Calculation of Solvency Capital Requirements for Non-life Underwriting Risk Using Generalized Linear Models

Valecký, Jiří

doi:10.18267/j.pep.621

Cited by 3 publications

(3 citation statements)

References 29 publications

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“…Among previous research on insurance risk variables (underwriting risk) like as conducted by (Chen et al, 2020;Valecký, 2017), (Ali et al, 2022), (Mulyani et al, 2020), (Sivaram et al, ., 2020), (Agussalim, MP, Rezkiana, A., & Ali, 2016), (Masruhin et al, 2021), (Richardo et al, 2020), (Zahran & Ali, 2020), (Fardinal et al, 2022).…”

Section: Ra = ∑ ((Carb I -Ar I )Fcb I )mentioning

confidence: 99%

Factors Influencing Credit Risk and Insurance Risk in General Insurance of Indonesia : Technical Reserve Ratio and Reinsurance Ratio (Financial Management Literature Review)

Wong

2022

DIJMS

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This article is a Literature Review to see whether there is an effect of the Technical Reserve ratio and the Reinsurance ratio on credit risk and insurance risk in General Insurance in Indonesia. The purpose of this scientific article is to build a causality research hypothesis between variables so that it can be used for further research. The topic of discussion is the area of financial management science. The method of writing this Literature Review article is Library Research with sources of books, journals and research articles contained in online media such as Google Scholar, Mendeley, Research gate and other academic media. From this literature review, it can be concluded that 1) the Technical Reserve ratio has an effect on credit risk; 2) Reinsurance ratio has an effect on credit risk; 3) Technical Reserve ratio has an effect on insurance risk; 4) Reinsurance ratio has an effect on insurance risk; and 5) insurance risk affects credit risk. Apart from these 2 exogenous variables that affect the 2 endogenous variables of credit risk and insurance risk, there are many other factors including the loss ratio, current ratio and total assets variables.

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Section: Ra = ∑ ((Carb I -Ar I )Fcb I )mentioning

confidence: 99%

Factors Influencing Credit Risk and Insurance Risk in General Insurance of Indonesia : Technical Reserve Ratio and Reinsurance Ratio (Financial Management Literature Review)

Wong

2022

DIJMS

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“…Hanewald et al [24] based on the research of macroeconomic fluctuation and demography 2 Complexity developed a dynamic asset liability model to evaluate the impact of macroeconomic fluctuation on the solvency of life insurance companies. Valecký [25] uses the GLM model to calculate solvency capital requirement of non-life insurance underwriting risk by using individual rating factors.…”

Section: Solvency Estimatementioning

confidence: 99%

Solvency Evaluation Model of Insurance Company Based on Stochastic Differential Equation

Wang

Zhu

2021

Complexity

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Solvency assessment is the core content of insurance supervision. In this paper, from the perspective of capital flow, the insurance company’s capital flow is regarded as a dynamic system, the stochastic differential equations model is established to describe its flow characteristics, and the existence of positive equilibrium point of the system is proved, as well as the conditions of stability at equilibrium point, that is, the requirements of the insurance company’s solvency. Furthermore, by using the numerical simulation method, we get the strategy of insurance companies to deal with the solvency shortage when facing the change of external environment, and the strategy of insurance company to deal with solvency shortage is obtained.

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“…More applications of GLMs occurred mostly after the 1990, when the insurance market was being deregulated in many countries and the GLMs were used to undertake a tariff analysis, for example (Andrade-Silva, 1989), (Brockman and Wright, 1992) or (Renshaw, 1994). GLMs are also used for premium optimization, for example (Zaks et al, 2006) or (Branda, 2014), and for the estimation of solvency capital requirement that has appeared recently in (Valecký, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Note on Mismodelling of Policyholder’s Age in Claim Frequency Model: A Matter of Gender in Vehicle Insurance

Valecký¹

2020

IJoES

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Using the motor hull insurance data of Czech insurer, the paper deals with how mismodelling of policyholder's age can induce misleading conclusions about the gender differences in claim frequency within vehicle insurance. This study is based on individual data with unit policy duration and puts the emphasis on correct modelling of functional form of the age to show that mismodelling as well as categorization yields misleading conclusions and, finally, we demonstrate how the inferences depend on categorization itself. Thus, we showed that linear form as well as the categorization increases the type I error to detect the obvious interaction between gender and age. By involving fractional polynomials, the results partially support the judgement of European Court of Justice to ban using gender as a rating factor, in particular for young policyholders. We concluded that, if another relevant data are not available, the gender as well as interaction with the age should be considered in the claim frequency model although such model cannot be used for setting premium.

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Calculation of Solvency Capital Requirements for Non-life Underwriting Risk Using Generalized Linear Models

Cited by 3 publications

References 29 publications

Factors Influencing Credit Risk and Insurance Risk in General Insurance of Indonesia : Technical Reserve Ratio and Reinsurance Ratio (Financial Management Literature Review)

Factors Influencing Credit Risk and Insurance Risk in General Insurance of Indonesia : Technical Reserve Ratio and Reinsurance Ratio (Financial Management Literature Review)

Solvency Evaluation Model of Insurance Company Based on Stochastic Differential Equation

Note on Mismodelling of Policyholder’s Age in Claim Frequency Model: A Matter of Gender in Vehicle Insurance

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