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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/30021

    Title: 以FIGARCH模型估計長期利率期貨風險值
    Modeling Daily Value-at-Risk for Long-term Interest Rate Futures Using FIGARCH Models
    Authors: 吳秉宗
    Contributors: 謝淑貞
    Keywords: 長期記憶性
    Long Memory
    Interest rate futures
    Date: 2004
    Issue Date: 2009-09-11 17:04:40 (UTC+8)
    Abstract: 近幾年,風險值已經成為金融機構風險控管的重要工具。它的明確及簡單易懂是其讓人接受的原因,加上巴塞爾銀行監理委員會在1996提出的巴塞爾協定修正,規定銀行將市場風險因素納入考量,並允許銀行自行發展內部模型,以風險值模型衡量市場風險後,各種風險值的估算方法相繼被提出。
    本篇論文是使用部分整合自回歸條件變異數(Fractional Integrated Generalized Autoregressive Conditional Heteroskedasticity,簡稱FIGARCH)計算長期利率期貨多空部位的每日風險值。選取的三支長期利率期貨是在芝加哥期貨交易所掛牌的三十年期美國政府債券期貨(TB)、十年期美國政府債券期貨(TN)
    FIGARCH模型係由Baillie、Bollerslev與Mikkelsen於1996所提出,與傳統GARCH模型所不同的是,FIGARCH模型特別適用於描述具有波動性長期記憶(Long Memory)性質的資料。所謂長期記憶性,是指衝擊所造成的持續性是以緩慢的雙曲線速率衰退。而許多市場實證分析均指出,FIGARCH較適合用來描述金融市場上的波動性。此外,本研究的風險值計算,除了一般實務界常用的常態分配以外,還考慮了t分配與偏斜t分配,以捕捉財務資料常見的厚尾與偏斜的特性。
    而樣本外的風險值預測,則有不同的結果,當α=0.05,t分配與偏斜t分配FIGARCH(1,d,1)模型表現較佳;而α=0.01時,常態分配FIGARCH(1,d,1)模型表現較佳。而且t分配與偏斜t分配FIGARCH(1,d,1)模型在α=0.01會出現太過保守的情形,出現失敗率(failure rate)為零,高估風險值。
    Value-at-Risk (VaR) has become the standard measure used to quantify market risk recently, and it is defined as the maximum expected loss in the value of an asset or portfolio, for a given probability α at a determined time period. This article uses the FIGARCH(1,d,1) models to calculate daily VaR for long-term interest rate futures returns for long and short trading positions based on the normal, the Student-t, and the skewed Student-t error distributions. The U.S. Treasury bonds futures, Treasury notes futures, and municipal notes index futures of daily frequency are studied.
    The empirical results show that returns series for three interest rate futures all have long memory in volatility, and should be modeled using fractional integrated models. Besides, the in-sample and out-of-sample VaR values generated using FIGARCH(1,d,1) models are more accurate than those generated using traditional GARCH(1,1) models. For different distributions among FIGARCH(1,d,1) models, the normal FIGARCH(1,d,1) models are preferred for in-sample VaR computing whenα=0.05, and the Student-t and skewed Student-t models perform better for in-sample VaR computing whenα=0.025-0.0025. Nonetheless, for out-of-sample VaR, the Student-t and skewed Student-t FIGARCH(1,d,1) models perform better in the case α=0.05 while the normal FIGARCH(1,d,1) models perform better in the case α=0.01. The VaR values obtained by the Student-t and skewed Student-t FIGARCH(1,d,1) models are too conservative whenα=0.01.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0091351014
    Data Type: thesis
    Appears in Collections:[國際經營與貿易學系 ] 學位論文

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