Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 197-214 |
| Seitenumfang | 18 |
| Fachzeitschrift | Astin bulletin |
| Jahrgang | 29 |
| Ausgabenummer | 2 |
| Publikationsstatus | Elektronisch veröffentlicht (E-Pub) - 29 Aug. 2014 |
Abstract
Numerical evaluation of compound distributions is one of the central numerical tasks in insurance mathematics. Two widely used techniques are Panjer recursion and transform methods. Many authors have pointed out that aliasing errors imply the need to consider the whole distribution if transform methods are used, a potential drawback especially for heavy-tailed distributions. We investigate the magnitude of aliasing errors and show that this problem can be solved by a suitable change of measure.
ASJC Scopus Sachgebiete
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Bilanzierung
- Volkswirtschaftslehre, Ökonometrie und Finanzen (insg.)
- Finanzwesen
- Volkswirtschaftslehre, Ökonometrie und Finanzen (insg.)
- Volkswirtschaftslehre und Ökonometrie
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in: Astin bulletin, Jahrgang 29, Nr. 2, 29.08.2014, S. 197-214.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Computation of compound distributions i
T2 - Aliasing errors and exponential tilting
AU - Grübel, Rudolf
AU - Hermesmeier, Renate
PY - 2014/8/29
Y1 - 2014/8/29
N2 - Numerical evaluation of compound distributions is one of the central numerical tasks in insurance mathematics. Two widely used techniques are Panjer recursion and transform methods. Many authors have pointed out that aliasing errors imply the need to consider the whole distribution if transform methods are used, a potential drawback especially for heavy-tailed distributions. We investigate the magnitude of aliasing errors and show that this problem can be solved by a suitable change of measure.
AB - Numerical evaluation of compound distributions is one of the central numerical tasks in insurance mathematics. Two widely used techniques are Panjer recursion and transform methods. Many authors have pointed out that aliasing errors imply the need to consider the whole distribution if transform methods are used, a potential drawback especially for heavy-tailed distributions. We investigate the magnitude of aliasing errors and show that this problem can be solved by a suitable change of measure.
KW - aliasing
KW - change of measure
KW - Fourier transformation
KW - random sums
KW - ruin probabilities
KW - Total claim size distribution
UR - http://www.scopus.com/inward/record.url?scp=85011437009&partnerID=8YFLogxK
U2 - 10.2143/AST.29.2.504611
DO - 10.2143/AST.29.2.504611
M3 - Article
AN - SCOPUS:85011437009
VL - 29
SP - 197
EP - 214
JO - Astin bulletin
JF - Astin bulletin
SN - 0515-0361
IS - 2
ER -