Enriching the VaR framework to EEMD with an application to the European carbon market

• Author: Bangzhu Zhu,Julien Chevallier,Ping Wang,Yi‐Ming Wei,Rui Xie
• Published date: 01 July 2018
• DOI: http://doi.org/10.1002/ijfe.1618
• Date: 01 July 2018

RESEARCH ARTICLE

Enriching the VaR framework to EEMD with an application

to the European carbon market

Bangzhu Zhu

1

| Ping Wang

1

| Julien Chevallier

2,3

|Yi‐Ming Wei

4

| Rui Xie

5

1

School of Management, Jinan University,

Guangzhou 510632, China

2

IPAG Business School, IPAG Lab, 184

Boulevard Saint‐Germain, Paris 75006,

France

3

Université Paris 8 (LED), 2 avenue de la

Liberté, 93526 Saint‐Denis cedex, France

4

Center for Energy and Environmental

Policy Research, Beijing Institute of

Technology, Beijing 100081, China

5

School of Economics and Trade, Hunan

University, Changsha 410082, China

Correspondence

Julien Chevallier, IPAG Business School,

IPAG Lab., 184 Boulevard Saint‐Germain,

Paris 75006, France.

Email: julien.chevallier@ipag.fr

Funding information

National Natural Science Foundation of

China (NSFC), Grant/Award Numbers:

71201010, 71303174 and 71473180;

National Philosophy and Social Science

Foundation of China, Grant/Award Num-

bers: 14AZD068 and 15ZDA054; Natural

Science Foundation for Distinguished

Young Talents of Guangdong, Grant/

Award Number: 2014A030306031; Distin-

guished Young Teachers of Guangdong,

Grant/Award Number: [2014]145; High‐

level Personnel Project of Guangdong,

Grant/Award Number: [2013]246; Guang-

zhou key base of humanities and social

science –Centre for Low Carbon Eco-

nomic Research

Abstract

Unlike common financial markets, the European carbon market is a typically

heterogeneous market, characterized by multiple timescales, and affected by

extreme events. The traditional value‐at‐risk (VaR) with single‐timescale fails

to deal with the multi‐timescale characteristics and the effects of extreme events,

which can result in the VaR overestimation for carbon market risk. To measure

accurately the risk on the European carbon market, we propose an ensemble

empirical mode decomposition (EEMD)‐based multiscale VaR approach. First,

the EEMD algorithm is utilized to decompose the carbon price return into

several intrinsic mode functions (IMFs) with different timescales and a residue,

which are modelled, respectively, using the ARMA‐Generalized Autoregressive

Conditional Heteroscedasticity model to obtain their conditional variances at

different timescales. Furthermore, the Iterated Cumulative Sums of Squares

algorithm is employed to determine the windows of an extreme event, so as to

identify the IMFs influenced by an extreme event and conduct an exponentially

weighted moving average on their conditional variations. Finally, the VaRs of

various IMFs and the residue are estimated to reconstruct the overall VaR, the

validity of which is verified later. Then, we illustrate the results by considering

several European carbon futures contracts. Compared with the traditional VaR

framework with single timescale, the proposed multiscale VaR‐EEMD model

can effectively reduce the influences of the heterogeneous environments (such

as the influences of extreme events) and obtain a more accurate overall risk mea-

sure on the European carbon market. By acquiring the distributions of carbon

market risks at different timescales, the proposed multiscale VaR‐EEMD estima-

tion is capable of understanding the fluctuation characteristics more comprehen-

sively, which can provide new perspectives for exploring the evolution law of the

risks on the European carbon market.

AREA FOR REVIEW

Financial modelling and risk management,Sustainable OR, Value at risk systems

KEYWORDS

ARMA‐GARCH, ensemble empirical modedecomposition, European carbon market, exponentially

weighted moving average, iterated cumulativesums of squares, value‐at‐risk

DOI: 10.1002/ijfe.1618

Int J Fin Econ. 2018;23:315–328. Copyright © 2018 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/ijfe 315