scholarly journals On the improved thinning risk model under a periodic dividend barrier strategy

2021 ◽  
Vol 6 (12) ◽  
pp. 13448-13458
Author(s):  
Fuyun Sun ◽  
◽  
Yuelei Li ◽  

<abstract><p>In this study, we consider a periodic dividend barrier strategy in an improved thinning risk model, which indicates that insurance companies randomly receive premiums and pay dividends. In the improved model, the premium is stochastic, and the claim counting process is a p-thinning process of the premium counting process. The integral equations satisfied by the Gerber-Shiu function and the expected discounted cumulative dividend function are derived. Explicit expressions of those actuarial functions are obtained when the claim and premium sizes are exponentially distributed. We analyze and illustrate the impact of various parameters on them and obtain the optimal barrier. Finally, a conclusion is drawn.</p></abstract>

2011 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Y. ARBI ◽  
R. BUDIARTI ◽  
I G. P. PURNABA

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes or external problems. Insurance companies as financial institution that also faced at risk. Recording of operating losses in insurance companies, were not properly conducted so that the impact on the limited data for operational losses. In this work, the data of operational loss observed from the payment of the claim. In general, the number of insurance claims can be modelled using the Poisson distribution, where the expected value of the claims is similar with variance, while the negative binomial distribution, the expected value was bound to be less than the variance.Analysis tools are used in the measurement of the potential loss is the loss distribution approach with the aggregate method. In the aggregate method, loss data grouped in a frequency distribution and severity distribution. After doing 10.000 times simulation are resulted total loss of claim value, which is total from individual claim every simulation. Then from the result was set the value of potential loss (OpVar) at a certain level confidence.


Risks ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 60
Author(s):  
Cláudia Simões ◽  
Luís Oliveira ◽  
Jorge M. Bravo

Protecting against unexpected yield curve, inflation, and longevity shifts are some of the most critical issues institutional and private investors must solve when managing post-retirement income benefits. This paper empirically investigates the performance of alternative immunization strategies for funding targeted multiple liabilities that are fixed in timing but random in size (inflation-linked), i.e., that change stochastically according to consumer price or wage level indexes. The immunization procedure is based on a targeted minimax strategy considering the M-Absolute as the interest rate risk measure. We investigate to what extent the inflation-hedging properties of ILBs in asset liability management strategies targeted to immunize multiple liabilities of random size are superior to that of nominal bonds. We use two alternative datasets comprising daily closing prices for U.S. Treasuries and U.S. inflation-linked bonds from 2000 to 2018. The immunization performance is tested over 3-year and 5-year investment horizons, uses real and not simulated bond data and takes into consideration the impact of transaction costs in the performance of immunization strategies and in the selection of optimal investment strategies. The results show that the multiple liability immunization strategy using inflation-linked bonds outperforms the equivalent strategy using nominal bonds and is robust even in a nearly zero interest rate scenario. These results have important implications in the design and structuring of ALM liability-driven investment strategies, particularly for retirement income providers such as pension schemes or life insurance companies.


2019 ◽  
Vol 24 ◽  
Author(s):  
R. Egan ◽  
S. Cartagena ◽  
R. Mohamed ◽  
V. Gosrani ◽  
J. Grewal ◽  
...  

AbstractCyber Operational Risk: Cyber risk is routinely cited as one of the most important sources of operational risks facing organisations today, in various publications and surveys. Further, in recent years, cyber risk has entered the public conscience through highly publicised events involving affected UK organisations such as TalkTalk, Morrisons and the NHS. Regulators and legislators are increasing their focus on this topic, with General Data Protection Regulation (“GDPR”) a notable example of this. Risk actuaries and other risk management professionals at insurance companies therefore need to have a robust assessment of the potential losses stemming from cyber risk that their organisations may face. They should be able to do this as part of an overall risk management framework and be able to demonstrate this to stakeholders such as regulators and shareholders. Given that cyber risks are still very much new territory for insurers and there is no commonly accepted practice, this paper describes a proposed framework in which to perform such an assessment. As part of this, we leverage two existing frameworks – the Chief Risk Officer (“CRO”) Forum cyber incident taxonomy, and the National Institute of Standards and Technology (“NIST”) framework – to describe the taxonomy of a cyber incident, and the relevant cyber security and risk mitigation items for the incident in question, respectively.Summary of Results: Three detailed scenarios have been investigated by the working party:∙Employee leaks data at a general (non-life) insurer: Internal attack through social engineering, causing large compensation costs and regulatory fines, driving a 1 in 200 loss of £210.5m (c. 2% of annual revenue).∙Cyber extortion at a life insurer: External attack through social engineering, causing large business interruption and reputational damage, driving a 1 in 200 loss of £179.5m (c. 6% of annual revenue).∙Motor insurer telematics device hack: External attack through software vulnerabilities, causing large remediation / device replacement costs, driving a 1 in 200 loss of £70.0m (c. 18% of annual revenue).Limitations: The following sets out key limitations of the work set out in this paper:∙While the presented scenarios are deemed material at this point in time, the threat landscape moves fast and could render specific narratives and calibrations obsolete within a short-time frame.∙There is a lack of historical data to base certain scenarios on and therefore a high level of subjectivity is used to calibrate them.∙No attempt has been made to make an allowance for seasonality of renewals (a cyber event coinciding with peak renewal season could exacerbate cost impacts)∙No consideration has been given to the impact of the event on the share price of the company.∙Correlation with other risk types has not been explicitly considered.Conclusions: Cyber risk is a very real threat and should not be ignored or treated lightly in operational risk frameworks, as it has the potential to threaten the ongoing viability of an organisation. Risk managers and capital actuaries should be aware of the various sources of cyber risk and the potential impacts to ensure that the business is sufficiently prepared for such an event. When it comes to quantifying the impact of cyber risk on the operations of an insurer there are significant challenges. Not least that the threat landscape is ever changing and there is a lack of historical experience to base assumptions off. Given this uncertainty, this paper sets out a framework upon which readers can bring consistency to the way scenarios are developed over time. It provides a common taxonomy to ensure that key aspects of cyber risk are considered and sets out examples of how to implement the framework. It is critical that insurers endeavour to understand cyber risk better and look to refine assumptions over time as new information is received. In addition to ensuring that sufficient capital is being held for key operational risks, the investment in understanding cyber risk now will help to educate senior management and could have benefits through influencing internal cyber security capabilities.


2014 ◽  
Vol 915-916 ◽  
pp. 459-463
Author(s):  
He Quan Zhang

In order to deal with the impact on traffic flow of the rule, we compare the influence factors of traffic flow (passing, etc.) into viscous resistance of fluid mechanics, and establish a traffic model based on fluid mechanics. First, in heavy and light traffic, we respectively use this model to simulate the actual segment of the road and find that when the traffic is heavy, the rule hinder the further increase in traffic. For this reason, we make further improvements to the model to obtain a fluid traffic model based on no passing and find that the improved model makes traffic flow increase significantly. Then, the improved model is applied to the light traffic, we find there are no significant changes in traffic flow .In this regard we propose a new rule: when the traffic is light, passing is allowed, but when the traffic is heavy, passing is not allowed.


1990 ◽  
Vol 117 (2) ◽  
pp. 173-277 ◽  
Author(s):  
C. D. Daykin ◽  
G. B. Hey

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.


2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Miao Liu ◽  
Jianhua Wang ◽  
Yao He

Aims. This study aimed at assessing the impact of baseline bilirubin (TBiL) on the incidence of diabetic retinopathy (DR) based on a five-year cohort study which consisted of 5323 Chinese male diabetic patients.Methods. A cohort study based on 5323 male diabetic patients was conducted in Beijing, from 2009 to 2013. Both baseline TBiL and follow-up changes were measured. Cox proportional risk model was used to calculate the hazard ratio (HR) of TBiL for DR risk.Results. During the follow-up period, there were 269 new DR cases. The incidence of five-year follow-up was 5.1% (95% CI: 4.5%~5.6%). The TBiL level of those who had diabetic retinopathy was lower than that of those without (12.51+ 1.20 mol/L and 13.11+ 1.32μmol/L,P=0.033). And more interestingly, along with the quintiles of baseline TBiL, there showed a U-shaped curve with DR incidence. And the RRs were 0.928 (95% CI: 0.646–1.331), 0.544 (95% CI: 0.365–0.811), 0.913 (95% CI: 0.629–1.324), and 1.035 (95% CI: 0.725–1.479) for the second, third, fourth, and fifth quintiles of baseline TBiL levels, respectively, compared with the first quintile. For follow-up TBiL changes, after being adjusted for related covariables and baseline TBiL levels (as continuous variable) in the model, the RRs for DR were 1.411 (95% CI: 1.081–1.842) for those who had decreased TBiL level and 0.858 (95% CI: 0.770–0.947) for those who had increased TBiL level during follow-up. And this association was more prominent among those with lower baseline TBiL level.Conclusions. Serum TBiL had a U-shaped relationship with DR incidence, which was independent of control status of diabetes and other related covariates.


Author(s):  
Mykhailo Demydenko ◽  
Ihor Pistunov

The competitiveness of an insurance company depends on the competitiveness of the products and services it introduces in the market. The competitive advantages of the insurance company are expressed in the attractiveness and competitiveness of insurance policies. An economic and mathematical model of increasing the competitiveness of the insurance company is proposed, which allows to calculate the integrated indicator of competitiveness of the insurance policy based on a comprehensive system of indicators characterizing the reliability of the insurance company, quality of its services, competitiveness, social activity. To analyze the impact of these indicators on the competitiveness of the insurance policy and identify areas for improving the efficiency and competitiveness of the insurance company. The competitiveness of an insurance company depends on the competitiveness of the products and services it introduces in the market. The assessment of the quality of insurance company services is compliance with the needs, requirements, and insurance interests of customers. This assessment is performed each time an individual client chooses to cooperate with an insurance company that meets his insurance interests and wishes. Therefore, the overall competitiveness of the enterprise depends on the competitiveness of products and services offered on the market. The competitive advantages of the insurance company are expressed in the attractiveness and competitiveness of insurance policies. The insurance market in recent years has shown consistently high growth, which makes it attractive for doing business. In these conditions, the task of modeling the activities of the insurance company in a highly competitive market environment becomes relevant. A mathematical model of increasing the competitiveness of the insurance company is proposed, which allows to calculate the integrated indicator of competitiveness of the insurance policy based on a comprehensive system of indicators characterizing the reliability of the insurance company, quality of its services, competitiveness, social activity. With the proposed model, insurance companies can objectively assess their weaknesses and strengths to ensure continuous growth and decent competition in a competitive market environment. The model allows you to select performance indicators and perform modeling and determine the consequences of changes in this indicator, analyze the impact of these indicators on the competitiveness of insurance policies and identify areas for improving the efficiency and competitiveness of the insurance company. By conducting such experiments, insurance companies can make more informed choices and decisions, analyze areas of competitiveness, and more efficiently allocate resources.


2016 ◽  
Author(s):  
Andreas Ostler ◽  
Ralf Sussmann ◽  
Prabir K. Patra ◽  
Sander Houweling ◽  
Marko De Bruine ◽  
...  

Abstract. The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model-TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. In comparison to simulated model fields we find an improved model-TCCON XCH4 agreement for all models with MIPAS-based stratospheric CH4 fields. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 ppb to 13.7 ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 ppb to 4.3 ppb), and LMDz (Laboratoire de Météorologie Dynamique model with Zooming capability; from 6.8 ppb to 4.3 ppb), respectively. MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduce the average XCH4 bias for ACTM (3.3 ppb), but increase the average XCH4 bias for TM5 (10.8 ppb) and LMDz (20.0 ppb). These findings imply that the range of satellite-based stratospheric CH4 is insufficient to resolve a possible stratospheric contribution to differences in total column CH4 between TCCON and TM5 or LMDz. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. This implies that there is a need to better understand the impact of individual model transport components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) on modeled stratospheric CH4.


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