Value-at-Risk Estimation Method Based on Normal Distribution, Logistics Distribution and Historical Simulation
نویسندگان
چکیده
منابع مشابه
Value at Risk Estimation using the Kappa Distribution with Application to Insurance Data
The heavy tailed distributions have mostly been used for modeling the financial data. The kappa distribution has higher peak and heavier tail than the normal distribution. In this paper, we consider the estimation of the three unknown parameters of a Kappa distribution for evaluating the value at risk measure. The value at risk (VaR) as a quantile of a distribution is one of the import...
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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...
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This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of ...
متن کاملThe impact of distribution on value-at-risk measures
Value at risk is a popular approach to aid financial risk management. Questions about the appropriateness of the measure have arisen since the related 2008 bubble collapses in some US housing markets and the global financial market. These questions include the presence of fat tails and their impact. This paper compares results based upon assumptions of normality and logistic distributions, comp...
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ژورنال
عنوان ژورنال: Operations Research: International Conference Series
سال: 2020
ISSN: 2722-0974,2723-1739
DOI: 10.47194/orics.v1i1.19