Bollerslev (1986)提出一般化自我回歸條件異質變異數模型，即GARCH模型。此模型雖然可捕捉波動叢聚(volatility clustering)現象，但Chan (2003)考慮跳躍行為變異數與共變異數，提出雙變量跳躍強度隨時間變動模型(correlated bivariate Poisson with GARCH, CBP-GARCH)，可以估計兩種資產共同跳躍強度隨時間變動的現象，捕捉兩資產價格不連續的變動及兩資產報酬的共變異數關係。
農產品價格波動受天氣、政策、市場供需等影響甚劇，而農產品價格與期貨價格可能皆會因稀少事件的發生而存在干擾的現象。本研究沿用Chan (2003)的CBP-GARCH模型探討2008年5月9日至2016年5月9日中國農產品期貨與現貨市場為主要目的，其次也研究其與基差風險的變化。主要研究大連商品交易所(Dalian Commodity Exchange，簡稱DCE)的農產品期貨與現貨，並對於樣本期間內全球所發生的事件進行子樣本期間農產品期現貨市場風險特性的分析，相關結果將提供市場參與者未來制定投資決策時，能有一完整參考的研究結果。選用交易規模連續5年位居大連商品交易所首位的豆粕期貨進行實證，結果顯示(1)兩報酬率皆存在著高度的波動叢聚現象，而期貨又較於現貨高；(2)現貨與期貨報酬存在共同跳躍行為，而其變動可能受到某些因素的干擾。

This paper uses Chan (2003) CBP-GARCH model to study jump dynamics in the return of agricultural commodity spot and futures. The traditional continuous and smooth models, like Bollerslev (1986) GARCH model, may fail to capture extreme returns volatility. The performance of CBP-GARCH model is better. CBP-GARCH model has high accuracy because it can capture dynamic jump process and jump correlation.
CBP-GARCH model shows that the character of volatility clustering of agricultural commodity in China. Moreover, we add basis in our model. 
We analyze soybean meal spot and futures in our study. Because Dalian Commodity Exchange occupies the one position in the soybean meal futures in China. In our study shows (1) futures causes greater volatility than spot. (2) The two volatility indices have the individual jump and the joint jump behavior.

目錄 V
圖目錄 VI 
表目錄 VII

第一章	緒  論	1
第一節	研究背景與動機	1
第二節	研究目的	4
第三節	研究架構	5
第二章	文獻回顧	7
第一節	避險（Hedging）相關文獻	7
第二節	現貨和期貨之基差(Basis)效應相關文獻	10
第三節	套利(Arbitrage)相關文獻	12
第三章	研究方法與實證模型	15
第一節	單根檢定	15
第二節	一般化自我迴歸異質條件變異數（GARCH）模型	18
第三節	CBP-GARCH 模型	20
第四章	實證結果分析	24
第一節	資料來源與處理	24
第二節	基本統計量	26
第三節	單根檢定與GARCH檢定	31
第四節	CBP-GARCH 模型實證結果分析	34
第五章	研究結論	39
參考文獻		40
附錄		46
豆粕期貨合約	46

圖目錄
【圖1-1- 1】全球農產品期貨選擇權交易量前十名圖	2
【圖1-1- 2】大連商品交易所農產品期貨收盤價時間序列圖	3
【圖1-4- 1】研究流程架構圖	6
【圖4-2- 1】大豆現貨報酬率	28
【圖4-2- 2】豆粕現貨報酬率	28
【圖4-2- 3】豆油現貨報酬率	28
【圖4-2- 4】玉米現貨報酬率	28
【圖4-2- 5】大豆1號期貨報酬率	28
【圖4-2- 6】大豆1號期貨報酬率 	28
【圖4-2- 7】豆粕期貨報酬率	28
【圖4-2- 8】豆油期貨報酬率	28
【圖4-2- 9】玉米期貨報酬率	29
【圖4-4- 1】豆粕現貨與期貨報酬共同跳躍強度	 37

表目錄
【表4-2- 1】基本統計量	27
【表4-2- 2】Q統計量與最大概似估計	30
【表4-3- 1】時間序列資料單根檢定	32
【表4-3- 2】時間序列資料的KSS單根檢定	33
【表4-4- 1】豆粕CBP-GARCH模型估計結果	35
【表4-4- 2】豆粕CBP-GARCH自我相關檢定與異質性檢定	36

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