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.
Highlights
As an indispensable industry input nowadays, electricity is an integral part of the modern economy.With the deregulation movement reforming the electricity industry since the 1970s, worldwide electricityEntropy 2015, 17 markets have become more competitive and integrated
We propose an entropy-optimized bivariate empirical mode decomposition (BEMD)-based methodology to study risk evolutions and estimate portfolio value at risk (PVaR) in the electricity markets
We propose the entropy theory to identify the BEMD model specifications and construct an effective PVaR estimation algorithm based on that
Summary
As an indispensable industry input nowadays, electricity is an integral part of the modern economy. Samui and Samantaray [22] incorporated the wavelet entropy measure in constructing the measuring index for islanding detection in distributed generation [22] To tackle these theoretical and methodological challenges, in this paper, we propose an entropy measure to quantify the forecasting accuracy using the in-sample data and identify the appropriate model specifications in the multiscale analysis. Different from the previous approaches to using entropy theory to measure the information distribution in the wavelet domain, the proposed model uses the entropy to measure the information content of the predictors in the EMD decomposed domain and to determine the appropriate levels for both data and extreme event components in the BEMD algorithm during the in-sample model tuning process.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
