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| 研究生： |
邱天禹 Chiu, Tien-Yu |
|---|---|
| 論文名稱： |
布蘭特原油期貨的波動率－以馬可夫移轉模型分析 Regime-switched volatility of Brent crude oil futures using Markov-switching ARCH model |
| 指導教授： |
謝淑貞
Shieh, Shwu-Jane |
| 學位類別： |
碩士
Master |
| 系所名稱： |
商學院 - 國際經營與貿易學系 Department of International Business |
| 論文出版年： | 2006 |
| 畢業學年度： | 94 |
| 語文別： | 英文 |
| 論文頁數： | 28 |
| 中文關鍵詞： | 馬可夫轉換 、波動率 、布蘭特原油 |
| 外文關鍵詞： | Markov-switching ARCH, SWARCH, Brent crude oil |
| 相關次數： | 點閱：105 下載：51 |
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本篇論文使用SWARCH模型探討布蘭特原油期貨市場的波動性。SWARCH模型將條件變異設定為可隨時間變動而改變，甚至移轉到不同的區間上。實證結果顯示SWARCH (3,3)模型具有最佳配適度與最準確的預測能力。樣本在不同區間下的平滑機率的估計值有助於補捉資料特性，而且當樣本落在高波動率區間上時會對應著重大事件的發生，如1990年波斯灣戰爭、1997年亞洲金融風暴與2001年的911恐怖攻擊。
This paper investigates the volatility of the Brent crude oil futures markets using Markov-switching ARCH (SWARCH) model. The SWARCH model allows the conditional disturbances to change as time passes and even to switch in different regimes. The empirical evidence shows that the SWARCH (3,3) model performs the best goodness of fit and the best forecast performance between different fitting models. The estimation of smoothing probabilities of data under different regimes facilitates to capture the characteristics of data, and the high-volatility regime is associated with the extraordinary events, such as the 1990’s Persian Gulf War, the 1997’s Asia Financial Crisis, and the 2001’s 911 terrorist attack.
1. Introduction 1
2. Methodology 5
2-1 Markov-switching ARCH (SWARCH) model 5
2-1-1 Markov chain and SWARCH model 5
2-1-2 Filter probability and smoothing probability 7
2-2 Maxima likelihood estimator 9
2-3 Forecast performance 9
3. Data and empirical result 10
3-1 Data 10
3-2 Statistic characteristics of data 12
3-3 Empirical results 13
3-3-1 Statistic fit comparisons for various specifications of different models 13
3-3-2 The forecast performance of different models 14
3-3-3 The estimation model and the probability under different regimes 14
4. Conclusions 16
Appendix 18
Reference 18
Table 1 Descriptive Statistics of 20
Table 2 ADF and PP Test 21
Table 3 Statistic fit comparisons for various specifications of different models 22
Table 4 Forecast performance of different models 23
Figure 1 Daily closing price series and daily log-return series 24
Figure 2 Q-Q plot against Normal and Student-t(7) 25
Figure 3 ACF and PACF of log-return 26
Figure 4 ACF and PACF of square of log-return 27
Figure 5 Smoothing probability under different regimes 28
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