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題名 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作者 康涴茜
Kang, Wo-Chien貢獻者 顏佑銘
Yen, Yu-Min
康涴茜
Kang, Wo-Chien關鍵詞 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日期 2022 上傳時間 1-Aug-2022 17:04:45 (UTC+8) 摘要 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 0.1.參考文獻 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. 描述 碩士
國立政治大學
國際經營與貿易學系
109351027資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109351027 資料類型 thesis dc.contributor.advisor 顏佑銘 zh_TW dc.contributor.advisor Yen, Yu-Min en_US dc.contributor.author (Authors) 康涴茜 zh_TW dc.contributor.author (Authors) Kang, Wo-Chien en_US dc.creator (作者) 康涴茜 zh_TW dc.creator (作者) Kang, Wo-Chien en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:04:45 (UTC+8) - dc.date.available 1-Aug-2022 17:04:45 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:04:45 (UTC+8) - dc.identifier (Other Identifiers) G0109351027 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/140972 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 國際經營與貿易學系 zh_TW dc.description (描述) 109351027 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 第一章、緒論 1第一節、研究動機與研究目的 1第二節、研究架構 3第二章、文獻探討 4第一節、風險值(VaR)與期望損失(ES)簡介 4第二節、風險值(VaR)與期望損失(ES)衡量 5第三節、FZ損失函數介紹 7第三章、研究方法 8第一節、FZ損失函數簡介 8第二節、FZ損失函數模型 8第三節、其他模型 11第四節、績效評估指標 12第四章、實證結果 14第一節、資料範圍與來源 14第二節、敘述統計分析 14第三節、模型實證結果 19第五章、結論與建議 33參考文獻 34 zh_TW dc.format.extent 2296748 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109351027 en_US dc.subject (關鍵詞) 風險值 zh_TW dc.subject (關鍵詞) 期望損失 zh_TW dc.subject (關鍵詞) FZ損失函數 zh_TW dc.subject (關鍵詞) 預測 zh_TW dc.subject (關鍵詞) 績效評估 zh_TW dc.subject (關鍵詞) Value-at-risk en_US dc.subject (關鍵詞) Expected shortfall en_US dc.subject (關鍵詞) FZ loss function en_US dc.subject (關鍵詞) Forecast en_US dc.subject (關鍵詞) Performance evaluation en_US dc.title (題名) 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 zh_TW dc.title (題名) The Performance Evaluation of Value-at-Risk and Expected Shortfall Models Evidence from Commodity Market en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200574 en_US
