Title page for etd-0708111-175020
 論文名稱Title 厚尾模型對商品期貨市場波動性預測能力的比較 Forecasting Volatility for commodity futures using fat-tailed model 系所名稱Department 財務管理學系Department of Finance 畢業學年期Year, semester 99 學年度 第 2 學期The spring semester of Academic Year 99 語文別Language 英文English 學位類別Degree 碩士Master 頁數Number of pages 57 研究生Author 柯珮如Pei-ru Ke 指導教授Advisor 召集委員Convenor 口試委員Advisory Committee 口試日期Date of Exam 2011-06-28 繳交日期Date of Submission 2011-07-08 關鍵字Keywords 波動預測、厚尾、高狹峰、偏斜一般化誤差分配leptokurtic, fat-tailed, volatility forecast, SGED 統計Statistics 本論文已被瀏覽 5871 次，被下載 0 次The thesis/dissertation has been browsed 5871 times, has been downloaded 0 times.
 中文摘要 本篇論文考慮了高階動差性質，使用偏斜一般化誤差分配(SGED)來解釋高峰、厚尾與偏態(skewness)的金融市場資料形態，與一般常用常態分配、Student-t分配與一般化誤差分配(GED)等對稱分配進行模型績效比較，探討商品報酬率普遍存在高峰、厚尾現象時，何種分配的模型對於波動率具有較佳的相對預測能力。本文的實證分析研究步驟如下：首先，對資料進行敘述性統計，得知應加入GARCH效果，接著透過階次篩選出最佳階次。再來，對資料做全樣本的參數估計，選出最佳模型。最後，進行樣本外估計後，分別做出1天、2天、5天、10天、20天的波動性預測，並採用不同的損失方程式評估預測的績效，來決定最佳模型的選取。再者，使用DM檢定來呈現在不同誤差分配下的模型之間的相對預測能力比較。 Abstract This paper considers the high-moments and uses the skew generalized error distribution (SGED) to explain the financial market data which have leptokurtic, fat-tailed and skewness. And we compare performance with the commonly used symmetrical distribution model such as normal distribution, student’s t distribution and generalized error distribution (GED). To research when returns of asset have leptokurtic and fat-tailed phenomena, what model has better predictive power for volatility forecasting?The empirical procedure is as follows: First step, make the descriptive statistics of raw data, and know that the GARCH effect should be considered, followed by selecting the optimal order of ARMA-GARCH. The second steps, make the parameter estimations of full-sample, and pick up the best model. Finally, forecast out-of-sample volatility for 1-day, 2-day, 5-day, 10-day and 20-day respectively, not only use different loss function to measure the performance, but also use DM test to compare the relative predictive power of the models under the different error distribution.
 目次 Table of Contents 論文審定書 i誌　謝 ii摘　要 iiiAbstract iv1. Introduction 11.1 Motivations 11.2 Importance of metal, oil and agricultural product markets 22. Literature Review 73. Methodology 123.1 Time-series forecasting 123.2 Student’s t Distribution 133.3 General Error Distribution 133.4 Skew Generalized Error Distribution 143.5 Forecasting methodology 154. Empirical results 194.1 Data and descriptive statistics 194.2 Order selection of ARMA-GARCH 234.3 Full sample estimation 284.4 Out-of-sample forecast evaluation 325. Conclusions 46References 48
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