農業和能源產業在生物學和經濟上都是密切相關的。本文討論了價格及波動率之間的關係和相互作用，並特別關注於這兩個產業的共同風險溢出效果。本文的主要重點是：能源和農業市場間資產的相互作用，以及共同風險溢出效果，或是當某個資產的報酬發生衝擊後，與另一個資產隨後的波動或是兩者間共同波動的延遲效應。雖然已經有許多關於生質能源和生質能源的相關作物的研究，但以前的研究大部分都只是單純尋求商品價格之間的關係。僅有少數發表的論文關注風險溢出效果，在理論和實證方面也仍存在許多不足或錯誤，需要更進一步的研究。本文不僅考慮了期貨這種被廣泛使用的避險工具，也還考慮了指數股票型基金，一種有趣且相對較新的避險工具。當投資者在管理投資組合時，指數型股票基金可以被視為一種指數期貨，因此亦可以計算最佳動態避險比率，這也是一種對估計和測試共同風險溢出效果非常有幫助的應用方式。多變量GARCH模型常被用來分析風險溢出效果，本文在實證分析中，使用了其中的對角BEKK模型來估計並比較共同風險溢出的模式，更提供了一種分析和描述共同風險溢出效果的新方法，這對未來對估計和測試共同風險溢出效果的實證分析應該是有用的。

The agricultural and energy industries are closely related, both biologically and financially. The paper discusses the relationship and the interactions on price and volatility, with special focus on the covolatility spillover effects for these two industries. The interaction and covolatility spillovers, or the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset, between the energy and agricultural industries is the primary emphasis of the paper. Although there has already been significant research on biofuel and biofuel-related crops, much of the previous research has sought to find a relationship among commodity prices. Only a few published papers have been concerned with volatility spillovers. However, it must be emphasized that there have been numerous technical errors in the theoretical and empirical research, which needs to be corrected. The paper not only considers futures prices as a widely-used hedging instrument, but also takes an interesting new hedging instrument, ETF, into account. ETF is regarded as index futures when investors manage their portfolios, so it is possible to calculate an optimal dynamic hedging ratio. This is a very useful and interesting application for the estimation and testing of volatility spillovers. In the empirical analysis, multivariate conditional volatility diagonal BEKK models are estimated for comparing patterns of covolatility spillovers. The paper provides a new way of analyzing and describing the patterns of covolatility spillovers, which should be useful for the future empirical analysis of estimating and testing covolatility spillover effects.

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