近年來，隨著金融市場規模擴大，眾多新金融商品的推出，金融機構不斷擴充其業務範圍，使得風險管理越來越重要。風險值的概念在1993年被三十人集團(G30)提出後，由於其概念是可以將風險予以量化，所以風險值也逐漸成為金融機構進行風險管控的重要工具，並為大家所普遍認同的新風險指標，各種不同估算風險值的方法也因此相繼地被提出。
本研究採用傳統計量方法及人工智慧模式來估計不同交易期間台灣發行量加權股價指數的風險值，以每日資料做為研究對象。在傳統計量方法上，使用結合部分整合自我迴歸移動平均(Fractional Integrated Autoregressive Moving Average, ARFIMA)及部分整合自我迴歸條件變異數(Fractional Integrated Generalized Autoregressive Conditional Heteroskedasticity, FIGARCH)兩個可捕捉到資產報酬率具有長期記憶特性的模型，在風險值的計算上不僅考量常態分配，同時也考量了t分配與偏斜t分配的情況，以捕捉資產報酬具有厚尾及偏斜的特性。再者，在人工智慧方法方面，本研究使用適應性類神經模糊推論系統(Adaptive Network Based Fuzzy Inference System, ANFIS)來預測下一日的波動率，以進一步估算及提高風險值衡量的績效。本研究之實證結果如下：
1.就抽樣期間而言，各抽樣期間表現較好的模型並不相同，由於指數報酬率的厚尾與高峰程度大小不一樣，造成並無一模型為最適模型。
2.就多頭部位風險值的整體績效來看，以ANFIS模型為相對較佳，ARFIMA(1,d,1)-FIGARCH(1,ξ,1)模型其次，最後為GARCH(1,1)模型。
3.就空頭方面風險值的整體績效來看，以ARFIMA(1,d,1)-FIGARCH(1,ξ,1)模型表現較佳，其次為ANFIS模型，最後為GARCH(1,1)模型。
4.整體而言，ANFIS模型在各期間相較之下皆比其他模型穩定，不易受資產報酬率序列厚尾與高狹峰或長期記憶特性的影響。

After 90’s several famous failed institutions which boomed up due to manipulate derivatives improperly, many countries in the world are paying attention to the risk management progressively. In the early 1990s, three events popularized VaR as a practical tool of risk management for financial institutions: In 1993, Group of Thirty published a groundbreaking report on derivatives practices. In 1994, JP Morgan launched its free RiskMetrics service. In 1995, the Basle Committee on Banking Supervision implemented market risk capital requirements for banks. As a consequence, VaR plays an important role in risk management.

The study is aimed at TSEC Weighted Stock Price Index in different trading periods. It uses different volatility forecasting models to improve the performance of VaR estimates, and discusses the predictability of different sample frequency and different volatility forecasting models. The study adopts ARFIMA-FIGARCH models to capture returns with long memory based on three kinds of density assumption including normal, Student-t, and skewed Student-t distributions. In addition, artificial intelligence methods regarding Adaptive Neuro-Fuzzy Inference System (ANFIS) is adopted to estimate Value at Risk. The empirical results of the study are summarized as the followings:

1.In the sampling periods, no models’ performance is superior to others due to different asset return’s distribution.
2.In terms of the overall performance of VaR model for the long trading positions, ANFIS models are the best. The second and successive models are ARFIMA(1,d,1)-FIGARCH(1,ξ,1), and GARCH(1,1).
3.In respect of the overall performance of VaR model for the short trading positions, ARFIMA(1,d,1)-FIGARCH(1,ξ,1) models are the best. The second and successive models are ANFIS, and GARCH(1,1).
4.ANFIS models are more stable than other models in each period. The asset returns’ fat-tailed and kurtosis or long memory property aren’t liable to influence ANFIS models.

中文部份
1.林孟迪 (2000)，「VaR風險管理之保守性、精確度與效率性研究」，私立淡江大學財務金融研究所未出版碩士論文。
2.林彥豪 (2002)，「長期記憶資料之風險值計算」，東吳大學商用數學系未出版碩士論文。
3.周大慶、沈大白、張大成、敬永康、柯瓊鳳 (2002)，「風險管理－風險值理論與應用」，智勝文化事業有限公司出版
4.王銘祥 (2003)，「結合真實波動性預測模型與準蒙地卡羅模擬法於風險值之研究」，國立台灣科技大學資訊管理系未出版碩士論文。
5.吳秉宗 (2004)，「以FIGARCH模型估計長期利率期貨風險值」，國立政治大學國際貿易研究所未出版碩士論文。
6.唐大倫 (2004)，「運用長期記憶模型於估計股票指數期貨之風險值」，國立政治大學國際貿易研究所未出版碩士論文。
7.張斐章、張麗秋、黃浩倫 (2004)，「類神經網路理論與實務」，東華書局出版社。

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