article

Hilbert Spectra and Empirical Mode Decomposition: A Multiscale Event Analysis Method to Detect the Impact of Economic Crises on the European Carbon Market

Authors:

Julien Chevallier,

Yi-Ming WeiAuthors Info & Claims

Computational Economics, Volume 52, Issue 1

Pages 105 - 121

https://doi.org/10.1007/s10614-017-9664-x

Published: 01 June 2018 Publication History

Abstract

Exploring the effect of an economic crisis on the carbon market can be propitious to understand the formation mechanisms of carbon pricing, and prompt the healthy development of the carbon market. Through the ensemble empirical mode decomposition (EEMD), a multiscale event analysis approach is proposed for exploring the effect of an economic crisis on the European carbon market. Firstly, we determine the appropriate carbon price data of the estimation and event windows to embody the impact of the interested economic crisis on carbon market. Secondly, we use the EEMD to decompose the carbon price into simple modes. Hilbert spectra are adopted to identify the main mode, which is then used to estimate the strength of an extreme event on the carbon price. Thirdly, we perform a multiscale analysis that the composition of the low-frequency modes and residue is identifying as the main mode to capture the strength of the interested economic crisis on the carbon market, and the high-frequency modes are identifying as the normal market fluctuations with a little short-term effect on the carbon market. Finally, taking the 2007---2009 global financial crisis and 2009---2013 European debt crisis as two cases, the empirical results show that contrasted with the traditional intervention analysis and event analysis with the principle of "one divides into two", the proposed method can capture the influences of an economic crisis on the carbon market at various timescales in a nonlinear framework.

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Digital Library

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Zhu, B. Z., & Wei, Y. M. (2013). Carbon price prediction with a hybrid ARIMA and least squares support vector machines methodology. Omega, 41(3), 517-524.

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cover image Computational Economics

Computational Economics Volume 52, Issue 1

June 2018

305 pages

ISSN:0927-7099

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2018

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Song JZhang YWang SLiu ZSun D(2022)Neural network combining X-ray and ultrasound in breast examinationNeural Computing and Applications10.1007/s00521-021-05882-234:5(3523-3535)Online publication date: 1-Mar-2022
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Li HYao HChen LCao CLi M(2022)Assessment of circular-bored twin tunnel (CBTT) performance using soft computing methodsEngineering with Computers10.1007/s00366-021-01288-938:4(2975-2990)Online publication date: 1-Aug-2022
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