JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (5): 77-87.doi: 10.6040/j.issn.1671-9352.0.2018.453

•   • Previous Articles     Next Articles

Domestic carbon price fluctuation and regional characteristics based on H-P filtering method

Shao-hui ZOU1,2(),Tian ZHANG3,*(),Xiao-xia YAN1,2   

  1. 1. School of Management, Xi'an University of Science and Technology, Xi'an 710054, Shaanxi, China
    2. Energy Economy and Management Research Center, Xi'an University of Science and Technology, Xi'an 710054, Shaanxi, China
    3. University of International Business and Economics, Beijing 100029, China
  • Received:2018-08-06 Online:2019-05-20 Published:2019-05-09
  • Contact: Tian ZHANG E-mail:zoushaohui1976@163.com;2274540847@qq.com
  • Supported by:
    国家自然科学基金资助项目(71273207);国家自然科学基金资助项目(71704140);陕西省科学技术研究发展计划项目(2011kjxx54);陕西省留学人员科技活动择优项目

RichHTML

20

PDF (PC)

391

Abstract:

The price of carbon emission trading in China has obvious volatility and regional differences. Scientifically describing the volatility of carbon emission trading price and analyzing the differences of different regions are conducive to avoiding investment risks, developing carbon market smoothly and improving the pricing ability of domestic carbon market in the international market. It is also particularly important to speed up the establishment of a unified national carbon market. H-P filtering is a commonly used trend decomposition method for economic variables, which can effectively analyze the seasonal variation law in time series data. Based on monthly data of carbon emission trading prices in seven major regions of China from December 2013 to June 2018, H-P filtering method is used to empirically study the fluctuation law and regional characteristics of domestic carbon prices. The results show that the domestic carbon price has a significant characteristic of "falling in fluctuation", showing 3 complete cycles, the time range of each cycle is 10~22 months. Peak and valley values show a downward trend in varying degrees, and all of them change from positive to negative, the cycle types show a steep downward trend. From the regional perspective, the volatility of carbon emission trading price in Tianjin and Beijing is more obvious, while the fluctuation of carbon emission trading price in Hubei and Chongqing has less impact on Tianjin.

Key words: carbon emissions trading price, H-P filtering, fluctuation rule, seasonal variation

CLC Number: 

  • F830

Fig.1

Domestic carbon price fluctuation"

Table 1

Average price of carbon emission rights in Shenzhen from 2013 to 2018"

(单位:元/吨二氧化碳当量)
2013年2014年2015年2016年2017年2018年
1月66.000 031.700 039.120 024.010 027.820 0
2月81.090 031.700 044.500 031.500 034.760 0
3月81.000 040.560 040.500 035.010 034.760 0
4月68.930 045.100 040.500 035.010 034.760 0
5月68.930 045.100 033.530 029.310 038.840 0
6月64.320 037.410 028.700 025.170 038.840 0
7月61.590 030.210 028.700 028.350 0
8月61.590 028.310 028.700 027.550 0
9月64.160 061.590 039.790 023.490 027.550 0
10月76.530 030.000 039.790 024.550 024.140 0
11月76.530 030.000 040.340 024.010 024.320 0
12月66.000 031.700 039.120 024.010 024.320 0

Fig.2

Carbon emission trading price and price fluctuations with Census X12"

Fig.3

Growth of carbon price in Shenzhen on a month-on-month basis"

Fig.4

Seasonal and irregular fluctuations of carbon emission trading prices in Shenzhen"

Fig.5

Seasonal fluctuation of carbon emission trading price in Shenzhen from 2013 to 2018"

Fig.6

Trend and cycle change of carbon emission trading price in Shenzhen from 2013 to 2018"

Table 2

Periodic characteristics of price fluctuations in carbon emission trading in Shenzhen"

周期时间波长/月峰值达到峰值时长/月谷值达到谷值时长/月周期类型
12013.09—2014.06101112.79陡降缓升
22014.07—2015.12187.16-13.612陡降陡升
32016.01—2017.1022-0.28-4.614陡降缓升
42017.11—2018.068/////

Fig.7

H-P filtering of the transaction prices of seven major carbon emission rights in China"

Table 3

Relevance analysis between price volatility of carbon emissions trading in different regions"

北京地区重庆地区广东地区湖北地区上海地区深圳地区天津地区
北京地区1
重庆地区0.782 0441
广东地区0.784 2800.927 3001
湖北地区0.688 2200.662 1000.607 6101
上海地区0.187 1540.121 4450.051 7250.297 0501
深圳地区0.999 8600.891 5300.919 8820.703 380.845 5651
天津地区0.922 1400.319 620-0.005 0520.086 5130.683 4320.820 3391

Table 4

Relevance analysis between trend values of carbon emission trading prices in different regions"

北京地区重庆地区广东地区湖北地区上海地区深圳地区天津地区
北京地区1
重庆地区0.759 8821
广东地区0.800 8670.665 9901
湖北地区0.688 1190.331 0070.823 9541
上海地区0.717 8900.685 9540.965 2200.567 7401
深圳地区0.559 6800.721 8900.702 3490.858 9840.542 0601
天津地区0.838 0720.842 1600.786 5340.916 7000.544 4070.890 7021
1 周天芸, 许锐翔. 中国碳排放权交易价格的形成及其波动特征—基于深圳碳排放权交易所的数据[J]. 金融发展研究, 2016, (1): 16- 25.
doi: 10.3969/j.issn.1674-2265.2016.01.003
ZHOU Tianyun , XU Ruixiang . The formation and volitility features of China's carbon emission trading price: based on the data of Shenzhen carbon emission exchange[J]. Journal of Financial Development Research, 2016, (1): 16- 25.
doi: 10.3969/j.issn.1674-2265.2016.01.003
2 吕勇斌, 邵律博. 我国碳排放权价格波动特征研究:基于GARCH族模型的分析[J]. 价格理论与实践, 2015, (12): 62- 64.
LYU Yongbin , SHAO Lyubo . Study on the price fluctuation characteristics of carbon emission rights in China based on GARCH family model[J]. Price:Theory & Practice, 2015, (12): 62- 64.
3 ZHANG Y. Pricing decision theory and the empirical research on international carbon emissions trading[C]//International Conference on Computer Science and Electronics Engineering. Hangzhou: IEEE, 2012: 6-10.
4 JOYEUX R , MILUNOVICH G . Testing market efficiency in the EU carbon futures market[J]. Applied Financial Economics, 2010, 20 (10): 803- 809.
doi: 10.1080/09603101003636220
5 FANG G C , TIAN L X , LIU M H , et al. How to optimize the development of carbon trading in China: enlightenment from evolution rules of the EU carbon price[J]. Applied Energy, 2018, 211: 1039- 1049.
doi: 10.1016/j.apenergy.2017.12.001
6 ZHAO X G , JIANG G W , NIE D , et al. How to improve the market efficiency of carbon trading: a perspective of China[J]. Renewable and Sustainable Energy Reviews, 2016, 59: 1229- 1245.
doi: 10.1016/j.rser.2016.01.052
7 ZHAO X G , WU L , LI A . Research on the efficiency of carbon trading market in China[J]. Renewable and Sustainable Energy Reviews, 2017, 79: 1- 8.
doi: 10.1016/j.rser.2017.05.034
8 CONG R , LO A Y . Emission trading and carbon market performance in Shenzhen, China[J]. Applied Energy, 2017, 193: 414- 425.
doi: 10.1016/j.apenergy.2017.02.037
9 袁嫄, 刘纪显, 张芳. 碳配额市场价格非对称性波动研究:基于欧盟碳配额管理制度的实证分析[J]. 金融论坛, 2015, 20 (5): 44- 53, 70.
YUAN Yuan , LIU Jixian , ZHANG Fang . A study of the asymmetry fuctuation in the price of carbon quota market: an analysis based on the management system of EU carbon quota[J]. Finance Forum, 2015, 20 (5): 44- 53, 70.
10 胡根华, 吴恒煜. 资产价格的时变跳跃:碳排放交易市场的证据[J]. 中国人口资源与环境, 2015, 25 (11): 12- 18.
HU Genhua , WU Hengyu . Time-varying jumps of capital's price: evidence from carbon emission trading market[J]. China Population, Resources and Environment, 2015, 25 (11): 12- 18.
11 刘承智, 潘爱玲, 谢涤宇. 我国碳排放权交易市场价格波动问题探讨[J]. 价格理论与实践, 2014, (8): 55- 57.
LIU Chengzhi , PAN Ailing , XIE Diyu . Price fluctuation in China's carbon emission trading market[J]. Price:Theory & Practice, 2014, (8): 55- 57.
12 汪文隽, 周婉云, 李瑾, 等. 中国碳市场波动溢出效应研究[J]. 中国人口·资源与环境, 2016, 26 (12): 63- 69.
WANG Wenjun , ZHOU Wanyun , LI Jin , et al. Volatility spillovers among Chinese carbon markets[J]. China Population, Resources and Environment, 2016, 26 (12): 63- 69.
13 张晨, 杨仙子. 基于多频组合模型的中国区域碳市场价格预测[J]. 系统工程理论与实践, 2016, 36 (12): 3017- 3025.
doi: 10.12011/1000-6788(2016)12-3017-09
ZHANG Chen , YANG Xianzi . Forecasting of China's regional carbon market price based on multi-frequency combined model[J]. Systems Engineering-Theory & Practice, 2016, 36 (12): 3017- 3025.
doi: 10.12011/1000-6788(2016)12-3017-09
14 邹绍辉, 张甜. 国际碳期货价格与国内碳价动态关系[J]. 山东大学学报(理学版), 2018, 53 (5): 70- 79.
ZOU Shaohui , ZHANG Tian . Interaction relationship between international carbon future price and domestic carbon price[J]. Journal of Shandong University (Natural Science), 2018, 53 (5): 70- 79.
15 郭白滢, 周任远. 我国碳交易市场价格周期及其波动性特征分析[J]. 统计与决策, 2016, (21): 154- 157.
GUO Baihao , ZHOU Renyuan . Analysis of price cycle and volatility characteristics of China's carbon trading market[J]. Statistics & Decision, 2016, (21): 154- 157.
16 白云帆.欧盟碳排放权市场价格波动规律研究[D].广州:暨南大学, 2016.
BAI Yunfan. The study on the price fluctuation laws of EU ETS[D]. Guangzhou: Jinan University, 2016.
17 HODRICK R, PRESCOTT E. Postwar U.S. business cycles[M]//Real Business Cycles. London: Routledge, 1998: 593-608.
[1] LIU Qian-hui1, PEI Hai-yan1, 2,*, HU Wen-rong1,2, XIE Jun3. Population characteristics and seasonal variations of  phytoplankton in Nansi Lake [J]. J4, 2010, 45(5): 12-18.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] ZHAO Jun1, ZHAO Jing2, FAN Ting-jun1*, YUAN Wen-peng1,3, ZHANG Zheng1, CONG Ri-shan1. Purification and anti-tumor activity examination of water-soluble asterosaponin from Asterias rollestoni Bell[J]. J4, 2013, 48(1): 30 -35 .
[2] YANG Yong-wei1, 2, HE Peng-fei2, LI Yi-jun2,3. On strict filters of BL-algebras#br#[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(03): 63 -67 .
[3] MENG Xiang-bo1, ZHANG Li-dong1, DU Zi-ping2. Investment and reinsurance strategy for insurers under #br# mean-variance criterion with jumps#br#[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(05): 36 -40 .
[4] Ming-Chit Liu. THE TWO GOLDBACH CONJECTURES[J]. J4, 2013, 48(2): 1 -14 .
[5] TANG Xiao-hong1, HU Wen-xiao2*, WEI Yan-feng2, JIANG Xi-long2, ZHANG Jing-ying2, SHAO Xue-dong3. Screening and biological characteristics studies of wide wine-making yeasts[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(03): 12 -17 .
[6] WANG Kai-rong, GAO Pei-ting. Two mixed conjugate gradient methods based on DY[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(6): 16 -23 .
[7] LI Shou-ju1,SHANGGUAN Zi-chang2,3,SUN Wei4,LUAN Mao-tian1,LIU Bo3. Parameter  inversion  procedure  for  a  nonlinear constitutive  model  of  conditioned  soils[J]. J4, 2010, 45(7): 24 -27 .
[8] ZENG Weng-fu1, HUANG Tian-qiang1,2, LI Kai1, YU YANG-qiang1, GUO Gong-de1,2. A local linear emedding agorithm based on harmonicmean geodesic kernel[J]. J4, 2010, 45(7): 55 -59 .
[9] QIU Tao-rong, WANG Lu, XIONG Shu-jie, BAI Xiao-ming. A granular computing approach for knowledge hiding[J]. J4, 2010, 45(7): 60 -64 .
[10] SUN Xiao-ting1, JIN Lan2*. Application of DOSY in oligosaccharide mixture analysis[J]. J4, 2013, 48(1): 43 -45 .
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd