Notice of RetractionA non-parametric estimator used the Support Vector Machine for expected shortfall

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Risk management is one of the top priorities in the financial industry today. The research of quantifying risk is one of risk management´s centers. Quantifying the risk of financial time series amounts to measuring their expected shortfall. Asymmetric Power Distribution(APD) is a new family of densities for expected shortfall. The main feature of the APD is that it combines the flexible tail decay property with the asymmetry, which makes it particularly suited for modeling the behavior of financial returns. In this paper, a non-parametric estimator, as a improvement to the traditional estimators, used the Support Vector Machine(SVM)for expected shortfall based on APD is proposed. The simulated studies show that this method can depict the distribution characters of Asymmetric Power Distribution with good results.