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

^*Corresponding author for this work

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

16 Citations (Scopus)

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
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
Publication status	Published - Dec 2006
Externally published	Yes

Keywords

Loss distribution
Natural catastrophe
Property insurance
Ruin probability
Truncated data

Access to Document

10.1007/s00180-006-0011-2

Cite this

@article{4e1a43ecf3ad426cbf73f2c09ad3521d,

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",

volume = "21",

pages = "537--555",

journal = "Computational Statistics",

issn = "0943-4062",

publisher = "Springer, Springer Nature",

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

U2 - 10.1007/s00180-006-0011-2

DO - 10.1007/s00180-006-0011-2

M3 - Article

AN - SCOPUS:33845314441

VL - 21

SP - 537

EP - 555

JO - Computational Statistics

JF - Computational Statistics

SN - 0943-4062

IS - 3-4

ER -

Modelling catastrophe claims with left-truncated severity distributions

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this