政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/140972
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113303/144284 (79%)
Visitors : 50815598      Online Users : 734
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/140972


    Title: 風險值與期望損失之不同模型的績效評估—以商品市場為例
    The Performance Evaluation of Value-at-Risk and Expected Shortfall Models Evidence from Commodity Market
    Authors: 康涴茜
    Kang, Wo-Chien
    Contributors: 顏佑銘
    Yen, Yu-Min
    康涴茜
    Kang, Wo-Chien
    Keywords: 風險值
    期望損失
    FZ損失函數
    預測
    績效評估
    Value-at-risk
    Expected shortfall
    FZ loss function
    Forecast
    Performance evaluation
    Date: 2022
    Issue Date: 2022-08-01 17:04:45 (UTC+8)
    Abstract: 巴塞爾公約已建議使用風險值（Value-at-Risk, VaR）和期望損失(Expected shortfall, ES）作為衡量尾端風險之工具。本研究採用了不同的模型來預測黃金、白銀、銅以及原油四種商品的VaR和ES。使用的方法包括了經由FZ損失函數(Fissler and Ziegel, 2016)來進行半參數模型估計與其他傳統模型。本研究以滾動窗方法估計VaR和ES模型並使用三種損失函數、命中率檢定以及Diebold-Mariano(DM)檢定進行預測績效評估。實證結果顯示在風險值水準為0.01與0.025之下，一些使用了FZ損失函數的半參數模型及非對稱GARCH模型，都各可以有不錯的表現；而在風險值水準為0.05與0.1之下，一些GARCH模型的預測績效平均而言反而較佳。
    The Basel III Accord has proposed using Value-at-Risk (VaR) and Expected Shortfall (ES) as tail risk measures. The main purpose of this study is to forecast VaR and ES with different models for four commodities: gold, silver, copper, and crude oil. We use semi-parametric models with the FZ loss function (Fissler and Ziegel, 2016) and other traditional models to estimate VaR and ES with a rolling window approach. To evaluate forecasts performances, we use three loss functions, hit proportion test, and Diebold-Mariano (DM) test. The empirical results show that some semi-parametric models with the FZ loss function and asymmetric GARCH models perform well under the VaR levels of 0.01 and 0.025. Some GARCH models have relatively better forecasts performances under the VaR levels of 0.05 and 0.1.
    Reference: 1. Acerbi, C. and Tasche, D. (2002). On the coherence of expected shortfall, Journal of Banking & Finance, 26(7), 1487-1503.
    2. Artzner, P., Delbaen, F., Eber, J.M. and Heath, D. (1999). Coherent measures of risk, Mathematical Finance, 9(3), 203-228.
    3. Barone-Adesi, G., Giannopoulos, K., Vosper, L. (1999). VaR Without Correlations for Nonlinear Portfolios, Journal of Futures Markets, 19, 583-602.
    4. Beder, T. S. (1995). VAR: Seductive but Dangerous, Financial Analysts Journal, 51(5), 12-24.
    5. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31(3), 307-327.
    6. Chen, Cathy W. S., Gerlach, Richard, Hwang, Bruce B. K. and McAleer, Michael (2012). Forecasting Value-at-Risk using nonlinear regression quantiles and the intra-day range, International Journal of Forecasting, 28(3), 557-574.
    7. Chou, R. Y., Yen, T. J. and Yen, Y. M. (2022). Forecasting Expected Shortfall and Value-at-Risk with the FZ Loss and Realized Variance Measures, Taiwan Economic Forecast and Policy, 52(3), 89-140.
    8. Ding, Z., Granger, C. and Engle, R. (1993). A long memory property of stock market returns and a new model, Journal of Empirical Finance, 1(1), 83-106.
    9. Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50(4), 987-1007.
    10. Engle, R. F., and Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric Reviews, 5, 1-500.
    11. Engle, R. F. and Manganelli, S. (2004). CAViaR:Conditional Autoregressive Value at Risk by Regression Quantiles, Journal of Business & Economic Statistics, 22(4), 367-381.
    12. Fissler, T. and Ziegel , J. F. (2016). Higher order elicitability and Osbands principle, The Annals of Statistics, 44, 1680-1707.
    13. Hull, J., and White, A. (1998). Incorporating volatility updating into the historical simulation method for value-at-risk. Journal of Risk, 1(1), 5-19.
    14. Jeon, J. and Taylor, J. W. (2013). Using CAViaR models with implied volatility for value-at-risk estimation. Journal of Forecasting, 32(1), 62-74.
    15. McCurdy, T. H. and Morgan, I. (1988). Testing the Martingale Hypothesis in Deutsche Mark Futures with Models Specifying the Form of the Heteroskedasticity, Journal of Applied Econometrics, 3, 187-202.
    16. Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns:A New Approach, Econometrica, 59, 347-370.
    17. Patton, A. J., Ziegel, J. F. and Chen, R. (2019). Dynamic semiparametric models for expected shortfall (and Value-at-Risk), Journal of Econometrics, 211, 388-413.
    18. Riskmetrics, T. M. (1996). JP Morgan Technical Document.
    19. Taylor, J. W. (2019). Forecasting Value at Risk and Expected Shortfall Using a Semiparametric Approach Based on the Asymmetric Laplace Distribution, Journal of Business & Economic Statistics, 37, 121-133.
    20. Yamai, Y. and Yoshiba, T. (2005). Value-at-Risk Versus Expected Shortfall: A Practical Perspective, Journal of Banking & Finance, 29(4), 997-1015.
    21. Zheng, Y., Zhu, Q, Li, G. and Xiao, Z. (2018). Hybrid quantile regression estimation for time series models with conditional heteroscedasticity, Journal of the Royal Statistical Society Series B (Statistical Methodology), 80, 975-993.
    Description: 碩士
    國立政治大學
    國際經營與貿易學系
    109351027
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109351027
    Data Type: thesis
    DOI: 10.6814/NCCU202200574
    Appears in Collections:[Department of International Business] Theses

    Files in This Item:

    File Description SizeFormat
    102701.pdf2242KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback