scholarly journals A Mixed Model for Loss Ratio Analysis

1991 ◽  
Vol 21 (2) ◽  
pp. 231-238 ◽  
Author(s):  
M. Y. El-Bassiouni
Keyword(s):  
Mixed Model ◽  
Structural Parameters ◽  
Maximum Likelihood Estimates ◽  
Insurance Companies ◽  
Ratio Analysis ◽  
Loss Ratio ◽  
Time Effects ◽  
General Insurance ◽  
Proposed Model ◽  
Ratio Data

AbstractThe model introduced may be treated as a mixed two-way analysis of variance with fixed company effects and random time effects. Further, the risk volumes are integrated into the model in such a way that the unexplained variance is inversely proportional to the risk volume of each company. The proposed model is used to analyze loss ratio data from the general insurance market in Kuwait. The maximum likelihood estimates of the structural parameters are obtained. These estimates are then used to compute the loss ratios and solvency margins for the four domestic insurance companies.

scholarly journals A mixed model for loss ratio analysis

1993 ◽  
Vol 12 (1) ◽  
pp. 69
Author(s):  
M.Y. El-Bassiouni
Keyword(s):  
Mixed Model ◽  
Ratio Analysis ◽  
Loss Ratio

scholarly journals EXAMINING THE EFFECTS OF FINANCIAL FACTORS ON PROFITABILITY OF GENERAL INSURANCE COMPANIES IN KENYA

2020 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Joseph Nthuli Ngunguni ◽  
Dr Sedina Misango ◽  
Dr. Martin Onsiro
Keyword(s):  
Regression Coefficient ◽  
Secondary Data ◽  
Annual Reports ◽  
Insurance Companies ◽  
P Value ◽  
Unique Contribution ◽  
Loss Ratio ◽  
Leverage Ratio ◽  
Financial Factors ◽  
General Insurance

Purpose: The objective of this study was to examine the financial factors which affect the profitability of general insurance companies in Kenya. The profitability in this study is represented by ROA, as dependent variable for the period 2013 to 2017. Independent variables in this study were liquidity, leverage, loss ratio and expenses ratio. Methodology: The type of research design used in this study was both descriptive as well as referential analysis. The study applied a census procedure to study all the 28 general insurance companies and targeted the entire population of 28 companies. Secondary data was collected from individual annual published financial statements of 28 general insurance companies for 5 years; 2013 to 2017, AKI reports and IRA published annual reports. A collection data sheet was used to collect the relevant data from all the 28 general insurance companies. After the data was collected and sorted, it was analyzed using referential analysis (multiple regression analysis). This was assisted by SPSS (Version 20) software. Findings: The study revealed that the regression coefficient of loss ratio was -0.068, t-statistics -0.415 and p-value of 0.682 while that of leverage ratio was -0.048, t-statistics -0.546 and p-value of 0.590. Liquidity ratio had a regression coefficient of 4.238, t-statistics 3.257 and p-value of 0.003 while expenses ratio had a regression coefficient of -0.281, t-statistics -3.840 and p-value of 0.001. Unique contribution to theory, practice and policy: The study recommends that the management of general insurance companies in Kenya need to address liquidity and expenses by minimizing expenses and maximizing liquidity in order to be on the safe side as far as profitability is concerned. The study also recommends that regulators and other stakeholders, within the industry, should at regular interval intensify efforts to ascertain the claims handling procedures currently in use by insurance companies in Kenya.

On the issue of assessing the adequacy of the reserves of insurance companies with various methods for determining the loss of business

2020 ◽  
pp. 46-53
Author(s):  
Margarita Naslednikova ◽  
Alexandr Zamalov
Keyword(s):  
Comparative Analysis ◽  
Health System ◽  
Financial Reporting ◽  
Accounting Standards ◽  
Insurance Companies ◽  
Insurance Company ◽  
Reporting Standards ◽  
Loss Ratio ◽  
Qualitative Characteristic ◽  
Financial Reporting Standards

The article discusses methods for calculating the loss ratio of insurance companies, including compulsory medical insurance, which is the basis for building a health system; su’ciency of formed reserves, which are created in connection with the possibility of losses. Variants of interpretation of calculated indicators into a qualitative characteristic of the insurance company. A comparative analysis of the calculation of indicators of loss-making of insurance companies and the adequacy of the formation of reserves of insurance companies according to Russian accounting standards and in accordance with the requirements of international financial reporting standards.

Managing uncertainty in a general insurance company

1990 ◽  
Vol 117 (2) ◽  
pp. 173-277 ◽  
Author(s):  
C. D. Daykin ◽  
G. B. Hey
Keyword(s):  
Computer Model ◽  
Flow Model ◽  
Insurance Companies ◽  
Simulation Methods ◽  
Insurance Company ◽  
Practical Applications ◽  
General Insurance ◽  
Alternative Approaches ◽  
The Impact

AbstractA cash flow model is proposed as a way of analysing uncertainty in the future development of a general insurance company. The company is modelled alongside the market in aggregate so that the impact of changes in premium rates relative to the market can be assessed. An extensive computer model is developed along these lines, intended for use in practical applications by actuaries advising the management of genera1 insurance companies. Simulation methods are used to explore the consequences of uncertainty, particularly in regard to inflation and investments. Some comments are made on the role of actuaries in general insurance. Alternative approaches to describing the behaviour of an insurance firm in the market are considered.

A HYBRID GENETIC ALGORITHM FOR THE MAXIMUM LIKELIHOOD ESTIMATION OF MODELS WITH MULTIPLE EQUILIBRIA: A FIRST REPORT

2005 ◽  
Vol 01 (02) ◽  
pp. 295-303 ◽  
Author(s):  
VICTOR AGUIRREGABIRIA ◽  
PEDRO MIRA
Keyword(s):  
Genetic Algorithm ◽  
Maximum Likelihood ◽  
Multiple Equilibria ◽  
Structural Parameters ◽  
Hybrid Genetic Algorithm ◽  
Likelihood Estimation ◽  
Maximum Likelihood Estimates ◽  
Large Space ◽  
Pseudo Maximum Likelihood ◽  
Structural Econometric

This paper presents a hybrid genetic algorithm to obtain maximum likelihood estimates of parameters in structural econometric models with multiple equilibria. The algorithm combines a pseudo maximum likelihood (PML) procedure with a genetic algorithm (GA). The GA searches globally over the large space of possible combinations of multiple equilibria in the data. The PML procedure avoids the computation of all the equilibria associated with every trial value of the structural parameters.

Efficiency Analysis of Malaysian General Insurance Companies Using Data Envelopment and Super-efficiency Approach

2021 ◽  
Vol 11 (10) ◽  
Author(s):  
Jamilah Mohd Mahyideen ◽  
Nur Azlina Abd Aziz ◽  
Hafisah Yaakob ◽  
Nurhanani Aflizan Mohamad Rusli ◽  
Wan Normila Mohamad
Keyword(s):  
Efficiency Analysis ◽  
Insurance Companies ◽  
Data Envelopment ◽  
Super Efficiency ◽  
General Insurance ◽  
Using Data
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