scholarly journals Expected Value Premium Principle Pada Data Reasuransi

2020 ◽  
Vol 6 (2) ◽  
pp. 21-27
Author(s):  
Radot Mh Siahaan ◽  
Dian Anggraini ◽  
Andi Fitriawati ◽  
Dani Al Makhya
Keyword(s):  
Threshold Value ◽  
Expected Value ◽  
Value Premium ◽  
Fire Insurance ◽  
Pure Premium ◽  
Stop Loss ◽  
Insurance Data

The amount of stop loss cover reinsurance using krone as Danish currency. The stop loss cover reinsurance scheme with a retention value of r = 50 million krone from fire insurance data in Denmark from 1980-1990 with truncate date at 10 million krone, resulting in a conditional expected value that decreases in value when the higher the threshold value. This is indicated by the threshold value of 1 = 2.976 resulting in pure premium of 1 = 0.1217, a threshold value of 2 = 10.0539 resulting in pure premium 2 = 0.0867 and a threshold value of 3 = 26.199 resulting in pure premium 3 = 0.0849. The use of expected value premium principle with the loading factor () is weighted to the value of the pure premium represented by. This is indicated by the weight of premium 1 = 0.13387, the weight of the premium 2 = 0.09537 and the weight of premium 3 = 0.09339.

The Expected Value Premium

CFA Digest ◽  
2008 ◽  
Vol 38 (3) ◽  
pp. 35-36
Author(s):  
Michael Kobal
Keyword(s):  
Expected Value ◽  
Value Premium

Fitting Nonstationary Cox Processes: An Application to Fire Insurance Data

2020 ◽  
pp. 1-28
Author(s):  
Hansjörg Albrecher ◽  
José Carlos Araujo-Acuna ◽  
Jan Beirlant
Keyword(s):  
Cox Processes ◽  
Fire Insurance ◽  
Insurance Data

New composite models for the Danish fire insurance data

2012 ◽  
Vol 2014 (2) ◽  
pp. 180-187 ◽  
Author(s):  
S. Nadarajah ◽  
S.A.A. Bakar
Keyword(s):  
Fire Insurance ◽  
Composite Models ◽  
Insurance Data

scholarly journals Estimation of Stop Loss Premium in Fire Insurance

1963 ◽  
Vol 2 (3) ◽  
pp. 356-361 ◽  
Author(s):  
C. P. Welten
Keyword(s):  
Distribution Function ◽  
Fire Insurance ◽  
Stop Loss ◽  
In Fire

The estimation of stop loss premiums can be based on some knowledge about the distribution function of the sum of all claims in a year (assuming that the stop loss insurance relates to a period of one calender year). Generally speaking there are two methods to obtain this knowledge about the distribution function.1. The first method is to construct a distribution function from data concerning:a. the distribution function of the number of claims per year, taking into account the variability of the parameter(s) of this distribution function.b. the distribution function of the insured sums.c. the distribution function of partial claims.d. the correlation between the insured sum and the probability of occurring of a claim.e. the probability of contagion.2. The second method is to derive a distribution function from the year's totals of claims over a long series of years, expressed in e.g: units of the totals of insured sums in that years.In practice it is often difficult to find a useful basis to apply one of these methods. Data concerning the distribution function of the number of claims per year, of the insured sums, and of partial claims are mostly available, but often nothing is known about the correlation between the insured sum and the probability of occurring of a claim.The second method is mostly not applicable because, if the year's totals of claims over a long series of, by preference recent, years are available, these data often turn out to be heterogeneous or to be correlated with time. If, in that case, only the data of the most recent years are used, the number of these data is often a too small basis for the construction of a distribution function.

The expected value premium☆

2008 ◽  
Vol 87 (2) ◽  
pp. 269-280 ◽  
Author(s):  
L CHEN ◽  
R PETKOVA ◽  
L ZHANG
Keyword(s):  
Expected Value ◽  
Value Premium

scholarly journals Penerapan Metode Limited-Fluctuation Credibility dalam Menentukan Premi Murni pada Asuransi Kendaraan Bermotor di PT XYZ

2021 ◽  
Vol 4 (2) ◽  
pp. 126
Author(s):  
Mira Zakiah Rahmah ◽  
Aceng Komarudin Mutaqin
Keyword(s):  
Motor Vehicle ◽  
Secondary Data ◽  
Past Experience ◽  
Credibility Theory ◽  
Partial Loss ◽  
Pure Premium ◽  
Premium Rates ◽  
Motor Vehicle Insurance ◽  
Insurance Data

<p><strong>Abstract. </strong>This paper discusses the method of limited-fluctuation credibility, also known as classic credibility. Credibility theory is a technique for predicting future premium rates based on past experience data. Limited fluctuation credibility consists of two credibility, namely full credibility if Z = 1 and partial credibility if Z &lt;1. Full credibility is achieved if the amount of recent data is sufficient for prediction, whereas if the latest data is insufficient then the partial credibility approach is used. Calculations for full and partial credibility standards are used for loss measures such as frequency of claims, size of claims, aggregate losses and net premiums. The data used in this paper is secondary data recorded by the company PT. XYZ in 2014. This data contains data on the frequency of claims and the size of the policyholder's partial loss claims for motor vehicle insurance products category 4 areas 1. Based on the results of the application, the prediction of pure premiums for 2015 cannot be fully based on insurance data for 2014 because the credibility factor value is less than 1. So based on the limited-fluctuation credibility method, the prediction of pure premiums for 2015 must be based on manual values for pure premiums as well as insurance data for 2014. If manual values for pure premium is 2,000,000 rupiah, then the prediction of pure premium for 2015 is 1,849,342 rupiah.</p><p><strong>Keywords</strong><strong>: </strong>limited fluctuation credibility, full credibility, partial credibility and partial loss</p>

scholarly journals The Expected Value Premium

2006 ◽  
Author(s):  
Long Chen ◽  
Ralitsa Petkova ◽  
Lu Zhang
Keyword(s):  
Expected Value ◽  
Value Premium

MODELLING INSURANCE DATA WITH THE PARETO ARCTAN DISTRIBUTION

2015 ◽  
Vol 45 (3) ◽  
pp. 639-660 ◽  
Author(s):  
Emilio Gómez-Déniz ◽  
Enrique Calderín-Ojeda
Keyword(s):  
Scale Parameter ◽  
Survival Function ◽  
Inverse Gamma ◽  
Heavy Tailed Distributions ◽  
Fire Insurance ◽  
Tangent Function ◽  
Heavy Tailed ◽  
Limiting Case ◽  
Insurance Data ◽  
Theoretical Results

AbstractIn this paper, a new methodology based on the use of the inverse of the circular tangent function that allows us to add a scale parameter (say α) to an initial survival function is presented. The latter survival function is determined as limiting case when α tends to zero. By choosing as parent the classical Pareto survival function, the Pareto ArcTan (PAT) distribution is obtained. After providing a comprehensive analysis of its statistical properties, theoretical results with reference to insurance are illustrated. Its performance is compared, by means of the well-known Norwegian fire insurance data, with other existing heavy-tailed distributions in the literature such as Pareto, Stoppa, Shifted Lognormal, Inverse Gamma and Fréchet distributions.

scholarly journals Pareto-Optimal Reinsurance Revisited: A Two-Stage Optimisation Procedure Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ying Fang ◽  
Lu Wang ◽  
Zhongfeng Qu ◽  
Wenguang Yu
Keyword(s):  
Value At Risk ◽  
Integral Form ◽  
Expected Value ◽  
Value Premium ◽  
Pareto Optimal ◽  
Two Stage ◽  
Optimal Reinsurance ◽  
Incentive Compatible ◽  
Ex Post ◽  
Optimisation Procedure

In this paper, based on the Tail-Value-at-Risk (TVaR) measure, we revisit the Pareto-optimal reinsurance policies for the insurer and the reinsurer via a two-stage optimisation procedure. To reduce ex-post moral hazard, we assume that reinsurance contracts satisfy the principle of indemnity and the incentive compatible constraint which have been advocated by Huberman et al. (1983). We show that the Pareto-optimal reinsurance policy exists if the reinsurance premiums can be expressed as an integral form. The proposed class of premium principles encompasses the net premium principle, expected value premium principle, TVaR premium principle, generalized percentile premium principle, and so on. We further use the TVaR premium principle and the expected value premium principle as examples to illustrate the two-stage optimisation procedure by deriving explicitly the Pareto-optimal reinsurance policies. We extend the results by Cai et al. (2017) when the expected value premium principle is replaced by the TVaR premium principle.

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