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Entropy 2015, 17(7), 4519-4532; doi:10.3390/e17074519

Estimating Portfolio Value at Risk in the Electricity Markets Using an Entropy Optimized BEMD Approach

1
School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China
2
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 27 January 2015 / Revised: 18 June 2015 / Accepted: 23 June 2015 / Published: 26 June 2015
View Full-Text   |   Download PDF [222 KB, uploaded 26 June 2015]

Abstract

In this paper, we propose a new entropy-optimized bivariate empirical mode decomposition (BEMD)-based model for estimating portfolio value at risk (PVaR). It reveals and analyzes different components of the price fluctuation. These components are decomposed and distinguished by their different behavioral patterns and fluctuation range, by the BEMD model. The entropy theory has been introduced for the identification of the model parameters during the modeling process. The decomposed bivariate data components are calculated with the DCC-GARCH models. Empirical studies suggest that the proposed model outperforms the benchmark multivariate exponential weighted moving average (MEWMA) and DCC-GARCH model, in terms of conventional out-of-sample performance evaluation criteria for the model accuracy. View Full-Text
Keywords: portfolio value at risk (PVaR); entropy theory; bivariate empirical mode decomposition (BEMD); DCC-GARCH model portfolio value at risk (PVaR); entropy theory; bivariate empirical mode decomposition (BEMD); DCC-GARCH model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zou, Y.; Yu, L.; He, K. Estimating Portfolio Value at Risk in the Electricity Markets Using an Entropy Optimized BEMD Approach. Entropy 2015, 17, 4519-4532.

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