Statistical premium in correlated losses of insurance

Lai, Li-Hua

doi:10.1016/j.econmod.2015.05.002

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Currently, most insurers use the performance of vehicles, claim counts and previous insurance losses to calculate the future premium [ 1 – 4 ]. Being an independent variable, the insurance loss is one of the most important statistics, since it has been a basis for complicatedly pricing [ 5 – 7 ]. A great many of bonus-malus systems use the claims number to optimize services and improve competitiveness of insurers as well [ 8 – 12 ].They proposed that not all accidents produced the same individual claim size and thus it did not seem fair to penalize all policyholders in the same way when claims were presented.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the influential priority of the factors on insurance loss of public transit

Zhang¹,

Su²,

Ke³

et al. 2018

PLoS ONE

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Understanding correlation between influential factors and insurance losses is beneficial for insurers to accurately price and modify the bonus-malus system. Although there have been a certain number of achievements in insurance losses and claims modeling, limited efforts focus on exploring the relative role of accidents characteristics in insurance losses. The primary objective of this study is to evaluate the influential priority of transit accidents attributes, such as the time, location and type of accidents. Based on the dataset from Washington State Transit Insurance Pool (WSTIP) in USA, we implement several key algorithms to achieve the objectives. First, K-means algorithm contributes to cluster the insurance loss data into 6 intervals; second, Grey Relational Analysis (GCA) model is applied to calculate grey relational grades of the influential factors in each interval; in addition, we implement Naive Bayes model to compute the posterior probability of factors values falling in each interval. The results show that the time, location and type of accidents significantly influence the insurance loss in the first five intervals, but their grey relational grades show no significantly difference. In the last interval which represents the highest insurance loss, the grey relational grade of the time is significant higher than that of the location and type of accidents. For each value of the time and location, the insurance loss most likely falls in the first and second intervals which refers to the lower loss. However, for accidents between buses and non-motorized road users, the probability of insurance loss falling in the interval 6 tends to be highest.

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Section: Introductionmentioning

confidence: 99%

Evaluating the influential priority of the factors on insurance loss of public transit

Zhang¹,

Su²,

Ke³

et al. 2018

PLoS ONE

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“…In an insurance contract, it will cover the crop-facility loss due to combined-peril y. In risk models, the random sum (Bowers et al, 1997;Boland, 2007) represents the aggregate losses generated by the portfolio for the period under studies (Lai, 2015(Lai, , 2017 and AR(1)-GJR(1,1) -GARCH(1,1) loss model with climate changes copula estimated (Lai, 2016(Lai, , 2017. This paper choose to work with the tail value at risk of L  under extreme clime typhoon-flood perils in major insurance coverage at level…”

Section: Introductionmentioning

confidence: 99%

“…An agricultural exposure unit is, roughly, crop exposed to the possibility of loss, for example, fruit (53%), vegetable (14%), rice (13%), special crops (7%), mixed grain (6%) cut flowers (1.76), and facility (0.69%). Major natural perils include single-peril event such as typhoons (73%), floods (16%), frost damage (4%), drought (2%) and/or combined-perils in crops damaged(Lai, 2010(Lai, , 2015Lai and Wu, 2013). Here that depending on the temperature, precipitation days, sunshine duration and prospective crops-specific-peril damages may vary considerably This paper follow part of the risk-peril event on the Copula-AR-GARCH Loss model…”

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confidence: 99%

Losses and Economic Capital under Climate Change Dependence in Taiwan The Case of Crop

Dai¹,

Wong²,

Lai³

et al. 2017

Seventh International Conference on Advances in Economics, Management and Social Study - EMS 2017

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This research will first examine the impact of climate change, tail loss and economic capital on major crops losses due to single-peril in crop insurance program based on panel data using copula-simulation method for 10 major crops over the 1971-2012. The purpose of this research will build the copula of temperature, precipitation and sunshine duration are copula to be the major climate variables and to assess the tail value at risk (TVaR) loss and economic capital models under climate changes copula estimated. The findings will be important in providing useful knowledge to future help examining risk measures that measure TVaR loss that can be used for determining provisions and capital requirements in order to avoid insolvency on government agricultural budgeting or insurer's coverage and to discovering effective regulations of crop insurance design and to enhance the efficiency of agricultural markets in future.

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Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty

et al. 2023

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Statistical premium in correlated losses of insurance

Cited by 5 publications

References 14 publications

Evaluating the influential priority of the factors on insurance loss of public transit

Evaluating the influential priority of the factors on insurance loss of public transit

Losses and Economic Capital under Climate Change Dependence in Taiwan The Case of Crop

Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty

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