博碩士論文 etd-0530112-102008 詳細資訊


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姓名 吳佳穎 (Chia-ying Wu) 電子郵件信箱 E-mail 資料不公開
畢業系所 財務管理學系研究所(Finance)
畢業學位 碩士(Master) 畢業時期 100學年第2學期
論文名稱(中) 原油期貨報酬之波動性預測─常態混合模型與NIG混合模型之應用   
論文名稱(英) Volatility Forecasting of Crude Oil Future-Under Normal Mixture Model and NIG Mixture Model
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    摘要(中) 本研究嘗試以常態混合GARCH模型( Normal Mixture GARCH Model)及NIG混合GARCH模型(Normal-inverse Gaissian Mixture GARCH Model)捕捉商品期貨市場之波動度行為。常態混合GARCH模型(以下簡稱為NM-GARCH模型)為一由二至數個常態分配以特定權重(mixing law)混合而成之模型,其變異數符合GARCH過程。NM-GARCH對於大部分具有高峰、厚尾現象之財務資料捕捉能力應較一般常態GARCH模型和Student’s t GARCH模型為佳。另外,NM-GARCH中權重較低之組成成分之變異數通常較高,而權重較高之組成成分波動度較小,說明了現實經濟情況中,大波動(shock)發生機率較小,小波動發生機率較高的現象,即在一般情況中不斷發生之波動幅度較平緩,較大的衝擊雖然幅度大,但較少發生。
    NIG混合分配為一由兩個或以上組成成份加權平均而成的混合分配,而其任一個組成成份均符合NIG分配。相較於常態混合分配,NIG混合分配將NIG各項優點納入考量,不但解釋了高峰、資料之離散程度,又因NIG分配之雙尾下降速度較常態分配緩慢,NIG混合分配應能更完整的解釋資料之厚尾現象。
    本研究將常態混合模型及NIG混合模型應用於原油期貨市場報酬率波動性預測,並經由參數估計、預測、損失函數及統計顯著性檢定,得以推論對原油期貨市場報酬率¬而言,此二種模型相較於其它波動度模型配適度及預測能力均顯著較佳。
    摘要(英) This study attempts to capture the behavior of volatility in the commodity futures market by importing the normal mixture GARCH Model and the NIG mixture GARCH model (Normal-inverse Gaussian Mixture GARCH Model). Normal mixture GARCH Model (what follows called NM-GARCH Model) is a model mixed by two to several normal distributions with a specific weight portfolio, and its variance abide by GAECH process. The ability of capturing the financial data with leptokurtosis and fat-tail of NM-GARCH Model is better than Normal GARCH Model and Student’s t GARCH Model.。Also,The Variance of the factor with lower weight in NM-GARCH Model usually higher, and the volatility of the factor with higher weight is lower, which explains the situation happens in the real market that the probability of large fluctuations (shocks) is small, and the probability of small fluctuations are higher. Generally, the volatilities which keeping occurring in common cases are respectively flat, and the shocks usually bring large impacts but less frequent.
    NIG Mixture Distribution is a distribution mixed by two to several weighted distributions, and the distribution of every factor abides by NIG Distribution. Compare to Normal Mixture Distribution, NIG Mixture Distribution takes the advantages of NIG Distribution into account, which can not only explain leptokurtosis and the deviation of data, but describe the fat-tail phenomenon more complete as well, because of the both tails of NIG Distribution decreasing slowly.
    This study will apply the NM GARCH Model and NIG GARCH Model to the Volatility forecasting of the return rates in the crude oil futures market, and infer the predictive abilities of this two kinds of models are significantly better than other volatility model by implementing parameter estimation, forecasting, loss function and statistic significant test.
    關鍵字(中)
  • 波動性預測
  • 原油期貨報酬
  • 關鍵字(英)
  • Normal Mixture
  • NIG Mixture
  • GARCH
  • Volatility forecasting
  • 論文目次 Contents
    Abstract ii
    Chapter 1 Introduction 1
    1.1 Background 1
    1.2 Purpose of Research, Process and Structure 4
    Chapter 2 Literature Review 7
    2.1 Volatility Models and The Empirical Results 7
    Chapter 3 Methodology 15
    3.1 The Characteristics and Definitions of Each Probability Density Function 15
    3.2 Data Analysis 23
    3.3 Model Selection 34
    Chapter 4 Empirical Research 38
    4.1 Research Process and Structure 38
    4.2 Parameters Estimation 44
    4.3 The Size of Volatility and Probability 47
    4.4 Forecasting and Performance Evaluation 50
    Chapter 5 Conclusion and Recommendations 54
    Reference 57
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    口試委員
  • 陳明吉 - 召集委員
  • 李健強 - 委員
  • 王昭文 - 指導教授
  • 黃振聰 - 指導教授
  • 口試日期 2011-07-11 繳交日期 2012-05-30

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