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Estimation of Value-at-Risk Using Mixture Copula Model for Heavy-Tailed Operational Risk Losses in Financial, Insurance & Climatological Data | IEEE Conference Publication | IEEE Xplore

Estimation of Value-at-Risk Using Mixture Copula Model for Heavy-Tailed Operational Risk Losses in Financial, Insurance & Climatological Data


Abstract:

Data fusion techniques are being regularly used for analysis in Operational Risk Management (ORM). A popular and commonly used risk metric of interest, Value-at-Risk (VaR...Show More

Abstract:

Data fusion techniques are being regularly used for analysis in Operational Risk Management (ORM). A popular and commonly used risk metric of interest, Value-at-Risk (VaR), has always been difficult to robustly estimate for different data types. The classical Monte Carlo simulation (MCS) approach (denoted henceforth as classical approach) assumes the independence of loss severity and loss frequency. In practice, this assumption may not always hold. To overcome this limitation and handle cases with heavy-tail data and more robustly estimate the corresponding VaR, we adopt a new approach known as Mixture Copula-based Parametric Modeling of Frequency and Severity (MCPFS). The proposed approach is verified via large-scale MCS experiments and validated on four publicly available financial datasets. We compare MCPFS with the classical approach for robust VaR estimation. We observe that the classical approach estimates VaR poorly while the MCPFS methodologies attain better VaR estimates for real-world data. These studies provide real-world evidence that the MCPFS methodologies have merits for its use to accurately estimate VaR.
Date of Conference: 10-13 July 2018
Date Added to IEEE Xplore: 06 September 2018
ISBN Information:
Conference Location: Cambridge, UK

References

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