本研究首先闡述氣體排放權交易由來及其理論依據，並探討國外有毒氣體排放管制制度的沿革，及全球主要氣體排放權相關金融商品市場概況，最後並蒐集氣體排放權相關金融商品評價的相關文獻，並以京都議定書承諾期第一階段(試行階段)歐洲溫室氣體排放權(European Union Allowances, EUAs)三大主要現貨市場Powernext、EEX、Nordpool為實證研究對象，探討其現貨價格日報酬率序列，配適性最佳及預測能力最好之條件均數、變異數模型。本研究假設其報酬率序列服從ARMA均數方程式模型，分別比較GARCH、GJR-GARCH、EGARCH等三種GARCH 變異數方程式模型，搭配誤差項服從Gaussian分配或Student's t 分配的假設，先分別分析三大現貨市場價格日報酬率序列ARMA均數方程式模型最適落後期數，再分別搭配三種GARCH模型及不同的誤差項分配型態的評估其模型最適落後期數，並驗證其在三大現貨市場價格日報酬率序列，樣本內(in-sample) 資料何種模型的配適能力較佳，最後利用移動視窗（rolling window）的方法比較在不同預測期間下，探討樣本外（out of sample）資料何種模型的預測能力較佳。實證結果顯示，歐洲溫室氣體排放權三大主要現貨市場價格報酬率序列，GJR-GARCH與EGARCH條件變異數模型在樣本內資料的配適能力，與樣本外資料的預測能力上，較GARCH模型相對較佳，符合三大主要現貨市場價格報酬率序列，存在有條件波動的不對稱性的假設，此外，本研究也發現Student's t分配在樣本內資料配適度相對較Gaussian分配佳，但在樣本外資料預測能力方面卻相反，反而以Gaussian分配較佳，因此，在本研究中顯示模型的配適度與預測能力未必是一致的結果。

In this paper, the first expounded on gas emissions trading origin and theoretical basis, and explore abroad toxic gas emission control system has evolved, and major global gas emissions related financial products market, Finally, the right to collect gas emissions related to the evaluation of financial products related literature, and the Kyoto Protocol commitment to the first phase (pilot stage) European GHG emissions allowance EUAs(European Union Allowances, EUAs) three major spot markets Powernext, EEX and Nordpool to empirical study to discuss which model that in-the-sample fitted ability, and the out-of-sample predictive ability performs better. The assumption returns of EUAs obeyed the ARMA mean equation models, and combined with GARCH, GJR-GARCH, EGARCH three GARCH conditional variance model, with the assumption of GARCH residual in Gaussian distribution or Student's t distribution. Frist, respectively analysis the EUAs three major spot markets return sequences, how many lag stages of the ARMA model is the optimal formula, then, respectively mix of three different GARCH models and different distribution of GARCH models’s residual to assess the best lag stages of models, and to verify in the EUAs three major spot markets return sequences, what kind of model fitted ability of in-the-sample data is more suitable than others, finally using the “rolling window” method comparison under different forecasting horizons, discusses EUAs out-of-sample returns data, what kind of model predictive ability of out-of-sample is more appropriate than others. The empirical results show that the EUAs three main spot market price return sequence, GJR-GARCH and EGARCH condition variance model no matter in-the-sample fitted ability, or the out-of-sample predictive ability are relatively better than GARCH model, this may be attributed that the EUAs three main spot market price return sequence, the existence of a conditional asymmetric volatility assumption. In addition, this study also found that the EUAs three main spot market price return sequence, in-the-sample fitted ability, the assumption of GARCH residual in student's t distribution is more suitable than Gaussian distribution, but the out-of-sample predictive ability, the assumption of GARCH residual in Gaussian distribution is more appropriate than student's t distribution , therefore, in this study shows that the model fitted ability with the predictive ability is not necessarily consistent results.

本研究由於參考文獻、資料眾多，難免有所疏漏，如有引證錯誤或是遺漏的部份，煩請不吝指正，謝謝。
Because of this study refer to many documents, information, it is inevitable omissions, if any errors or omissions cited part, I beg you not hesitate to correct me, thank you.
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