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Title:	碳信用抵換期貨發展及交易之研究 Research on the Development and Trading of Carbon Offset Futures
Authors:	陳彥惇 Chen, Yen-Tun
Contributors:	林士貴黃炳鈞 Lin, Shih-Kuei Huang, Bing-Jing 陳彥惇 Chen, Yen-Tun
Keywords:	碳權自願性碳權市場碳信用抵換期貨機器學習長短記憶模型 voluntary carbon market carbon offset futures machine learning LSTM
Date:	2024
Issue Date:	2024-02-01 11:31:48 (UTC+8)
Abstract:	2021年舉辦的COP26，各締約國簽署格拉斯哥氣候協議（Glasgow Climate Pact），同意全球必須採取更積極作為，達成2050年淨零碳排，以守住升溫臨界值攝氏 1.5 度之目標。若要實現淨零碳排的目標，強制性市場工具（如各國政府實施的碳排放總量管制、碳費）及自願性市場工具（如碳信用抵換機制）皆扮演重要角色。碳信用抵換期貨市場近年發展快速，以CME所上市之GEO及N-GEO期貨最為成功；本研究發現，CME碳信用抵換期貨在不同到期日之契約間具有正價差之特性，且該特性不因價格走跌而消失；此外，未平倉量及交易需求主要皆集中在當年度12月份到期之契約，此現象應與歐、美碳排放配額市場之法遵期限有關。實證部分，本論文透過RNN機器學習模型－LSTM，進行GEO及N-GEO期貨價格之預測，發現除碳信用抵換商品外，加入碳排放配額資產（EUA期貨及RGGI期貨）及能源類資產（天然氣期貨及電力期貨）對GEO及N-GEO期貨價格皆有良好的預測效果。 In 2021, during the COP26 conference, participating countries signed the Glasgow Climate Pact. They agreed that global efforts must be more proactive to achieve net-zero carbon emissions by 2050, aiming to keep the global temperature rise below the critical threshold of 1.5 degrees Celsius. To achieve the goal of net-zero carbon emissions, both compliance market tools (such as cap-and-trade mechanism implemented by governments) and voluntary market tools (such as carbon credit offset) play important roles. In recent years, the carbon offset futures market has rapidly developed, with the GEO and N-GEO futures listed on the CME proving to be particularly successful. This study found that CME carbon offset futures exhibit contango between contracts with different expiration periods, and this characteristic persists even when prices decline. Furthermore, open interest and trading demand are primarily concentrated in contracts expiring in December of the current year, a phenomenon likely related to compliance deadlines in the European and American carbon emission markets. In the empirical part of the study, the paper uses the RNN machine learning model – LSTM to predict the prices of GEO and N-GEO futures. The findings indicate that, apart from carbon credit offset itself, adding carbon emission allowance assets (EUA & RGGI futures) and energy-related assets (Nature Gas & Electricity futures) can demonstrate good predictive capabilities for GEO and N-GEO futures prices.
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Description:	碩士國立政治大學國際金融碩士學位學程 111ZB1020
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111ZB1020
Data Type:	thesis
Appears in Collections:	[Master’s Program in Global Banking and Finance] Theses

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