Journal of Risk
ISSN:
1465-1211 (print)
1755-2842 (online)
Editor-in-chief: Farid AitSahlia

Need to know
- Separation of the distribution function with an automated tail-detection procedure in risk management.
- Data-based tail modeling for risk assessment.
- Automation and improvement of risk assessment in risk management.
Abstract
In risk management, tail risks are of crucial importance. The quality of a tail model, which is determined by data from an unknown distribution, depends critically on the subset of data used to model the tail. Based on a suitably weighted mean square error, we present a completely automated method that can separate the required subset of data to model the tail. The selected data are used to determine the parameters of the tail model. Notably, no parameter specifications have to be made to apply the proposed procedure in the automatic evaluation of large amounts of data. Standard goodness-of-fit tests allow us to evaluate the quality of the fitted tail model. We apply the method to standard distributions that are usually considered in the finance and insurance industries. We consider the MSCI World Index as our example. We analyze historical data to identify the tail model and calculate the high quantiles required for a risk assessment.
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Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/
If you would like to purchase additional rights please email info@risk.net