Modelling catastrophe claims with left-truncated severity distributions

Anna Chernobai; Krzysztof Burnecki; Svetlozar Rachev; Stefan Trück; Rafał Weron

doi:10.1007/s00180-006-0011-2

Modelling catastrophe claims with left-truncated severity distributions

Anna Chernobai, Krzysztof Burnecki, Svetlozar Rachev, Stefan Trück, Rafał Weron

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

In this paper, we present a procedure for consistent estimation of the severity and frequency distributions based on incomplete insurance data and demonstrate that ignoring the thresholds leads to a serious underestimation of the ruin probabilities. The event frequency is modelled with a non- homogeneous Poisson process with a sinusoidal intensity rate function. The choice of an adequate loss distribution is conducted via the in-sample goodness-of-fit procedures and forecasting, using classical and robust methodologies.

Original language	English (US)
Pages (from-to)	537-555
Number of pages	19
Journal	Computational Statistics
Volume	21
Issue number	3-4
DOIs	https://doi.org/10.1007/s00180-006-0011-2
State	Published - Dec 2006
Externally published	Yes

Keywords

Loss distribution
Natural catastrophe
Property insurance
Ruin probability
Truncated data

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Computational Mathematics

Access to Document

10.1007/s00180-006-0011-2

Cite this

@article{5b5fcfff8a8547389e83cf38a92fd60d,

title = "Modelling catastrophe claims with left-truncated severity distributions",

abstract = "In this paper, we present a procedure for consistent estimation of the severity and frequency distributions based on incomplete insurance data and demonstrate that ignoring the thresholds leads to a serious underestimation of the ruin probabilities. The event frequency is modelled with a non- homogeneous Poisson process with a sinusoidal intensity rate function. The choice of an adequate loss distribution is conducted via the in-sample goodness-of-fit procedures and forecasting, using classical and robust methodologies.",

keywords = "Loss distribution, Natural catastrophe, Property insurance, Ruin probability, Truncated data",

author = "Anna Chernobai and Krzysztof Burnecki and Svetlozar Rachev and Stefan Tr{\"u}ck and Rafa{\l} Weron",

year = "2006",

month = dec,

doi = "10.1007/s00180-006-0011-2",

language = "English (US)",

volume = "21",

pages = "537--555",

journal = "Computational Statistics",

issn = "0943-4062",

publisher = "Springer Verlag",

number = "3-4",

}

TY - JOUR

T1 - Modelling catastrophe claims with left-truncated severity distributions

AU - Chernobai, Anna

AU - Burnecki, Krzysztof

AU - Rachev, Svetlozar

AU - Trück, Stefan

AU - Weron, Rafał

PY - 2006/12

Y1 - 2006/12

N2 - In this paper, we present a procedure for consistent estimation of the severity and frequency distributions based on incomplete insurance data and demonstrate that ignoring the thresholds leads to a serious underestimation of the ruin probabilities. The event frequency is modelled with a non- homogeneous Poisson process with a sinusoidal intensity rate function. The choice of an adequate loss distribution is conducted via the in-sample goodness-of-fit procedures and forecasting, using classical and robust methodologies.

AB - In this paper, we present a procedure for consistent estimation of the severity and frequency distributions based on incomplete insurance data and demonstrate that ignoring the thresholds leads to a serious underestimation of the ruin probabilities. The event frequency is modelled with a non- homogeneous Poisson process with a sinusoidal intensity rate function. The choice of an adequate loss distribution is conducted via the in-sample goodness-of-fit procedures and forecasting, using classical and robust methodologies.

KW - Loss distribution

KW - Natural catastrophe

KW - Property insurance

KW - Ruin probability

KW - Truncated data

UR - http://www.scopus.com/inward/record.url?scp=33845314441&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33845314441&partnerID=8YFLogxK

U2 - 10.1007/s00180-006-0011-2

DO - 10.1007/s00180-006-0011-2

M3 - Article

AN - SCOPUS:33845314441

SN - 0943-4062

VL - 21

SP - 537

EP - 555

JO - Computational Statistics

JF - Computational Statistics

IS - 3-4

ER -

Modelling catastrophe claims with left-truncated severity distributions

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this