSTNESS
CHECK:
THE
CAIRNS-BLAKE-DOWD
MODEL
.
131
5.4
ADJUSTING
MODELS
FOR
MORTALITY
SHOCKS
.
131
5.4.1
FORECASTING
THE
PERIOD
EFFECT
WITH
AN
ARIMA
MODEL
.
132
5.4.2
DEALING
WITH
MORTALITY
SHOCKS
IN
AN
ARIMA
SETTING
.
133
5.4.3
A
MODIFIED
SHOCK
MODEL
CALIBRATION
PROCEDURE
.
137
5.5
EMPIRICAL
RESULTS:
EVALUATION
OF
MODEL
ADJUSTMENTS
.
137
5.5.1
CHECKING
THE
NORMAL
DISTRIBUTION
ASSUMPTION
.
137
5.5.2
EVALUATING
OUR
SHOCK
MODEL
CALIBRATION
PROCEDURE
.
139
5.5.3
MODEL
PARAMETERS
.
139
5.5.4
GOODNESS
OF
FIT
.
141
5.5.5
BACKTEST
.
143
5.5.6
LIFE
INSURANCE
AND
ANNUITY
VALUES
.
144
5.5.7
MODEL
PERFORMANCE
UNDER
DIFFERENT
FUTURE
SCENARIOS
.
146
5.6
CONCLUSION
.
148
5.
A
RECALIBRATION
CONSISTENCY
OF
THE
LC
MODEL
.
150
5.B
ADDITIONAL
FIGURES
FOR
SECTION
5.3
.
151
5.C
DEFINING
SCENARIOS
OF
FUTURE
MORTALITY
.
156
5.D
ANNUITY
VALUES
.
158
6
OUTLOOK
.159
BIBLIOGRAPHY
.
161
SCIENTIFIC
CAREER
.
175
WISSENSCHAFTLICHER
WERDEGANG
.
177 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Schnürch, Simon |
| author_GND | (DE-588)1273780213 |
| author_facet | Schnürch, Simon |
| author_role | aut |
| author_sort | Schnürch, Simon |
| author_variant | s s ss |
| building | Verbundindex |
| bvnumber | BV049104386 |
| ctrlnum | (OCoLC)1370941752 (DE-599)DNB1264151608 |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV049104386 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T22:33:32Z |
| indexdate | 2024-10-17T14:00:56Z |
| institution | BVB |
| institution_GND | (DE-588)10126139-1 (DE-588)4786605-6 |
| isbn | 9783839618318 3839618312 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034365855 |
| oclc_num | 1370941752 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | xviii, 178 Seiten Diagramme 21 cm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Fraunhofer Verlag |
| record_format | marc |
| spelling | Schnürch, Simon Verfasser (DE-588)1273780213 aut Mortality modeling: machine learning and mortality shocks Simon Schnürch Stuttgart Fraunhofer Verlag [2022] xviii, 178 Seiten Diagramme 21 cm txt rdacontent n rdamedia nc rdacarrier Dissertation Technische Universität Kaiserslautern 2022 Sterblichkeit (DE-588)4057312-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Mortality modeling Machine learning Mortality shocks Lee-Carter model Neural networks Versicherungsmathematiker/Aktuare Demographen Statistiker (DE-588)4113937-9 Hochschulschrift gnd-content Sterblichkeit (DE-588)4057312-6 s Maschinelles Lernen (DE-588)4193754-5 s Statistik (DE-588)4056995-0 s DE-604 Fraunhofer-Institut für Techno- und Wirtschaftsmathematik (DE-588)10126139-1 isb Fraunhofer IRB-Verlag (DE-588)4786605-6 pbl X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2dee14104503473bb1c44a94054df079&prov=M&dok_var=1&dok_ext=htm Inhaltstext B:DE-101 application/pdf https://d-nb.info/1264151608/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034365855&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p dnb 20230213 DE-101 https://d-nb.info/provenance/plan#dnb 2\p dnb 20230213 DE-101 https://d-nb.info/provenance/plan#dnb |
| spellingShingle | Schnürch, Simon Mortality modeling: machine learning and mortality shocks Sterblichkeit (DE-588)4057312-6 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4057312-6 (DE-588)4056995-0 (DE-588)4193754-5 (DE-588)4113937-9 |
| title | Mortality modeling: machine learning and mortality shocks |
| title_auth | Mortality modeling: machine learning and mortality shocks |
| title_exact_search | Mortality modeling: machine learning and mortality shocks |
| title_exact_search_txtP | Mortality modeling: machine learning and mortality shocks |
| title_full | Mortality modeling: machine learning and mortality shocks Simon Schnürch |
| title_fullStr | Mortality modeling: machine learning and mortality shocks Simon Schnürch |
| title_full_unstemmed | Mortality modeling: machine learning and mortality shocks Simon Schnürch |
| title_short | Mortality modeling: machine learning and mortality shocks |
| title_sort | mortality modeling machine learning and mortality shocks |
| topic | Sterblichkeit (DE-588)4057312-6 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Sterblichkeit Statistik Maschinelles Lernen Hochschulschrift |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2dee14104503473bb1c44a94054df079&prov=M&dok_var=1&dok_ext=htm https://d-nb.info/1264151608/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034365855&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT schnurchsimon mortalitymodelingmachinelearningandmortalityshocks AT fraunhoferinstitutfurtechnoundwirtschaftsmathematik mortalitymodelingmachinelearningandmortalityshocks AT fraunhoferirbverlag mortalitymodelingmachinelearningandmortalityshocks |