Robust SURE estimates of profitability in the Egyptian insurance market

2021 ◽  
pp. 1-13
Author(s):  
Ahmed H. Youssef ◽  
Amr R. Kamel ◽  
Mohamed R. Abonazel
Keyword(s):  
Robust Regression ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Insurance Market ◽  
Insurance Companies ◽  
Regression Equations ◽  
Robust Estimators ◽  
Exogenous Variables ◽  
Net Profit ◽  
Mm Estimation

This paper proposed three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.

Intellectual capital and profitability in an emerging insurance market

2017 ◽  
Vol 33 (1) ◽  
pp. 2-19 ◽  
Author(s):  
Nicholas Asare ◽  
Abdul Latif Alhassan ◽  
Michael Effah Asamoah ◽  
Matthew Ntow-Gyamfi
Keyword(s):  
Intellectual Capital ◽  
Value Added ◽  
Ordinary Least Squares ◽  
Insurance Market ◽  
Insurance Companies ◽  
Content Type ◽  
Life Insurers ◽  
Main Driver ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this paper is to examine the relationship between intellectual capital (IC) and profitability of insurance companies in Ghana. Design/methodology/approach Data on 36 life and non-life insurance companies from 2007 to 2011 are employed to estimate the value added intellectual coefficient of Pulic (2004, 2008). Using return on assets and underwriting profit as indicators of profitability, the ordinary least squares panel corrected standard errors of Beck and Katz (2005) is used in estimating the relationship in the presence of serial correlation and heteroskedasticity. Leverage, underwriting risk and insurers’ size are used as control variables. Findings Non-life insurers have high IC performance comparative to life insurers. This study finds a significant positive relationship between IC and profitability of insurers in Ghana while human capital efficiency is the main driver of insurers’ IC performance. Practical implications The study discusses relevance of IC for management of insurance companies in Ghana and other emerging insurance markets in Africa. Originality/value This appears to be the first study to examine the impact of IC on profitability of a developing insurance market in Africa.

scholarly journals COMPARING THE PREDICTIVE PERFORMANCE OF OLS AND 7 ROBUST LINEAR REGRESSION ESTIMATORS ON A REAL AND SIMULATED DATASETS

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Sacha Varin
Keyword(s):  
Linear Regression ◽  
High Efficiency ◽  
Robust Regression ◽  
Mean Squared Error ◽  
Predictive Performance ◽  
Ordinary Least Squares ◽  
Small Samples ◽  
Robust Estimators ◽  
Absolute Deviation ◽  
M Estimator

Robust regression techniques are relevant tools for investigating data contaminated with influential observations. The article briefly reviews and describes 7 robust estimators for linear regression, including popular ones (Huber M, Tukey’s bisquare M, least absolute deviation also called L1 or median regression), some that combine high breakdown and high efficiency [fast MM (Modified M-estimator), fast ?-estimator and HBR (High breakdown rank-based)], and one to handle small samples (Distance-constrained maximum likelihood (DCML)). We include the fast MM and fast ?-estimators because we use the fast-robust bootstrap (FRB) for MM and ?-estimators. Our objective is to compare the predictive performance on a real data application using OLS (Ordinary least squares) and to propose alternatives by using 7 different robust estimations. We also run simulations under various combinations of 4 factors: sample sizes, percentage of outliers, percentage of leverage and number of covariates. The predictive performance is evaluated by crossvalidation and minimizing the mean squared error (MSE). We use the R language for data analysis. In the real dataset OLS provides the best prediction. DCML and popular robust estimators give good predictive results as well, especially the Huber M-estimator. In simulations involving 3 predictors and n=50, the results clearly favor fast MM, fast ?-estimator and HBR whatever the proportion of outliers. DCML and Tukey M are also good estimators when n=50, especially when the percentage of outliers is small (5% and 10%%). With 10 predictors, however, HBR, fast MM, fast ? and especially DCML give better results for n=50. HBR, fast MM and DCML provide better results for n=500. For n=5000 all the robust estimators give the same results independently of the percentage of outliers. If we vary the percentages of outliers and leverage points simultaneously, DCML, fast MM and HBR are good estimators for n=50 and p=3. For n=500, fast MM, fast ? and HBR provi

scholarly journals The Innovative Impact of Insurance for Economic Growth: The Evidence from New EU Member States

ECONOMICS ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 109-122
Author(s):  
Hristo Kondovski
Keyword(s):  
Economic Growth ◽  
Insurance Industry ◽  
Ordinary Least Squares ◽  
Insurance Market ◽  
Insurance Companies ◽  
Annual Data ◽  
Member States ◽  
Market Activity ◽  
Eu Member States ◽  
New Eu Member States

Abstract Insurance market activity, both as a financial intermediary and a provider of risk transfer and indemnification, may contribute to economic growth by allowing different risks to be managed more efficiently and by mobilizing domestic savings. During the last decade, there has been faster growth in insurance market activity, particularly in emerging markets given the process of liberalization and financial integration, which raises questions about its impact on economic growth.The aim of this paper is to examine relationship between insurance sector development and economic in 11 new EU member states from CentralandEasternEurope, using annual data fortheperiod 1999-2018. We apply dynamic and fully modified ordinary least squares to estimate the relationship between the variables. The results of our study indicate there are a positive and a significant relationship between insurance, measured through penetration, and economic growth Thus, functions of insurance companies - providing means of risk management and performing mobilization and allocation of resources - are important for economic growth and is in line with previous studies and with our hypothesis These results could be useful for regional governments that seek to improve economic growth as they suggest the need for implementation of stimulative policies for the development of insurance industry

scholarly journals Graphical Evaluation of the Ridge-Type Robust Regression Estimators in Mixture Experiments

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Ali Erkoc ◽  
Esra Emiroglu ◽  
Kadri Ulas Akay
Keyword(s):  
Robust Regression ◽  
Mean Squared Error ◽  
Ordinary Least Squares ◽  
Robust Estimators ◽  
Estimation Of Parameters ◽  
Mixture Experiments ◽  
Data Set ◽  
Regression Techniques ◽  
Alternative Approaches ◽  
Selection Of

In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set.

Studying the factors of regional insurance market development using the correlation and regression analysis

2020 ◽  
Vol 26 (7) ◽  
pp. 1610-1630
Author(s):  
E.L. Prokop'eva
Keyword(s):  
Regression Analysis ◽  
Federal District ◽  
Insurance Market ◽  
Market Development ◽  
Insurance Markets ◽  
Regression Equations ◽  
Factors Affecting ◽  
Correlation And Regression Analysis ◽  
Country Level ◽  
Inverse Effect

Subject. The article investigates and quantifies factors of insurance markets functioning in Russian regions, and reveals possibilities to manage them. Objectives. The purpose of the study is to substantiate regional factors that determine the specifics of regional insurance market development; to quantify them to increase the efficiency of regional insurance. Methods. The study draws on statistical methods, functional analysis, algorithm development, correlation and regression analysis. Results. I calculated coefficients of pair and multiple correlation with the indicators of insurance markets in the context of the subjects of the Russian Federation, and composed regression equations. Based on the analysis, I determined the algorithm for inverse effect of the insurance market on the economic, social, fiscal and environmental performance of the region, offered appropriate measures aimed at developing the economic potential of the region and its social sphere. Conclusions. The paper considers the case of the Republic of Khakassia, one of depressed subjects in the Siberian Federal District. The developed models can be used for other regions of Russia, given the geographical and economic features of development. The findings may help generate regional strategies for socio-economic development at the country level. The scientific contribution and the novelty of the work consist of systematizing and quantifying the factors affecting the insurance mechanisms of regional markets, and assessing the inverse effect of insurance mechanisms on integrated development of the region.

Digitalization of Competition in the Insurance Market

2018 ◽  
Vol 11 (2) ◽  
pp. 121-128
Author(s):  
D. V. Bryzgalov
Keyword(s):  
Digital Technologies ◽  
Insurance Market ◽  
Insurance Companies ◽  
Sales Channels ◽  
Channel Competition ◽  
Research Findings ◽  
The Subject

The subject of the research is the influence of the insurance market digitalization on competition forms in insurance. The purpose of the research was to study the forms of competition and factors of competitiveness in the process of digitalization of insurance activities. The research findings revealed the specifics of competition between insurance companies in digital sales channels of insurance services, and identified groups of new factors in the competitiveness of insurance programs. The paper describes two models of the policyholder behavior typical for traditional and digital sales channels in the insurance market — classical and digital. It is concluded that the digitalization of the insurance market influences the competition between insurance companies making a shift towards the channel competition and contributing to the emergence of new competition factors for insurance programs developed with digital technologies.

The Usefulness of Readability Formulas in the Insurance Industry

2015 ◽  
Vol 10 (4) ◽  
pp. 339-351
Author(s):  
Katarzyna Barczuk
Keyword(s):  
Final Section ◽  
Insurance Industry ◽  
Insurance Market ◽  
Insurance Companies ◽  
Text Readability ◽  
The Given ◽  
Readability Formulas

The aim of this paper is to characterize the most important methods which are used to determine the level of text readability. The author presents practical examples of the usage of chosen methods by foreign insurance companies. The final section of the study is completed with general conclusions relating to the application of the given solutions to the Polish insurance market. 

Indian Life Insurance Market and Corporate Performances: A Study of Selected Firms

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Joy Chakraborty ◽  
Partha Pratim Sengupta
Keyword(s):  
Market Share ◽  
Life Insurance ◽  
Market Concentration ◽  
Insurance Market ◽  
Insurance Companies ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Insurance Sector ◽  
Life Insurers ◽  
Company Limited

In the pre-reform era, Life Insurance Corporation of India (LICI) dominated the Indian life insurance market with a market share close to 100 percent. But the situation drastically changed since the enactment of the IRDA Act in 1999. At the end of the FY 2012-13, the market share of LICI stood at around 73 percent with the number of players having risen to 24 in the countrys life insurance sector. One of the reasons for such a decline in the market share of LICI during the post-reform period could be attributed to the increasing competition prevailing in the countrys life insurance sector. At the same time, the liberalization of the life insurance sector for private participation has eventually raised issues about ensuring sound financial performance and solvency of the life insurance companies besides protection of the interest of policyholders. The present study is an attempt to evaluate and compare the financial performances, solvency, and the market concentration of the four leading life insurers in India namely the Life Insurance Corporation of India (LICI), ICICI Prudential Life Insurance Company Limited (ICICI PruLife), HDFC Standard Life Insurance Company Limited (HDFC Standard), and SBI Life Insurance Company Limited (SBI Life), over a span of five successive FYs 2008-09 to 2012-13. In this regard, the CARAMELS model has been used to evaluate the performances of the selected life insurers, based on the Financial Soundness Indicators (FSIs) as published by IMF. In addition to this, the Solvency and the Market Concentration Analyses were also presented for the selected life insurers for the given period. The present study revealed the preexisting dominance of LICI even after 15 years since the privatization of the countrys life insurance sector.

scholarly journals Mechanisms of Protection from Interest Rate Risk with Reference to the Life Insurance Market in Montenegro

2018 ◽  
Vol 7 (1) ◽  
pp. 17-42
Author(s):  
Milijana Novović Burić ◽  
Vladimir Kašćelan ◽  
Milivoje Radović ◽  
Ana Lalević Filipović
Keyword(s):  
Risk Management ◽  
Interest Rate ◽  
Life Insurance ◽  
Financial Management ◽  
Financial Stability ◽  
Interest Rate Risk ◽  
Economic Security ◽  
Insurance Market ◽  
Insurance Companies ◽  
Rate Risk

Abstract Insurance companies are facing major challenges that point to the need for control process and risk management. Risk management in insurance has a direct impact on solvency, economic security, and overall financial stability of insurance companies. It is very important for insurance companies to adequately calculate risks to which they are exposed. Asset liability management (ALM), as an integrated approach to financial management, requires simultaneous decision-making about categories and values of assets and liabilities in order to establish the optimum volume and the ratio of assets and liabilities, with the understanding of complexity of the financial market in which financial institutions operate. ALM focuses on a significant number of risks, whereby the emphasis in this paper will be on interest rate risk which indicates potential losses that may reflect in a lower interest margin, a lower value of assets or both, in terms of changes in interest rates. In the above context, the aim of this paper is to show how to protect from interest rate changes and how these changes influence the insurance market in Montenegro, both from the theoretical and the practical point of view. The authors consider this to be an interesting and very important topic, especially because the life insurance market in Montenegro is underdeveloped and subject to fluctuations. Also, taking into account the fact that Montenegro is a country that has been making serious efforts to join the EU, it is expected that insurance companies in Montenegro will strengthen their financial position in the market even using the ALM traditional techniques, which is shown in this paper.

scholarly journals PEMODELAN REGRESI RIDGE ROBUST-MM DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi AKB di Jawa Tengah Tahun 2017)

2019 ◽  
Vol 8 (1) ◽  
pp. 24-34
Author(s):  
Eka Destiyani ◽  
Rita Rahmawati ◽  
Suparti Suparti
Keyword(s):  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Method ◽  
Predictor Variables ◽  
Healthy Life ◽  
Significance Level ◽  
Central Java ◽  
Regression Parameters

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linear regression parameters. If multicollinearity is exist within predictor variables especially coupled with the outliers, then regression analysis with OLS is no longer used. One method that can be used to solve a multicollinearity and outliers problems is Ridge Robust-MM Regression. Ridge Robust-MM  Regression is a modification of the Ridge Regression method based on the MM-estimator of Robust Regression. The case study in this research is AKB in Central Java 2017 influenced by population dencity, the precentage of households behaving in a clean and healthy life, the number of low-weighted baby born, the number of babies who are given exclusive breastfeeding, the number of babies that receiving a neonatal visit once, and the number of babies who get health services. The result of estimation using OLS show that there is violation of multicollinearity and also the presence of outliers. Applied ridge robust-MM regression to case study proves ridge robust regression can improve parameter estimation. Based on t test at 5% significance level most of predictor variables have significant effect to variable AKB. The influence value of predictor variables to AKB is 47.68% and MSE value is 0.01538.Keywords:  Ordinary  Least  Squares  (OLS),  Multicollinearity,  Outliers,  RidgeRegression, Robust Regression, AKB.

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