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

2019 ◽  
Vol 53 (4/2019) ◽  
pp. 275-292
Author(s):  
AHMAD ISHFAQ ◽  
ALMANJAHIE IBRAHIM MUFRAH ◽  
ASGHER MUHAMMAD ◽  
EHTSHAM HAQUL
Keyword(s):  
Extreme Value Theory ◽  
Risk Measures ◽  
Value Theory ◽  
Extreme Value ◽  
Fire Loss ◽  
Probability Modeling ◽  
Loss Severity ◽  
Estimation Of Risk

scholarly journals Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory

1997 ◽  
Vol 27 (1) ◽  
pp. 117-137 ◽  
Author(s):  
Alexander J. McNeil
Keyword(s):  
Extreme Value Theory ◽  
Generalized Pareto Distribution ◽  
Value Theory ◽  
Extreme Value ◽  
Parametric Curve ◽  
Excess Loss ◽  
Generalized Pareto ◽  
Fire Insurance ◽  
Loss Severity ◽  
Parametric Curve Fitting

AbstractGood estimates for the tails of loss severity distributions are essential for pricing or positioning high-excess loss layers in reinsurance. We describe parametric curve-fitting methods for modelling extreme historical losses. These methods revolve around the generalized Pareto distribution and are supported by extreme value theory. We summarize relevant theoretical results and provide an extensive example of their application to Danish data on large fire insurance losses.

scholarly journals Risk Analysis of Gold Prices in Pakistan Using Extreme Value Theory

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Ghulam Raza Khan ◽  
Alanazi Talal Abdulrahman ◽  
Osama Alamri ◽  
Zahid Iqbal ◽  
Maqsood Ahmad
Keyword(s):  
Extreme Value Theory ◽  
Value At Risk ◽  
Risk Measures ◽  
Value Theory ◽  
Extreme Value ◽  
Return Level ◽  
Extreme Stress ◽  
Daily Data ◽  
Peak Over Threshold ◽  
The Impact

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.

scholarly journals EVT-based estimation of risk capital and convergence of high quantiles

2008 ◽  
Vol 40 (03) ◽  
pp. 696-715 ◽  
Author(s):  
Matthias Degen ◽  
Paul Embrechts
Keyword(s):  
Extreme Value Theory ◽  
Value Theory ◽  
Extreme Value ◽  
Second Order ◽  
Parametric Models ◽  
Tail Behavior ◽  
Parametric Approach ◽  
Risk Capital ◽  
High Quantiles ◽  
Estimation Of Risk

We discuss some issues regarding the accuracy of a quantile-based estimation of risk capital. In this context, extreme value theory (EVT) emerges naturally. The paper sheds some further light on the ongoing discussion concerning the use of a semi-parametric approach like EVT and the use of specific parametric models such as the g-and-h. In particular, we discusses problems and pitfalls evolving from such parametric models when using EVT and highlight the importance of the underlying second-order tail behavior.

Coherent Risk Measures

2014 ◽  
pp. 115-142
Keyword(s):  
At Risk ◽  
Extreme Value Theory ◽  
Risk Measures ◽  
Risk Measure ◽  
Value Theory ◽  
Extreme Value ◽  
Expected Shortfall ◽  
Coherent Risk Measures ◽  
Coherent Risk ◽  
Distortion Risk Measure

This chapter introduces some alternative risk measures to Vale-At-Risk (VaR) calculations: Extreme Value Theory (EVT), Expected Shortfall (ES) and distortion risk measure. It also discusses their more coherent characteristics useful for shoring up the weaknesses of VaR.

scholarly journals EVT-based estimation of risk capital and convergence of high quantiles

2008 ◽  
Vol 40 (3) ◽  
pp. 696-715 ◽  
Author(s):  
Matthias Degen ◽  
Paul Embrechts
Keyword(s):  
Extreme Value Theory ◽  
Value Theory ◽  
Extreme Value ◽  
Second Order ◽  
Parametric Models ◽  
Tail Behavior ◽  
Parametric Approach ◽  
Risk Capital ◽  
High Quantiles ◽  
Estimation Of Risk

We discuss some issues regarding the accuracy of a quantile-based estimation of risk capital. In this context, extreme value theory (EVT) emerges naturally. The paper sheds some further light on the ongoing discussion concerning the use of a semi-parametric approach like EVT and the use of specific parametric models such as the g-and-h. In particular, we discusses problems and pitfalls evolving from such parametric models when using EVT and highlight the importance of the underlying second-order tail behavior.

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