本篇論文延伸Cao-Wei (2004) 方法之兩大主軸建構一評價氣候衍生性商品的理論模型。第一，採用Campbell and Diebold (2005) 發展出的時間序列模型來描述溫度的動態過程。這個模型的好處在於其不僅自增溫趨勢、季節性與自我相關捕捉到日均溫的條件均值特性，亦能對其條件變異的性質予以掌握。第二，本文延伸Cao and Wei的固定比率之風險趨避效用函數(CPRA)假設，改採廣義冪級數效用函數(Extended power utility function)。其優點是可自由呈現遞增、遞減或固定比率之風險趨避特性。最後本文發現氣溫衍生性商品的價格主要由天氣狀況、折現因子及遠期風險溢酬來決定。此外，造成這些價格變動的起因與風險趨避參數有很密切的關係。本文研究的結果與Cao and Wei的狹義設定下相比是一致的，因此本研究提供了更具一般性的氣溫衍生性商品之評價方法。

This paper extended the Cao-Wei (2004,JFM) model to construct a theoretical model for pricing weather derivatives in two significant ways. One is to adopt a time series model developed by Campbell and Diebold (2005, JASA) to describe the dynamic of temperature. The advantage of using Campbell and Diebold’s time series model to describe the temperature dynamics is that it can not only take the conditional mean of temperature coming from trend, seasonal, and cyclical components, but also allow for the conditional variance dynamics. The other purpose of this paper is to use an extended power utility function, instead of Cao and Wei’s constant proportional risk aversion (CPRA) utility function. The extended power utility function could exhibit decreasing, constant and increasing relative risk aversion. Eventually, we find that the prices of weather derivatives can be determined by weather conditions, discount factors, and forward premiums. Additionally, these sources have close relations with some risk aversion parameters. Furthermore, the results are consistent with Cao and Wei’s condition under some specific parameter assumptions.

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