Fitting the generalized Pareto distribution to commercial fire loss severity: evidence from Taiwan

Lee, Wo-Chiang

doi:10.21314/jor.2012.244

Cited by 6 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At p% level, it may be defined as expected returns of the portfolio in the worst p% cases. For example, ES (0.1) is the expectation of the worst 10 cases out of 100 cases (Lee, 2012).By definition ES is defined as for confidence level "q" ES q = E(loss given that loss > VaR q ). After solving the above conditional expectation, we get the following form for GPD.…”

Section: Value At Risk and Expected Shortfallmentioning

confidence: 99%

“…However, one solution could be to consider a general class of distributions for fire loss data. Keeping the hope that these distributions would be flexible enough and conform the underlying data of fire loss severity in a reasonable way (Lee, 2012). We considered different parametric distributions like exponential, Pareto, gamma, logistic, generalized extreme value (GEV) and Generalized Pareto distribution (GPD) in this study.…”

Section: Selection Of the Probability Model For Commercial Fire Loss Datamentioning

confidence: 99%

“…Among the major insurance claims, commercial fire insurance has the largest value. Therefore, modeling the tail behavior of fire loss severity is of great concern for pricing and risk management (Lee, 2012). Risk management is a process of thinking and identifying the all possible risks and problems before they happen and making decisions to control the risk or minimize its impact.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Probability Modeling and Estimation of Risk Measures for Fire Loss Severity in Pakistan: An Application of Extreme Value Theory

Ahmad¹,

Almanjahie²,

Muhammad³

et al. 2019

ECECSR

View full text Add to dashboard Cite

Extreme events are increasing in the insurance and financial markets, causing large losses and ultimately huge insurance claims. Commercial fire loss severity has the largest value among the major insurance claims. The goal of our study is modeling the commercial fire loss severity and estimating the risk of extreme fire losses by using Extreme Value Theory (EVT). In the present study, we utilize the EVT (point over threshold modeling) for modeling the tail of fire loss data. We find that the Generalized Pareto distribution (GPD) gives more satisfactory fit to commercial fire loss data as compared to other parametric distributions including exponential, Pareto, gamma, logistic and generalized extreme value (GEV) distribution. In the empirical study, we determine the peaks over threshold of the GPD with the help of Mean Excess plots and Hill plots. We also estimate the risk measures like value at risk (VaR) and expected shortfall (ES). These estimates are helpful for pricing and risk management of non-insurance companies for their policy implications.

show abstract

Section: Value At Risk and Expected Shortfallmentioning

confidence: 99%

Section: Selection Of the Probability Model For Commercial Fire Loss Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Probability Modeling and Estimation of Risk Measures for Fire Loss Severity in Pakistan: An Application of Extreme Value Theory

Ahmad¹,

Almanjahie²,

Muhammad³

et al. 2019

ECECSR

View full text Add to dashboard Cite

show abstract

“…A comparative analysis of two composite models, namely, log-normal-Pareto and Weibull-Pareto, was carried out in [5]. Lee [6] undertook the modeling of fire loss severity (from Taiwan) using GP distribution, with the determination of the thresholds being carried out using mean excess plots and Hill plots. Several tests for goodness-of-fit were conducted and Value-at-Risk (VaR) as well as Expected Shortfall (ES) were determined.…”

Section: Introductionmentioning

confidence: 99%

Modeling premiums of non-life insurance companies in India

Sethi,

Chakrabarty

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…They show how the decision-making process, relating to the pricing of reinsurance, is optimized when considering a risk transfer position. Brazauskasa and Kleefeld (2016) modelled the severity and the tail risk associated with the Norwegian fire claims while that of Taiwan was fitted using the Generalized Pareto distribution (GPD) (Lee, 2012).…”

Section: Introductionmentioning

confidence: 99%

Reinsurance Pricing of Large Motor Insurance Claims in Nigeria: An Extreme Value Analysis

Chukwudum¹

2019

IJSP

View full text Add to dashboard Cite

Reinsurance is of utmost importance to insurers because it enables insurance companies cover risks that they, under normal circumstances, would not be able to cover on their own. An insurer needs to be able to evaluate his solvency probability and consequently, adjust his retention levels appropriately because the insurer’s retention level plays a vital role in determining the premiums he will pay to the reinsurer. To illustrate how Extreme Value theory can be applied, this study delves into modelling the probabilistic behaviour of the frequency and severity of large motor claims from the Nigerian insurance sector (2013-2016) using the Negative Binomial-Generalized Pareto distribution (NB-GPD). The annual loss distribution is simulated using the Monte Carlo method and it is used to predict the expected annual total claims and estimate the capital requirement for a year. Pricing of the Excess-of-loss (XL) reinsurance is also examined to aid insurers in optimizing their risk management decision in regards to the choice of their risk transfer position.

show abstract

Fitting the generalized Pareto distribution to commercial fire loss severity: evidence from Taiwan

Cited by 6 publications

References 0 publications

Probability Modeling and Estimation of Risk Measures for Fire Loss Severity in Pakistan: An Application of Extreme Value Theory

Probability Modeling and Estimation of Risk Measures for Fire Loss Severity in Pakistan: An Application of Extreme Value Theory

Modeling premiums of non-life insurance companies in India

Reinsurance Pricing of Large Motor Insurance Claims in Nigeria: An Extreme Value Analysis

Contact Info

Product

Resources

About