High volatility, thick tails and extreme value theory in value-at-risk estimation

high volatility, thick tails and extreme value theory in value at risk estimation: the case of liability insurance in iran insurance company

در این بررسی ابتدا به بررسی ماهیت توزیع خسارات پرداخته میشود و از روش نظریه مقادیر نهایی برای بدست آوردن برآورد ارزش در معرض خطر برای خسارات روزانه بیمه مسئولیت شرکت بیمه ایران استفاده میشود. سپس کارایی نظریه مقدار نهایی در برآورد ارزش در معرض خطر با کارایی سایر روشهای واریانس ، کواریانس و روش شبیه سازی تاریخی مورد مقایسه قرار میگیرد. نتایج این بررسی نشان میدهند که توزیع ،garch شناخته شده مدل...

Analytical Estimation of Value at Risk Under Thick Tails and Fast Volatility Updating

Value at Risk Estimation Using Extreme Value Theory

A common assumption in quantitative financial risk modelling is the distributional assumption of normality in the asset’s return series, which makes modelling easy but proves to be inefficient if the data exhibit extreme tails. When dealing with extreme financial events like the Global Financial Crisis of 2007-2008 while quantifying extreme market risk, Extreme Value Theory (EVT) proves to be a...

Using Extreme Value Theory to Estimate Value-at-Risk

Martin Odening and Jan Hinrichs Abstract: This article examines problems that may occur when conventional Value-at-Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard VaR methods, such as variance-covariance method or historical simulation, can fail when the return distribution is fat tailed. This problem is aggravated when long-term VaR for...

Nonparametric estimation of conditional value-at-risk and expected shortfall based on extreme value theory

Abstract. We propose nonparametric estimators for conditional value-at-risk (VaR) and expected shortfall (ES) associated with conditional distributions of a series of returns on a financial asset. The return series and the conditioning covariates, which may include lagged returns and other exogenous variables, are assumed to be strong mixing and follow a fully nonparametric conditional location...