Signs of dependence and heavy tails in non-life insurance data

Jonas Alm

RECORD

Signs of dependence and heavy tails in non-life insurance data

Jonas Alm

Risk Management · Submitted 5 Jan 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We find that dependence between LoBs is weaker in our data than what is assumed in the Solvency II standard formula.

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What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.30 · human participants 0.03 · controlled comparison 0.33 · randomized assignment 0.01 · states quantitative result 0.07 · compares with prior results 0.90 · mentions code or data availability 0.02.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

In this paper we study data from the yearly reports the four major Swedish non-life insurers have sent to the Swedish Financial Supervisory Authority (FSA). We aim at finding marginal distributions of, and dependence between, losses on the five largest lines of business (LoBs) in order to create models for Solvency Capital Requirement (SCR) calculation. We try to use data in an optimal way by sensibly defining an accounting year loss in terms of actuarial liability predictions, and by pooling observations from several companies when possible to decrease the uncertainty about the underlying distributions and their parameters. We find that dependence between LoBs is weaker in our data than what is assumed in the Solvency II standard formula. We also find dependence between companies that may affect financial stability, and must be taken into account when estimating loss distribution parameters. Moreover, we discuss under what circumstances an insurer is better (or worse) off using an internal model for SCR calculation instead of the standard formula.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	In this paper we study data from the yearly reports the four major Swedish non-life insurers have sent to the Swedish Financial Supervisory Authority (FSA).	method 0.73	0.72	0.02	asserted 0.92
s1	We aim at finding marginal distributions of, and dependence between, losses on the five largest lines of business (LoBs) in order to create models for Solvency Capital Requirement (SCR) calculation.	contribution 0.89	0.21	0.04	asserted 0.61
s2	We try to use data in an optimal way by sensibly defining an accounting year loss in terms of actuarial liability predictions, and by pooling observations from several companies when possible to decrease the uncertainty about the underlying distributions and their parameters.	method 0.99	0.79	0.03	asserted 0.53
s3	We find that dependence between LoBs is weaker in our data than what is assumed in the Solvency II standard formula.	result 1.00	0.96	0.35	asserted 1.00
s4	We also find dependence between companies that may affect financial stability, and must be taken into account when estimating loss distribution parameters.	result 0.93	0.94	0.17	hedged possibility 0.88
s5	Moreover, we discuss under what circumstances an insurer is better (or worse) off using an internal model for SCR calculation instead of the standard formula.	interpretation 0.50	0.55	0.10	unclear 0.64

What kind of paper the classifier reports this is: empirical study 1.00 · evidence from analysis of existing data 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

In this paper we study data from the yearly reports the four major Swedish non-life insurers have sent to the Swedish Financial Supervisory Authority (FSA). We aim at finding marginal distributions of, and dependence between, losses on the five largest lines of business (LoBs) in order to create models for Solvency Capital Requirement (SCR) calculation. We try to use data in an optimal way by sensibly defining an accounting year loss in terms of actuarial liability predictions, and by pooling observations from several companies when possible to decrease the uncertainty about the underlying distributions and their parameters. We find that dependence between LoBs is weaker in our data than what is assumed in the Solvency II standard formula. We also find dependence between companies that may affect financial stability, and must be taken into account when estimating loss distribution parameters. Moreover, we discuss under what circumstances an insurer is better (or worse) off using an internal model for SCR calculation instead of the standard formula.

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