請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98349完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳宜廷 | zh_TW |
| dc.contributor.advisor | Yi-Ting Chen | en |
| dc.contributor.author | 蔡秉叡 | zh_TW |
| dc.contributor.author | Ping-Jui Tsai | en |
| dc.date.accessioned | 2025-08-04T16:07:21Z | - |
| dc.date.available | 2025-08-05 | - |
| dc.date.copyright | 2025-08-04 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-07-30 | - |
| dc.identifier.citation | Al-Thaqeb, S. A. and Algharabali, B. G. (2019). Economic policy uncertainty: A literature review. The Journal of Economic Asymmetries, 20:e00133.
Albulescu, C. T. (2021). COVID-19 and the united states financial markets＇volatility. Finance Research Letters, 38:101699. Asgharian, H., Hou, A. J., and Javed, F. (2013). The importance of the macroeconomic variables in forecasting stock return variance: A GARCH-MIDAS approach. Journal of Forecasting, 32(7):600–612. Baker, S. R., Bloom, N., and Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4):1593–1636. Baker, S. R., Bloom, N., Davis, S. J., and Kost, K. J. (2019). Policy news and stock market volatility. Working Paper 25720, National Bureau of Economic Research. Baker, S. R., Bloom, N., Davis, S. J., Kost, K. J., Sammon, M. C., and Viratyosin, T. (2020). The unprecedented stock market impact of COVID-19. Working Paper 26945, National Bureau of Economic Research. Caldara, D. and Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4):1194– 1225. Conrad, C. and Kleen, O. (2020). Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models. Journal of Applied Econometrics, 35(1):19–45. Conrad, C. and Loch, K. (2015). Anticipating long-term stock market volatility. Journal of Applied Econometrics, 30(7):1090–1114. Engle, R. F., Ghysels, E., and Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. The Review of Economics and Statistics, 95(3):776–797. Eniayewu, P. E., Samuel, G. T., Joshua, J. D., Samuel, B. T., Dogo, B. S., Yusuf, U., Ihekuna, R. O., and Mevweroso, C. R. (2024). Forecasting exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach. Scientific African, 23:e02101. Fan, Y. and Tang, C. Y. (2012). Tuning parameter selection in high dimensional penalized likelihood. Journal of the Royal Statistical Society Series B: Statistical Methodology,75(3):531–552. Fang, L., Chen, B., Yu, H., and Qian, Y. (2018). The importance of global economic policy uncertainty in predicting gold futures market volatility: A GARCH-MIDAS approach. Journal of Futures Markets, 38(3):413–422. Fang, T., Lee, T.-H., and Su, Z. (2020). Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection. Journal of Empirical Finance, 58:36–49. Ghysels, E., Sinko, A., and, R. V. (2007). Midas regressions: Further results and new directions. Econometric Reviews, 26(1):53–90. Gulen, H. and Ion, M. (2015). Policy uncertainty and corporate investment. The Review of Financial Studies, 29(3):523–564. Hansen, B. E. (1994). Autoregressive conditional density estimation. International Economic Review, 35(3):705–730. Hansen, P. R., Lunde, A., and Nason, J. M. (2011). The model confidence set. Econometrica, 79(2):453–497. Jiang, C., Li, Y., Xu, Q., and Liu, Y. (2021). Measuring risk spillovers from multiple developed stock markets to china: A vine-copula-GARCH-MIDAS model. International Review of Economics Finance, 75:386–398. Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. The Journal of Derivatives, 2(4):73–84. Liu, R., Zhou, Z., and Li, H. (2022). The influence of COVID-19 epidemic on bdi volatility: An evidence from GARCH-MIDAS model. Ocean & Coastal Management, 229:106330. Lu, H., Gao, Q., Xiao, L., and Dhesi, G. (2024). Forecasting EUA futures volatility with geopolitical risk: Evidence from GARCH-MIDAS models. Review of Managerial Science, 18:1917–1943. Patton, A. J. (2011). Journal of Econometrics, 160(1):246–256. Volatility forecast comparison using imperfect volatility proxies. Raza, S. A., Masood, A., Benkraiem, R., and Urom, C. (2023). Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach. Energy Economics, 120:106591. Rogers, L. C. G. and Satchell, S. E. (1991). Estimating variance from high, low and closing prices. The Annals of Applied Probability, 1(4):504–512. Salisu, A. A., Ogbonna, A. E., Gupta, R., and Ji, Q. (2024). Energy market uncertainties and exchange rate volatility: A GARCH-MIDAS approach. Finance Research Letters, 67:105847. Schwert, G. W. (1989). Why does stock market volatility change over time? The Journal of Finance, 44(5):1115–1153. Segnon, M., Gupta, R., and Wilfling, B. (2024). Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks. International Journal of Forecasting, 40(1):29–43. Yang, D. and Zhang, Q. (2000). Drift‐independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3):477–492. Zou, H. (2006). The adaptive Lasso and its oracle properties. Journal of the American Statistical Association, 101(476):1418–1429. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98349 | - |
| dc.description.abstract | 本文應用 Fang et al. (2020, Journal of Empirical Finance) 所提出結合懲罰項之自我迴歸條件異質變異數-混合頻率模型 (GARCH-MIDAS)，進行股票與債券市場報酬率之波動度與涉險值（VaR）的日頻率預測。在實證過程中考慮了多個月頻率的預測變數；此外，也加入傳染病風險變數做為新的解釋變數。實證結果表示所考慮的實證設定在股票市場波動度與涉險值預測均具有相對的優勢；另外，就債券市場的波動度與涉險值預測而言，雖整體的預測能力下降，但本文的實證設定尚能展現一定的預測能力。 | zh_TW |
| dc.description.abstract | This thesis applies the generalized autoregressive conditional heteroskedasticity mixed data sampling (GARCH-MIDAS) with penalized framework proposed by Fang et al. (2020, Journal of Empirical Finance) to generate daily forecasts of volatility and Value at Risk (VaR) for stock and bond index returns. Beyond daily returns, we incorporate several monthly macroeconomic and financial predictors, and we further augment the model with indicators that capture infectious disease risk. Empirically, our specification outperforms both the traditional GJR-GARCH model and the quarterly-frequency GARCH-MIDAS framework. While its overall predictive power declines for bond index returns, the proposed specification still delivers a meaningful level of accuracy in that market. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-04T16:07:21Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-04T16:07:21Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii Contents iii List of Figures v List of Tables vi Chapter 1 Introduction 1 Chapter 2 Econometric Method 5 2.1 Model 5 2.2 Estimation Procedure 7 2.3 Forecasting 9 2.3.1 Volatility Forecast 9 2.3.2 Value-at-Risk Forecast 10 Chapter 3 Empirical Setting 12 3.1 Financial Variables 13 3.2 Macroeconomic Variables 14 3.3 Volatility Variables 15 Chapter 4 Empirical Findings 19 4.1 In-sample Estimation 19 4.2 Out-of-sample Forecast 25 4.2.1 Volatility Forecast 25 4.2.2 Value-at-Risk Forecast 34 Chapter 5 Conclusion 39 References 41 | - |
| dc.language.iso | en | - |
| dc.subject | 波動度預測 | zh_TW |
| dc.subject | 結合懲罰項之混合頻率-自我迴歸條件異質變異數模型 | zh_TW |
| dc.subject | 涉險值預測 | zh_TW |
| dc.subject | Value at Risk forecast | en |
| dc.subject | GARCH-MIDAS with penlized framework | en |
| dc.subject | Volatility forecast | en |
| dc.title | GARCH-MIDAS 對波動性與涉險值預測的應用 | zh_TW |
| dc.title | An Empirical Application of GARCH-MIDAS for Volatility and Value-at-Risk Forecasts | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 洪志清;楊睿中 | zh_TW |
| dc.contributor.oralexamcommittee | Chih-Ching Hung;Jui-Chung Yang | en |
| dc.subject.keyword | 結合懲罰項之混合頻率-自我迴歸條件異質變異數模型,波動度預測,涉險值預測, | zh_TW |
| dc.subject.keyword | GARCH-MIDAS with penlized framework,Volatility forecast,Value at Risk forecast, | en |
| dc.relation.page | 44 | - |
| dc.identifier.doi | 10.6342/NTU202502186 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-07-31 | - |
| dc.contributor.author-college | 管理學院 | - |
| dc.contributor.author-dept | 財務金融學系 | - |
| dc.date.embargo-lift | 2025-08-05 | - |
| 顯示於系所單位： | 財務金融學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf | 820.19 kB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。