|Table of Contents|

Commodity price risk supervision in the context of “data element ×”(PDF)

《长安大学学报（社科版）》[ISSN:1671-6248/CN:61-1391/C]

Issue:
2024年02期
Page:
82-97
Research Field:
经济学·“数据要素×”研究
Publishing date:
2024-04-20

Info

Title:
Commodity price risk supervision in the context of “data element ×”
Author(s):
XU Liang1;  WEN Jing1;  ZHU Yuchen2;  TANG Xianbo3
(1. School of Business Administration, Southwestern University of Finance and Economics,Chengdu 611130, Sichuan, China; 2. Faculty of Administration, City University of Macau, Macau 999078, China; 3. Faculty of Arts and Social Sciences,University of Sydney, Sydney 2006, NSW, Australia)
Keywords:
“data element ×; ”;  commodity;  digital economy;  risk supervision;  machine learning
PACS:
F49
DOI:
-
Abstract:
With the advent of the era of digital economy, the commodity market faces risks such as significant price fluctuations and supply chain uncertainties. To examine the commodity price risk supervision challenges in the context of “data element ×”, this paper analyzes the pivotal role and policy measures concerning commodities, the influential force of “data element ×” on the commodity market, the challenges and prospects related to commodity trading risks, and the pressing need for commodity price risk supervision. The study reveals that emerging technologies such as data analysis and artificial intelligence are reshaping China's commodity market entirely. Commodities encounter four central issues: data collection, requirements for price data labeling, construction of knowledge maps, and dynamic risk alerts. Research suggests leveraging deep learning for gathering multi-source heterogeneous data, employing knowledge element indexing and integration technology to establish data labels, utilizing data mining for knowledge map construction, and implementing hierarchical calibration to establish a dynamic risk alert system. These measures aim to enhance the risk response capabilities of investors and decision-makers in commodity markets.

References:

[1] CECCHETTI S G,BARSKY R,BERNANKE B,et al.Prices during the Great Depression:was the deflation of 1930 to 1932 unanticipated?[J].National bureau of economic research,1989(11):3174.
[2]BEASLEY A B.Overview:the oil shocks of the 1970s[EB/OL].(2016-05-15)[2024-01-22].https://energyhistory.yale.edu/the-oil-shocks-of-the-1970s/.
[3]MONASTEROLO I.Climate change and the financial system[J].Annual review of resource economics,2020(12):299-320.
[4]SCHOLES M S.Crisis and risk management[J].American economic review,2000(2):17-21.
[5]FRANK C.The financial crisis of 2008 and its impact on the Asian Rice Export Market[J].Journal of advances in economics and finance,2017(2):1-20.
[6]DEYUAN Z,ZHAO C,HU J Y.Impact of geopolitical risk on the volatility of natural resource commodity futures prices in China[J].Resources policy,2023(83):1-14.
[7]KUMAR A N V.Commodity derivative:a viable option for price risk management[EB/OL].(2015-12-04)[2023-12-20].http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/1984.
[8]BESSEMBINDER H,LEMMON M.Equilibrium pricing and optimal hedging in electricity forward markets[J].The journal of finance,2002(57):1347-1382.
[9]CARTER D A,ROGERS D A,SIMKINS,et al.A review of the literature on commodity risk management[J].Journal of commodity markets,2017(8):1-17.
[10]MIRANDA P,JORGE C,PESCATORI A,et al.Monetary policy transmission through commodity prices[J].International monetary fund,2023(215):1-42.
[11]ZHENG D,ZHAO C,HU J.Impact of geopolitical risk on the volatility of natural resource commodity futures prices in China[J].Resources policy,2023(83):1-14.
[12]ROBERT H.The impact of economics on environmental policy[J].Journal of environmental economics and management,2000(3):375-399.
[13]SANTOS D A,MARQUES L.Big data analytics for supply chain risk management:research opportunities at process crossroads[J].Business process management journal,2000(4):1117-1145.
[14]RAMAKRISHNAN S,BUTT S,CHOHAN M A,et al.Forecasting Malaysian exchange rate using machine learning techniques based on commodities prices[EB/OL].(2017-07-01)[2023-12-20].https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8002544&tag=1.
[15]CHEN Z,GOH H S,SIN K,et al.Automated agriculture commodity price prediction system with machine learning techniques[J].Technology and engineering systems,2021(4):376-384.
[16]HU G,LIU S,WU G,HU P,et al. Economic policy uncertainty,geopolitical risks,and the heterogeneity of commodity price fluctuations in China——an empirical study based on TVP-SV-VAR model[J].Resources policy,2023(85):1-12.
[17]邱中行,徐小芳.地缘政治风险对大宗商品期货价格的影响研究[J].财富涌现与流转,2023,13(3):15-25.
[18]SHANG R A,CHEN Y C,LIAO H J.The value of participation in virtual consumer communities on brand loyalty[J].Internet research,2006(4):398-418.
[19]MARO F,SAPORITO Y F.Forecasting the term structure of commodities future prices using machine learning[J].Digital finance,2023(1):57-90.
[20]付星星,张军,秦岩,等.基于图深度学习的大宗商品价格预测研究[J].计算机应用与软件,2021(7):60-66.
[21]杨英晖.基于大宗商品价格指数的PPI预测分析研究——传统与机器学习预测方法的构建与比较[J].投资与合作,2023(6):202-204.
[22]许珠路,王兴芬,刘亚辉.融合CNN-BiLSTM-Attention的集成学习价格预测[J].计算机系统应用,2023(6):32-41.
[23]ATANU G,PUNDIT M.Economic growth in China and its impact on international commodity prices[J].International journal of finance & economics,2021(2):2776-2789.
[24]廖杉杉,鲁钊阳.农产品价格风险的成因及规避机制研究[J].农村经济,2013(3):27-30.
[25]黄莉群,官心果,钟宇.数字经济时代的数据安全研究——以金融行业为例[J].商业经济,2024(2):174-179.
[26]ATHIQUE A.Integrated commodities in the digital economy[J].Media,culture & society,2020,42(4):554-570.
[27]郭金涛.大宗商品贸易融资模式创新与风险防控[EB/OL].(2023-09-04)[2024-01-22].https://ojs.s-p.sg/index.php/fm/article/view/13734.
[28]TOMEK W G.Commodity futures prices as forecasts[J].Applied economic perspectives and policy,1997(1):23-44.
[29]GOLDFARB A.TUCKER C.Digital economics[J].Journal of economic literature,2019(1):3-43.
[30]CHEN Z,LIU Z,TEKA,H,et al.Smart money in China's A share market:evidence from big data[J].Research in international business & finance,2022(4):1-15.
[31]陈国青,曾大军,卫强,等.大数据环境下的决策范式转变与使能创新[J].管理世界,2020(2):95-105,220.
[32]MAURO A D,GRECO M,GRIMALDI M.A formal definition of big data based on its essential features[J].Library review,2016(3):122-135.
[33]顾冰清.大数据背景下人工智能在大宗商品期货高频套利中的应用[J].中国集体经济,2024(2):174-176.
[34]LI D F,LI Z R,SUN K.Development of a novel soft sensor with long short-term memory network and normalized mutual information feature selection[J].Mathematical problems in engineering,2020(11):1-11.
[35]BAKAS D,TRIANTAFYLLOU A.Commodity price volatility and the economic uncertainty of pandemics[J].Economics-letter,2020(8):1-14.
[36]张威波,胡艳英.基于高频数据的豆类期货套利实证研究[J].时代金融,2018(6):155-156,158.
[37]WU Y,ROSEA M,LILLICRAP T.Deep compressed sensing[EB/OL].(2019-05-18)[2023-12-22].http://proceedings.mlr.press/v97/wu19d.html.
[38]TEOH T T,RONG Z.Python for data analysis[M].Berlin:Springer,2022.
[39]BALTRUSAITIS T,AHUJA C,MORENCY L P.Multimodal machine learning:a survey and taxonomy[J].IEEE Transactions on pattern analysis and machine intelligence,2019(2):423-443.
[40]JELODAR H,WANG Y,YUAN C,et al.Latent dirichlet allocation(LDA)and topic modeling:models,applications,a survey[J].Multimedia tools & applications,2019(78):15169-15211.
[41]王保加,潘海为,谢晓芹,等.基于多模态特征的医学图像聚类方法[J].计算机科学与探索,2018(3):1-12.

Memo

Memo:
-
Last Update: 2024-04-20