臺灣博碩士論文加值系統

在期貨交易的發展上，農產品為早期即上市交易之期貨商品，近年來因環境氣候變化，再生能源政策影響以及金融重大事件之影響，使農產品價格波動快速劇烈，加上大陸期貨交易市場發展迅速，成為世界上第二大農產品期貨交易市場，使投資人在交易農產品期貨上除了芝加哥期貨交易所之外，增添另一個選項，本文目的在探討跨市場相同商品是否存在套利避險之效益，同時研究期貨間避險的最適比率，研究樣本取自美國芝加哥交易所和大連期貨交易所之玉米期貨日資料，期間為2003年1月28日至2017年12月22日，合計交易筆數為2881筆，以及芝加哥交易所和大連期貨交易所之大豆期貨日資料，期間為2004年9月22日至2017年12月22日，合計交易筆數為2650筆，檢定模型採用單根檢定與ARCH檢定進行測檢，最後採用GARCH模型與DCC-GARCH模型做實證分析。
本文利用芝加哥期貨交易所之大豆期貨與玉米期貨，和大連期貨交易所之大豆期貨與玉米期貨進行檢定分析，因兩者皆為交易活絡之市場，預期商品之流動性與價格波動度皆適合展開配對套利交易，且上述兩項商品交易結算合約月份相似，皆採用實物交割方式，可避免因交割方式不同，造成強制平倉事宜，另美國農產品期貨市場因自然人交易居多，所以價格波動劇烈，風險較大，大陸農產品期貨交易市場以法人機構參與居多，價格波動較平緩，因此投資人可透過此特性商品進行避險策略，以提高交易獲利並降低投資風險。

The purpose of this paper is to explore the effectiveness of arbitrage hedging for the same commodities cross markets and to study the optimal ratio of hedging between futures. The sample of the study was taken from Corn Futures Day data of the Chicago Board of Trade and the Dalian Futures Exchange during the period from January 28, 2003 to December 22, 2017 and the Chicago Board of Trade and Dalian Futures Exchange soybean futures day information, the period of September 22, 2004 to December 22, 2017.The test model uses a GARCH model and DCC-GARCH model for empirical analysis.
This article uses the Chicago Board of the soybean futures and corn futures, and Dalian Futures Exchange, soybean futures and corn futures verification analysis, the expected liquidity and price volatility of goods are suitable for expansion Paired arbitrage transactions, and the two commodity trading settlement contract month are similar, are based on physical delivery, to avoid due to different delivery modes, resulting in forced liquidation, and the other of the United States agricultural futures market due to the majority of natural person transactions, the price volatility, the risk Larger, the mainland agricultural futures market to participate in the body of the majority of corporate bodies.

第一章　緒 論 1
第一節 農產品期貨市場交易背景討論 1
第二節 研究農產品跨市場是否有避險空間 7
第三節 研究流程 9
第四節 研究架構 11
第二章　文獻回顧 12
第一節　氣候變化與再生能源政策之影響 12
第二節　跨商品與跨市場套利之討論 16
第三節　基差風險與波動率影響之探討 21
第三章　研究方法與實證模型 24
第一節　變數操作性定義 24
第二節　單根檢定 26
第三節　ARCH效果檢定 29
第四節　GARCH模型與DCC-GARCH模型設定 31
第四章 資料來源與處理 36
第一節　資料來源說明處理 36
第二節　基本敘述統計說明 38
第五章　實證結果 42
第一節　單根分析 42
第二節　實證結果 44
第六章　結論 52
參考文獻 54
表　目　錄
【表1-1-1】芝加哥期貨交易所農產品期貨規格表 3
【表1-1-2】大連期貨交易所農產品期貨規格表 3
【表4-2-1】大豆期貨基本敘述統計量表 39
【表4-2-2】玉米期貨基本敘述統計量表 41
【表5-1-1】大豆期貨單根分析表 43
【表5-1-2】玉米期貨單根分析表 44
【表5-2-1】大豆期貨DCC-GARCH實證表 49
【表5-2-2】玉米期貨DCC-GARCH實證表 50

圖　目　錄
【圖1-1-1】芝加哥期貨交易所農產品期貨2004年至2016年價格走勢圖 4
【圖1-4-1】研究架構之流程圖設置 11
【圖5-2-1】大豆2004年至2017年相關係數走勢圖 51
【圖5-2-2】玉米2003年至2015年相關係數走勢圖 51

大連期貨交易所網站: http://www.dce.com.cn/。
芝加哥期貨交易所網站 : http://www.cmegroup.com/。
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