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博碩士論文 etd-0708111-175020 詳細資訊
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
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
57
研究生
Author
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
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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 iii
Abstract iv
1. Introduction 1
1.1 Motivations 1
1.2 Importance of metal, oil and agricultural product markets 2
2. Literature Review 7
3. Methodology 12
3.1 Time-series forecasting 12
3.2 Student’s t Distribution 13
3.3 General Error Distribution 13
3.4 Skew Generalized Error Distribution 14
3.5 Forecasting methodology 15
4. Empirical results 19
4.1 Data and descriptive statistics 19
4.2 Order selection of ARMA-GARCH 23
4.3 Full sample estimation 28
4.4 Out-of-sample forecast evaluation 32
5. Conclusions 46
References 48
參考文獻 References
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Young, S. K., Rachev, S. T., Bianchi, M. L., Mitov, I., & Fabozzi, F. J. (2010). “Time series analysis for financial market meltdowns”. Preprint submitted to Journal of Banking and Finance.
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