時間序列分析法在近年來的金融財務領域中愈來愈受到重視，其中的GARCH模型更能有效地分析波動叢聚現象的訊息。然而在實際建模時，經濟金融資料常帶有強烈的不確定訊息；此外，所使用的樣本資料期間過長或是其中發生的變化過大皆會影響整體預測的準確度。因此，本文嘗試將GARCH模型結合模糊理論與轉折區間的概念，建構出更能附合實際情況的門檻型GARCH模式。接著，我們以模擬及實際的資料來驗證模型的預測準確性。

In recent years, the time series method has been got more and more attention in financial domain. In time series analytic method, the GARCH model could analyze effectively the information of volatility clustering. When the actual model set, the economical or financial resources are always having strongly unstable information that can not be confirmed. In addition, if the sample material period too long or the sample material changing too much. These will influence the accuracy of the forecasting. Therefore, in this article, we will attempt to base on the GARCH model with the advantage of change periods and the fuzzy theory, in order to build the GARCH model that can more close to the real situation. Then, we used the simulation data and real resources to test and verify the accuracy of model forecasting.

致謝 I
摘要 II
ABSTRACT III
目次 IV
圖目錄 V
表目錄 VII
第一章 前言 1
第二章 理論方法 4
2.1 ARCH與GARCH模型 4
2.2 K平均法 5
2.3 模糊理論 6
2.4 資訊量與熵 8
2.5 模糊熵分類法 11
2.6 最小樣本數決定演算法則 14
2.7 模糊門檻型GARCH模式建構步驟 16
第三章 模擬分析 17
第四章 實證分析與預測 48
4.1新台幣兌美元匯率 48
4.2 國際西德州原油價格 54
第五章 結論 60
參考文獻 61

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