scholarly journals Contributions from Spatial Models to Non-Life Insurance Pricing: An Empirical Application to Water Damage Risk

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2476
Author(s):  
Maria Victoria Rivas-Lopez ◽  
Roman Minguez-Salido ◽  
Mariano Matilla Matilla Garcia ◽  
Alejandro Echeverria Echeverria Rey
Keyword(s):  
Life Insurance ◽  
Spatial Information ◽  
Spatial Models ◽  
Spatial Error ◽  
Insurance Pricing ◽  
Insurance Portfolio ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
Insurance Data ◽  
Damage Risk

This paper explores the application of spatial models to non-life insurance data focused on the multi-risk home insurance branch. In the pricing modelling and rating process, spatial information should be considered by actuaries and insurance managers because frequencies and claim sizes may vary by region and the premium should be different considering this rating variable. In addition, it is relevant to examine the spatial dependence due to the fact that the frequency of claims in neighbouring regions is often expected to be more closely related than those in regions far from each other. In this paper, a comparison between spatial models, such as spatial autoregressive models (SAR), the spatial error model (SEM), and the spatial Durbin model (SDM), and a non-spatial model has been developed. The data used for this analysis are for a home insurance portfolio located in Spain, from which we have selected peril of water coverage.

scholarly journals SPATIAL CONVERGENCE OF THE GROSS VALUE OF PRODUCTION OF FIREWOOD IN THE MESOREGIONS OF THE BRAZILIAN NORTHEAST

2018 ◽  
Vol 42 (2) ◽  
Author(s):  
Luiz Moreira Coelho Junior ◽  
Kalyne de Lourdes da Costa Martins ◽  
Magno Vamberto Batista da Silva
Keyword(s):  
Spatial Dependence ◽  
Classical Model ◽  
Spatial Models ◽  
Information Criterion ◽  
Spatial Error ◽  
Spatial Convergence ◽  
Spatial Autoregressive ◽  
Classical Linear Regression ◽  
Object Of Study ◽  
Brazilian Northeast

ABSTRACT This paper analyzed the process of convergence in the gross value of wood production in mesoregions of Northeast Brazil, in the period of 1994 and 2013. The object of study was the Gross Value of Production (GVP) of firewood per km2 of the mesoregions of the Northeast of Brazil. In the methodology the Absolute Convergence Model was applied and estimated through the classical model and spatial models. In the spatial approach we used the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM). From the results obtained, the following conclusions were reached: The mesoregions of the Northeast of Brazil had an average fall of 3.94% a.a. of the GVP/km2 of native wood for the period 1994 to 2013. Considering the classical linear regression model, convergence was verified and also the presence of spatial dependence for GVP/km2 of firewood. In order to correct the spatial dependence, the SAR and SEM Models were adequate and according to Akaike's Information Criterion and used the rook matrix the SEM was configured the best model. This study showed the importance of the involvement of the spatial question in the models, either by the overlap of information of the GVP and in the development of public policies that positively affect the neighborhood.

scholarly journals Challenges and Ways to Develop Insurance Industry in KSA Market

2016 ◽  
Vol 5 (2) ◽  
pp. 152 ◽  
Author(s):  
Maher Taib Toukabri ◽  
Hafedh Hedi Ibrahim
Keyword(s):  
Life Insurance ◽  
Emotional Regulation ◽  
Insurance Industry ◽  
Opinion Leadership ◽  
Theoretical Interest ◽  
Measurement Scales ◽  
Insurance Portfolio ◽  
Two Samples ◽  
Sample Count ◽  
Insurance Data

Psychology and ethnic play a central role within Saudi Arabia market. Thus, this editorial argues the cultural and emotion effects on the Saudi consumption of life insurance. Even thought, the present paper intend to understand the reasons of the slowly growth of the purchase of this product and how to increase its rate in the insurance portfolio? Subsequently, this study target to confirm the significant effects of religiosity, optimism, opinion leadership, emotional regulation on Saudi behavior to subscribe in life insurance. Data was collected from two samples. The first sample, count 210 respondents, worn to purify the measurement scales of the variables studied in the exploratory phase. The second sample was administered from belonging 654 policyholders in order to confirm the measures instruments, to verify the hypotheses, validate and re-specify the model. Thus, this study bears a theoretical interest for researchers and it is useful for practitioners in this sector.

scholarly journals On the Validation of Claims with Excess Zeros in Liability Insurance: A Comparative Study

Risks ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 71 ◽  
Author(s):  
Marjan Qazvini
Keyword(s):  
Poisson Regression ◽  
Third Party ◽  
Liability Insurance ◽  
Insurance Pricing ◽  
Excess Zeros ◽  
Insurance Portfolio ◽  
Zip Model ◽  
Validation Set ◽  
Insurance Data ◽  
Better Than

In this study, we consider the problem of zero claims in a liability insurance portfolio and compare the predictability of three models. We use French motor third party liability (MTPL) insurance data, which has been used for a pricing game, and show that how the type of coverage and policyholders’ willingness to subscribe to insurance pricing, based on telematics data, affects their driving behaviour and hence their claims. Using our validation set, we then predict the number of zero claims. Our results show that although a zero-inflated Poisson (ZIP) model performs better than a Poisson regression, it can even be outperformed by logistic regression.

Testing Spatial Dependence in Spatial Models with Endogenous Weights Matrices

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Anil K. Bera ◽  
Osman Doğan ◽  
Süleyman Taşpınar
Keyword(s):  
Spatial Dependence ◽  
Spatial Models ◽  
Test Statistics ◽  
Simple Test ◽  
Robust Test ◽  
Finite Samples ◽  
Robust Tests ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
Empirical Illustration

Abstract In this study, we propose simple test statistics for identifying the source of spatial dependence in spatial autoregressive models with endogenous weights matrices. Elements of the weights matrices are modelled in such a way that endogenity arises when the unobserved factors that affect elements of the weights matrices are correlated with the unobserved factors in the outcome equation. The proposed test statistics are robust to the presence of endogeneity in the weights and can be used to detect spatial dependence in the dependent variable and/or the disturbance terms. The robust test statistics are easy to calculate as computationally simple estimations are needed for their calculations. Our Monte Carlo results indicate that these tests have good size and power properties in finite samples. We also provide an empirical illustration to demonstrate the usefulness of the robust tests in identifying the source of spatial dependence.

scholarly journals Modeling Severe Acute Respiratory Syndrome Coronavirus 2019 (SARS-CoV-19) Incidence across Conterminous US Counties: A Spatial Perspective

2021 ◽  
Vol 4 ◽  
pp. 1-9
Author(s):  
Olawale Oluwafemi ◽  
Oluseyi Oladepo
Keyword(s):  
Health Outcomes ◽  
Urban Migration ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Incidence And Mortality ◽  
Lag Model ◽  
Spatial Autoregressive ◽  
Socio Economic Variables ◽  
Spatial Autoregressive Models ◽  
Urban Conditions

Abstract. This study examines the spatial distribution of COVID-19 incidence and mortality rates across the counties in the conterminous US in the first 604 days of the pandemic. The dataset was acquired from Emory University, Atlanta, United States, which includes socio-economic variables and health outcomes variables (N = 3106). OLS estimates accounted for 31% of the regression plain (adjusted R2 = 0.31) with AIC value of 9263, and Breusch-Pagan test for heteroskedasticity indicated 472.4, and multicollinearity condition number of 74.25. This result necessitated spatial autoregressive models, which were performed on GeoDa 1.18 software. ArcGIS 10.7 was used to map the residuals and selected significant variables. Generally, the Spatial Lag Model (SLM) and Spatial Error Model (SEM) models accounted for substantial percentages of the regression plain. While the efficiency of the models is the order of SLM (AIC: 8264.4: BreucshPagan test: 584.4; Adj. R2 = 0.56) > SEM (AIC: 8282.0; Breucsh-Pagan test: 697.2; Adj. R2 = 0.56). In this case, the least predictive model is SEM. The significant contribution of male, black race, poverty and urban and rural dummies to the regression plain indicated that COVID-19 transmission is more of a function of socio-economic, and rural/urban conditions rather than health outcomes. Although, diabetes and obesity showed a positive relationship with COVID-19 incidence. However, the relationship was relatively low based on the dataset. This study further concludes that the policymakers and health practitioners should consider spatial peculiarities, rural-urban migration and access to resources in reducing the transmission of COVID-19 disease.

scholarly journals Peer effects and scholastic achievement: spatial models estimates using the student friendship network at the classroom level

2019 ◽  
Vol 23 (1) ◽  
pp. 5-24
Author(s):  
Isabel Pessoa de Arruda Raposo ◽  
Tatiane Almeida de Menezes ◽  
Ricardo Carvalho de Andrade Lima ◽  
Ricardo Zimmerle da Nóbrega
Keyword(s):  
Standard Deviation ◽  
Peer Effects ◽  
Fixed Effects ◽  
Spatial Models ◽  
Friendship Networks ◽  
Friendship Network ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
The Individual ◽  
Identification Strategy

This paper evaluates the peer effects on individual academic performance. The identification strategy considers the architecture of friendship networks within classrooms, in addition to group and individual fixed effects. Estimates of spatial autoregressive models show that an increase of one standard deviation (sd) in peers’ math grade improves by 6% sds the student’s grade. Furthermore, when we also consider the indirect friendship bonds, the aggregate peer impact raises to 45% sds of the individual math grade.

scholarly journals Developing Non-Negative Spatial Autoregressive Models for Better Exploring Relation Between Nighttime Light Images and Land Use Types

2020 ◽  
Vol 12 (5) ◽  
pp. 798
Author(s):  
Honghan Zheng ◽  
Zhipeng Gui ◽  
Huayi Wu ◽  
Aihong Song
Keyword(s):  
Land Use ◽  
Spatial Autocorrelation ◽  
Autoregressive Models ◽  
Spatial Error ◽  
Land Use Types ◽  
Central Pixel ◽  
Nighttime Light ◽  
Spatial Autoregressive ◽  
Spatial Autoregressive Models ◽  
The Relationship

Exploring the relationship between nighttime light and land use is of great significance to understanding human nighttime activities and studying socioeconomic phenomena. Models have been studied to explain the relationships, but the existing studies seldom consider the spatial autocorrelation of night light data, which leads to large regression residuals and an inaccurate regression correlation between night light and land use. In this paper, two non-negative spatial autoregressive models are proposed for the spatial lag model and spatial error model, respectively, which use a spatial adjacency matrix to calculate the spatial autocorrelation effect of light in adjacent pixels on the central pixel. The application scenarios of the two models were analyzed, and the contribution of various land use types to nighttime light in different study areas are further discussed. Experiments in Berlin, Massachusetts and Shenzhen showed that the proposed methods have better correlations with the reference data compared with the non-negative least-squares method, better reflecting the luminous situation of different land use types at night. Furthermore, the proposed model and the obtained relationship between nighttime light and land use types can be utilized for other applications of nighttime light images in the population, GDP and carbon emissions for better exploring the relationship between nighttime remote sensing brightness and socioeconomic activities.

Assessing the Impact of Suicide Exclusion Periods on Life Insurance

Crisis ◽  
2010 ◽  
Vol 31 (4) ◽  
pp. 217-223 ◽  
Author(s):  
Paul Yip ◽  
David Pitt ◽  
Yan Wang ◽  
Xueyuan Wu ◽  
Ray Watson ◽  
...  
Keyword(s):  
Life Insurance ◽  
Accidental Death ◽  
Insurance Policy ◽  
Death Rates ◽  
Insurance Policies ◽  
Suicidal Death ◽  
Insured Individual ◽  
The Impact ◽  
Insurance Data ◽  
The Relationship

Background: We study the impact of suicide-exclusion periods, common in life insurance policies in Australia, on suicide and accidental death rates for life-insured individuals. If a life-insured individual dies by suicide during the period of suicide exclusion, commonly 13 months, the sum insured is not paid. Aims: We examine whether a suicide-exclusion period affects the timing of suicides. We also analyze whether accidental deaths are more prevalent during the suicide-exclusion period as life-insured individuals disguise their death by suicide. We assess the relationship between the insured sum and suicidal death rates. Methods: Crude and age-standardized rates of suicide, accidental death, and overall death, split by duration since the insured first bought their insurance policy, were computed. Results: There were significantly fewer suicides and no significant spike in the number of accidental deaths in the exclusion period for Australian life insurance data. More suicides, however, were detected for the first 2 years after the exclusion period. Higher insured sums are associated with higher rates of suicide. Conclusions: Adverse selection in Australian life insurance is exacerbated by including a suicide-exclusion period. Extension of the suicide-exclusion period to 3 years may prevent some “insurance-induced” suicides – a rationale for this conclusion is given.

scholarly journals Applications of Clustering with Mixed Type Data in Life Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 47
Author(s):  
Shuang Yin ◽  
Guojun Gan ◽  
Emiliano A. Valdez ◽  
Jeyaraj Vadiveloo
Keyword(s):  
Life Insurance ◽  
Clustering Algorithm ◽  
Early Stage ◽  
Financial Statements ◽  
Life Insurers ◽  
Insurance Portfolio ◽  
Policy Information ◽  
Spatial Attributes ◽  
Type Data ◽  
Significant Mortality

Death benefits are generally the largest cash flow items that affect the financial statements of life insurers; some may still not have a systematic process to track and monitor death claims. In this article, we explore data clustering to examine and understand how actual death claims differ from what is expected—an early stage of developing a monitoring system crucial for risk management. We extended the k-prototype clustering algorithm to draw inferences from a life insurance dataset using only the insured’s characteristics and policy information without regard to known mortality. This clustering has the feature of efficiently handling categorical, numerical, and spatial attributes. Using gap statistics, the optimal clusters obtained from the algorithm are then used to compare actual to expected death claims experience of the life insurance portfolio. Our empirical data contained observations of approximately 1.14 million policies with a total insured amount of over 650 billion dollars. For this portfolio, the algorithm produced three natural clusters, with each cluster having lower actual to expected death claims but with differing variability. The analytical results provide management a process to identify policyholders’ attributes that dominate significant mortality deviations, and thereby enhance decision making for taking necessary actions.

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