§ Browsing ETD Metadata
  
System No. U0002-1607201219195700
Title (in Chinese) 動態價格跳躍與最小變異數避險組合的風險值-以西德州原油現貨與期貨價格為例
Title (in English) Dynamic price jump and value-at-risk for the minimum variance hedging portfolio: The case of the WTI crude oil spot and futures prices
Other Title
Institution 淡江大學
Department (in Chinese) 管理科學學系碩士班
Department (in English) Master’s Program, Department of Management Sciences
Other Division
Other Division Name
Other Department/Institution
Academic Year 100
Semester 2
PublicationYear 101
Author's name (in Chinese) 林哲宇
Author's name(in English) Che-Yu Lin
Student ID 699620299
Degree 碩士
Language Traditional Chinese
Other Language
Date of Oral Defense 2012-06-22
Pagination 32page
Committee Member advisor - 莊忠柱
co-advisor - 王譯賢
co-chair - 林忠機
co-chair - 蔡蒔銓
co-chair - 婁國仁
Keyword (inChinese) 期貨
風險值
最小變異數避險組合
價格跳躍
回顧測試
Keyword (in English) futures
value-at-risk
minimum variance hedging portfolio
price jump
backtesting
Other Keywords
Subject
Abstract (in Chinese)
近年來國際原油價格劇烈的波動,常導致投資人承受巨額損失,因而使原油期貨成為避險的金融商品之一。由於原油價格常因稀少事件,而產生價格不連續現象,本研究利用Chan(2003)的雙變量CBP-GARCH模型,估計最小變異數避險組合條件風險值,最後藉Kupiec(1995)的概似比檢定法與Christoffersen(1998)的條件涵蓋檢定法進行回顧測試,以評估風險值模型的準確性。
    研究發現,雙變量CBP-GARCH模型的最小變異數避險組合條件風險值模型通過回顧測試,而未避險模型與雙變量DCC-GARCH模型均未通過回顧測試。有鑑於此,雙變量CBP-GARCH模型的最小變異數避險組合條件風險值模型準確性較高,此乃因雙變量CBP-GARCH模型能捕捉跳躍動態過程與跳躍相關之故。因此若僅考慮資產價格間的動態波動性過程,會造成低估風險現象而容易使投資人承擔超過預期的損失,此結果可作為投資人避險的參考。
Abstract (in English)
International crude oil prices of volatility severely make investors bear huge loss in recent years. Thus, crude oil futures become one of financial instruments of hedge. The crude oil prices bring out discontinuous phenomena, because of the rare events. In this study, it estimates the conditional value-at-risk of the minimum variance hedging portfolio by using the bivariate CBP-GARCH model proposed by Chan(2003). Moreover, this study is to evaluate the accuracy of the bivariate CBP-GARCH model by using backtesting method based on likelihood ratio test proposed by Kupiec(1995) and conditional coverage test proposed by Christoffersen(1998).The empirical results are as follows.
     The conditional value-at-risk model of the minimum variance hedging portfolio by using the bivariate CBP-GARCH model passes the backtesting; however, the conditional value-at-risk model of the minimum variance hedging portfolio by using non-hedge model and the bivariate DCC-GARCH model do not pass. The conditional value-at-risk model of the minimum variance hedging portfolio by using the bivariate CBP-GARCH model has high accuracy; it is because that it can capture dynamic jump process and jump correlation. Therefore, if we just consider the dynamic volatility process, it could underestimate risk and let investors bear the loss than expected. This result can be used as a hedge reference for investors.
Other Abstract
Table of Content (with Page Number)
目錄Ⅰ
表目錄Ⅱ
圖目錄Ⅲ
1.  緒論1
2.  樣本與方法5
2.1 研究樣本與資料來源5
2.2 實證模型5
2.3 最小變異數避險比率9
2.4 最小變異數避險組合的條件風險值估計10
2.5 風險值模型績效的衡量12
3.  實證結果與分析15
3.1 基本敘述統計分析15
3.2 實證模型的參數估計18
3.3 條件風險值績效衡量20
4.  結論與建議24
4.1 結論24
4.2 建議25
參考文獻26
中文文獻26
英文文獻27

表目錄
表3-1  西德州原油現貨與期貨報酬的基本敘述統計分析17
表3-2  DCC-GARCH(1,1)與CBP-GARCH(1,1)模型的參數估計19
表3-3  條件最小變異數避險組合的避險比率21
表3-4  最小變異數避險組合的條件風險值績效22

圖目錄
圖3-1  西德州原油現貨與期貨日資料價格時間走勢圖15
圖3-2  西德州原油現貨與期貨日資料報酬時間走勢圖16
圖3-3  移動視窗法的樣本外條件風險值估計20
圖3-4  CBP-GARCH(1,1)模型的最小變異數避險組合報酬時間走勢圖23
References
中文文獻
1.李沃牆與柯中偉 (2011)。外匯投資組合之風險值評估-分量迴歸的應用,中原企管評論,第九卷第一期,頁97-116。
2.李彥賢、姜淑美與邱建良 (2006)。亞洲金融風暴對台灣股匯市影響:跳躍-擴散模型應用,朝陽商管評論,第五卷第一期,頁1-22。
3.林丙輝與葉仕國 (1999)。台灣股票價格非連續跳躍變動與條件異質變異之研究,證券市場發展季刊,第十一卷第十一期,頁61-92。
4.林楚雄與王韻怡 (2008)。異質變異資產之成份風險值評價投資組合風險值:極值方法之應用,管理與系統,第十五卷第一期,頁33-53。
5.林師模、謝文耀與林晉勗 (2009)。原油進口之動態避險策略分析,農業與資源經濟,第六卷第二期,頁1-27。
6.劉洪鈞、黃聖志與王怡文 (2008)。西德州與布蘭特原油避險策略,真理財經學報,第十八期,頁71-98。
7.蘇榮斌 (2010)。美國原油市場之風險值預測,中華科技大學學報,第四十二期,頁161-175。

英文文獻
1.Akgiray, V. and G. G. Booth (1986). Compound distribution models of stock returns: An empirical comparison. Journal of Financial Research, 10(3), 259-280.
2.Alexander, C. and E. Lazar (2006). Normal mixture GARCH(1,1): Applications to foreign exchange markets. Journal of Applied Econometrics, 21(2), 307-336.
3.Angelidis, T., A. Benos and S. Degiannakis (2007). A robust VaR model under different time periods and weighting schemes. Review Quantitative Finance and Accounting, 28, 187-201.
4.Angelidis, T. and A. Benos (2008). Value-at-risk for Greek stocks. Multinational Finance Journal, 12(1), 67-104.
5.Asai, M. and M. McAleer (2008). A portfolio index GARCH model. International Journal of Forecasting, 24(3), 449-461.
6.Baba, Y., R. F. Engle, D. F. Kraft and K. F. Kroner (1989). Multivariate simultaneous generalized ARCH. UCSD, Department of Economics, University of California, San Diego, CA Unpublished manuscript.
7.Ball, C. A. and W. N. Torous (1985). On jumps in stock returns. Journal of Financial Quantitative Analysis, 10, 337-351.
8.Bollerslev, T. (1986). Generalized autoregressive conditional hetero- skedasticity. Journal of Economics, 31, 307-327.
9.Bollerslev, T. (1987). A conditionally heteroskedastic time series model for speculative prices and rates of return. Review of Economics and Statistics, 69, 542-547.
10.Brooks, C., A. D. Clare, J. W. Dalle Molle and G. Persand (2005). A comparison of extreme value theory approaches for determining value at risk. Journal of Empirical Finance, 12, 339-352.
11.Campbell, J. T. (1934). The Poisson correlation function. Proceedings of the Edinburgh Mathematical Society, 2, 18-26. 
12.Caporin, M. and M. Mcaleer (2008). Scalar BEKK and indirect DCC. Journal of Forecasting, 27(6), 537-549.
13.Chan, W. H. (2003). A correlated bivariate Poisson jump model for foreign exchange. Empirical Economics, 28, 669-685.
14.Chan, W. H. and J. M. Maheu (2002). Conditional jump dynamics in stock market returns. Journal of Business and Economic Statistics, 20(3), 377-389.
15.Chan, W. H. and D. Young (2006). Jumping hedges: An examination of movements in copper spot and futures markets. Journal of Futures Markets, 26(2), 169-188.
16.Chang, K. L. (2011). The optimal value-at-risk hedging strategy under bivariate regime switching ARCH framework. Applied Economics, 43(21), 2627-2640.
17.Chang, K. H. and M. J. Kim (2001). Jumps and time-varying correlations in daily foreign exchange rates. Journal of International Money and Finance, 20(5), 611-637.
18.Chang, T. H., H. M. Su and C. L. Chiu (2011). Value-at-risk estimation with the optimal dynamic biofuel portfolio. Energy Economics, 33(2), 264-272. 
19.Chang, C. L., M. Mcaleer and R. Tansuchat (2011). Crude oil hedging strategies using dynamic multivariate GARCH. Energy Economics, 33(5), 912-923.
20.Cheng, W. H. and J. C. Hung (2011). Skewness and leptokurtosis in GARCH-typed VaR estimation of petroleum and metal asset returns. Journal of Empirical Finance, 18, 160-173.
21.Chiu, C. L., M. C. Lee and J. C. Hung (2005). Estimation of value-at-risk under jump dynamics and asymmetric information. Applied Financial Economics, 15, 1095-1106.
22.Chiu, C. L. and J. C. Hung (2007). Normal and abnormal information transmissions: Evidence from China's stock markets. Applied Economics Letters, 14(12), 863-870.
23.Chong, J. (2004). Value at risk from econometric models and implied from currency options. Journal of Forecasting, 23(8), 603-620.
24.Christoffersen, P. (1998). Evaluating interval forecasts. International Economic Review, 39(4), 841-862.
25.Engle, R. (1982). Autoregressive conditional heteroskedasticity with estimates of variance of UK inflation. Econometrica, 50, 987-1008.
26.Fama, E. F. (1965). The behavior of stock market price. Journal of Business, 38, 34-105.
27.Gao, F. and F. Song (2008). Estimation risk in GARCH VaR and ES estimates. Econometric Theory, 24(5), 1404-1424.
28.Giot, P. and S. Laurent (2003). Value-at-risk for long and short trading positions. Journal of Applied Econometrics, 18(6), 641-664.
29.Guidolin, M., and A. Timmermann (2006). An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns. Journal of Applied Econometrics, 21(1), 1-22.
30.Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. Economics Policy Review, 2, 39-70.
31.Hung, J. C., C. L. Chiu and M. C. Lee (2006). Hedging with zero-value at risk hedge ratio. Applied Financial Economics, 16(3), 259-269.
32.Jarrow, H. and E. R. Rosenfeld (1984). Jump risks and the intertemporal capital asset pricing model. Journal of Business, 57, 337-351.
33.Johnson, L. (1960). The theory of hedging and speculation in commodity futures. Review of Economic Studies, 27, 139-151.
34.Jorion, P. (1988). On jump processes in the foreign exchange and stock markets. Review of Financial Studies, 1(4), 427-445.
35.Jorion, P. (2000). Value at risk: The new benchmark for managing financial risk. McGraw-Hill, New York.
36.Kim, H. Y. and J. P. Mei (2001). What makes the stock market jump? An analysis of political risk on Hong Kong stock returns. Journal of International Money and Finance, 20(7), 1003-1016.
37.Kupiec, P. (1995). Techniques for verifying the accuracym of risk management models. Journal of Derivatives, 3, 73-84.
38.Lee, M. C. and W. H. Cheng (2007). Correlated jumps in crude oil and gasoline during the Gulf War. Applied Economics, 39(7), 903-913.
39.Lee, Y. H., H. N. Hu and J. S. Chiou (2010). Jump dynamics with structural breaks for crude oil prices. Energy Economics, 32(2), 343-350.
40.Lin, C. T. and Y. H. Lee (2010). The jump-diffusion process for the VIX and the S&P 500 index. African Journal of Business Management, 4(9), 1761-1768. 
41.Liu, H. C. and J. C. Hung (2010). Forecasting volatility and capturing downside risk of the Taiwanese futures markets under the financial tsunami. Managerial Finance, 36(10), 860-875.
42.Lu, X., K. I. Kawai and K. Maekawa (2010). Estimating bivariate GARCH-jump model based on high frequency data: The case of revaluation of the Chinese yuan in july 2005. Asia-Pacific Journal of Operational Research, 27(2), 287-300.
43.Maheu, J. M. and T. H. McCurdy (2004). New arrival, jump dynamics and volatility components for individual stock returns. Journal of Finance, 59(2), 755-793.
44.Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, 36, 394-419.
45.M’Kendrick, A. G., (1926). Applications of mathematics to medical problems. Proceedings of the Edinburgh Mathematical Society, 44, 98-130.
46.Obi, P., S. Sil and J. G. Choi (2010). Value-at-risk with time varying volatility in south African equities. Journal of Global Business and Technology, 6(2), 1-11.
47.Pan, J. (2002). The jump-risk premia implicit in options: Evidence from an integrated time-series study. Journal of Financial Economics, 63(1), 3-50.
48.Perignon, C. and D. R. Smith (2008). A new approach to comparing VaR estimation methods. Journal of Derivatives, 16(2), 54-66.
49.Sadeghi, M. and S. Shavvalpour (2006). Energy risk management and value at risk modeling. Energy Policy, 34, 3367-3373.
50.Sarno, L. and M. P. Valente (2005). Empirical exchange rate models and currency risk: Some evidence from density forecasts. Journal of International Money and Finance, 24(2), 363-385.
51.So, M. K. P. and P. L. H. Yu (2006). Empirical analysis of GARCH models in value at risk estimation. International Financial Markets, Institutions and Money, 16, 180-197.
52.Tsafack, G. (2009). Asymmetric dependence implications for extreme risk management. Journal of Derivatives, 17(1), 7-20.
53.Wang, Y., C. Wu and Y. Wei (2011). Can GARCH-class models capture long memory in WTI crude oil markets? Economic Modelling, 28(3), 921-927.
Terms of Use
Within Campus
I request to embargo my thesis/dissertation for 5 year(s) right after the date I submit my Authorization Approval Form.
Agree to authorize disclosure on campus
Duration for delaying release from 5 years.
Outside the Campus
I grant the authorization for the public to view/print my electronic full text with royalty fee and contact me for receiving the payment.
Duration for delaying release from 5 years.
 Top

If you have any questions, please contact us!

Library: please call (02)2621-5656 ext. 2487 or email