ΥΠΕΝΘΥΜΙΣΗ
Wed 9 Apr 2014 - 10:22
ΥΠΕΝΘΥΜΙΣΗ - Η ΟΜΙΛΙΑ ΕΙΝΑΙ ΑΥΡΙΟ ΣΤΙΣ 12 ΣΤΟ ΚΤΙΡΙΟ ΤΩΝ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΤΟΥ ΟΠΑ
AUEB SEMINARS: Modelling the time delay in Critical Illness Insurance using GB2 family of distributions
Fri 21 Mar 2014 - 17:21
ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ
ΤΜΗΜΑ ΣΤΑΤΙΣΤΙΚΗΣ
ΠΕΜΠΤΗ 10/4/2014
12:00 – 13:00
ΑΙΘΟΥΣΑ 607, 6ος ΟΡΟΦΟΣ,
ΚΤΙΡΙΟ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ
(ΕΥΕΛΠΙΔΩΝ & ΛΕΥΚΑΔΟΣ)
Erengul (Ozkok) Dodd
Faculty of Mathematical Sciences
University of Southampton
Modelling the time delay in Critical Illness Insurance using GB2 family of distributions
ΠΕΡΙΛΗΨΗ (ΣΤΑ ΑΓΓΛΙΚΑ)
Critical Illness Insurance (CII) is subject to long delays between the diagnosis of a claim and the corresponding settlement. Consideration of the uncertainty associated with different models used in the estimation of the delay between diagnosis and settlement is crucial for financial purposes, especially when the date of diagnosis is missing from the data records. Using UK data supplied by the Continuous Mortality Investigation, we account for uncertainty in CII modelling by exploring various claim delay distributions such as generalised beta of the second kind (GB2) family of distributions in a generalised linear-type model setting. The non-recorded dates of diagnosis are included in the analysis as missing values using their posterior predictive distribution. The models are fitted under a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Variable selection using Bayesian methodology to obtain the best model with different prior distribution setups for the parameters is also discussed for a wide selection of error distributions.
ΤΜΗΜΑ ΣΤΑΤΙΣΤΙΚΗΣ
ΠΕΜΠΤΗ 10/4/2014
12:00 – 13:00
ΑΙΘΟΥΣΑ 607, 6ος ΟΡΟΦΟΣ,
ΚΤΙΡΙΟ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ
(ΕΥΕΛΠΙΔΩΝ & ΛΕΥΚΑΔΟΣ)
Erengul (Ozkok) Dodd
Faculty of Mathematical Sciences
University of Southampton
Modelling the time delay in Critical Illness Insurance using GB2 family of distributions
ΠΕΡΙΛΗΨΗ (ΣΤΑ ΑΓΓΛΙΚΑ)
Critical Illness Insurance (CII) is subject to long delays between the diagnosis of a claim and the corresponding settlement. Consideration of the uncertainty associated with different models used in the estimation of the delay between diagnosis and settlement is crucial for financial purposes, especially when the date of diagnosis is missing from the data records. Using UK data supplied by the Continuous Mortality Investigation, we account for uncertainty in CII modelling by exploring various claim delay distributions such as generalised beta of the second kind (GB2) family of distributions in a generalised linear-type model setting. The non-recorded dates of diagnosis are included in the analysis as missing values using their posterior predictive distribution. The models are fitted under a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Variable selection using Bayesian methodology to obtain the best model with different prior distribution setups for the parameters is also discussed for a wide selection of error distributions.
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