本篇論文研用 Cao-Wei (2004) 之定價模型且將原在Cao-Wei定價模型中的氣溫模型以Campbell and Diebold (2005)之離散時間序列模型替代。Campbell and Diebold (2005)利用傅立葉級數所建構時間序列模型來描述氣溫的特性，此模型不僅考慮了日均溫的條件平均值特性，更將氣溫之條件變異數納入模型中。而Cao-Wei (2004)的均衡定價模型是考慮總股利變數與氣溫變數所產生的共同隨機過程對衍生性商品的風險溢酬的影響。此外，由於Gamma分配族涵蓋許多重要的分配，不論是特例、極限分配還是透過簡單的轉換，都可透過Gamma分配的轉換而得，為了讓模型更具一般性，本篇論文並不限制氣溫衝擊符合常態分配，而是根據氣溫模型的估計結果，利用Gamma轉換(Gamma transformation)來進行氣溫衝擊分配的設定。This paper extended the valuation model proposed by Cao-Wei (2004, JFM); furthermore, we substitute the discrete time series model proposed by Campbell and Diebold (2005) for the sine function model proposed by Cao and Wei. The Campbelland Diebold’s time series model describes the temperature characteristics by using a Fourier series. It can not only consider the conditional mean of temperature dynamicsbut also take into account the conditional variance dynamics. The Cao and Wei’s equilibrium pricing model consider a joint process of the aggregate dividend and thetemperature to discuss the significance of the market price of temperature risk.Besides, since the gamma class of distributions includes many important distributions, either as special or limiting cases or through simple transformation. Therefore, considering more general situation, this paper does not restrict the temperature disturbance to follow normal distribution. We set the distribution of the temperaturedisturbance by Gamma transformation according to the estimated result of temperature variable.
