scholarly journals Pricing catastrophe reinsurance under the standard deviation premium principle

2021 ◽  
Vol 7 (3) ◽  
pp. 4472-4484
Author(s):  
Wen Chao ◽  

<abstract><p>Catastrophe reinsurance is an important way to prevent and resolve catastrophe risks. As a consequence, the pricing of catastrophe reinsurance becomes a core problem in catastrophic risk management field. Due to the severity of catastrophe loss, the Peak Over Threshold (POT) model in extreme value theory (EVT) is extensively applied to capture the tail characteristics of catastrophic loss distribution. However, there is little research available on the pricing formula of catastrophe excess of loss (Cat XL) reinsurance when the catastrophe loss is modeled by POT. In the context of POT model, we distinguish three different relations between retention and threshold, and then prove the explicit pricing formula respectively under the standard deviation premium principle. Furthermore, we fit POT model to the earthquake loss data in China during 1990–2016. Finally, we give the prices of earthquake reinsurance for different retention cases. The computational results illustrate that the pricing formulas obtained in this paper are valid and can provide basis for the pricing of Cat XL reinsurance contracts.</p></abstract>


2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ghulam Raza Khan ◽  
Alanazi Talal Abdulrahman ◽  
Osama Alamri ◽  
Zahid Iqbal ◽  
Maqsood Ahmad

Extreme value theory (EVT) is useful for modeling the impact of crashes or situations of extreme stress on investor portfolios. EVT is mostly utilized in financial modeling, risk management, insurance, and hydrology. The price of gold fluctuates considerably over time, and this introduces a risk on its own. The goal of this study is to analyze the risk of gold investment by applying the EVT to historical daily data for extreme daily losses and gains in the price of gold. We used daily gold prices in the Pakistan Bullion Market from August 1, 2011 to July 30, 2021. This paper covers two methods such as Block Maxima (BM) and Peak Over Threshold (POT) modeling. The risk measures which are adopted in this paper are Value at Risk (VaR) and Expected Shortfall (ES). The point and interval estimates of VaR and ES are obtained by fitting the Generalized Pareto (GPA) distribution. Moreover, in this paper, return-level forecasting is also included for the next 5 and 10 years by analyzing the Generalized Extreme Value (GEV) distribution.



Author(s):  
C. Guedes Soares ◽  
R. G. Ferreira ◽  
Manuel G. Scotto

This paper provides an overview of different methods of extrapolating environmental data to low probability levels based on the extreme value theory. It discusses the Annual Maxima method and the Peak Over Threshold method, using unified terminology and notation. Furthermore, it describes a method based on the r largest order statistics that has the advantage of providing more accurate parameters and quantile estimates than the Annual Maxima method. Several examples illustrate the methodology and reveal strengths and weaknesses of the various approaches.



2004 ◽  
Vol 2004 (3) ◽  
pp. 211-228 ◽  
Author(s):  
Mario V. Wüthrich


1985 ◽  
Author(s):  
M. R. Leadbetter


2006 ◽  
Vol 1 (2) ◽  
pp. 51-57 ◽  
Author(s):  
Mikhail Makarov


2005 ◽  
Vol 7 (2) ◽  
pp. 63-84 ◽  
Author(s):  
Kaj Nyström ◽  
Jimmy Skoglund




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