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題名 以關聯結構條件風險值模型解構臺灣證券市場系統風險
Application of copula CoVaR models in systemic risk of Taiwan security market
作者 陳紹傑
Chen, Shao Jie
貢獻者 徐士勛
陳紹傑
Chen, Shao Jie
關鍵詞 關聯結構
條件風險值
系統風險
Copula
CoVaR
Systemic risk
日期 2017
上傳時間 11-Jul-2017 12:07:16 (UTC+8)
摘要 本研究以關連結構模型分析 2007 年金融海嘯前後臺灣證券市場與國際主要市場之關係,進而由條件風險值估算當其他市場面臨風險事件下,臺灣證券市場所面臨之潛在系統風險。實證結果顯示,各市場條件風險值估值均低於臺灣證券市場自身在險值水準,顯示如由在險值衡量該風險仍有未盡妥善之處,反應條件風險值與國際市場資訊對於臺灣市場之系統風險衡量有相當之價值。此外,我們亦得以檢視臺灣證券市場是否受不同市場之系統風險影響,結果指出金融海嘯後各市場影響漸趨一致。
In this paper, we first apply copula model to capture the relationship be-tween Taiwan securities market and major international markets across the financial tsunami in 2007. The systemic risk for Taiwan’s securities market is then measured by CoVaR while other markets facing risk events. The main results show that the CoVaR of each market is lower than the VaR of Taiwan securities market, which means there is still room for improvement in the measurement of systemic risk by VaR. They point out that CoVaR and the information of international market are valuable in measuring the systemic risk of Taiwan securities market. Moreover, we can also check the systemic impact of major international markets on Taiwan securities mar-ket. The results indicate that the impact of others market on Taiwan tend to be identical after the financial tsunami.
參考文獻 Aas, K. (2016). Pair-copula constructions for financial applications: A review. Econometrics, 4(4), 43.
Abadie, A. (2002). Bootstrap tests for distributional treatment effects in instrumental variable models. Journal of the American statistical Association, 97(457), 284-292.
Adrian, T., and Brunnermeier, M. K. (2011). CoVaR (No. w17454). National Bureau of Economic Research.
Algieri, B., and Leccadito, A. (2017). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312-322.
Bartram, S. M., Taylor, S. J., and Wang, Y. H. (2007). The Euro and European financial market dependence. Journal of Banking & Finance, 31(5), 1461-1481.
Bedford, T., and Cooke, R. M. (2001). Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial intelligence, 32(1), 245-268.
Cherubini, U., Luciano, E., and Vecchiato, W. (2004). Copula methods in finance. John Wiley & Sons : New Jersey.
Embrechts, P., McNeil, A., and Straumann, D. (1998). Correlation and dependency in risk management: properties and pitfails. Working paper.
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.
Girardi, G., and Ergun, A. T. (2013). Systemic risk measurement: Multivariate GARCH estimation of CoVaR. Journal of Banking & Finance, 37(8), 3169-3180.
Hansen, L. P. (2012). Challenges in identifying and measuring systemic risk. (No. w18505). National Bureau of Economic Research.
Joe, H. (1997). Multivariate models and multivariate dependence concepts. Chapman & Hall : London.
Joe, H., and Xu, J. J. (1996). The estimation method of inference functions for margins for multivariate models. Technical report,
Jondeau, E., and Rockinger, M. (2006). The copula-garch model of conditional dependencies: An international stock market application. Journal of international money and finance, 25(5), 827-853.
Karimalis, E. N., and Nomikos, N. (2014). Measuring systemic risk in the European banking sector: A Copula CoVaR approach. Working paper.
Koenker, R., and Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 46, 33-50.
Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. The journal of Derivatives, 3(2), 73-84.
Liu, C. L., and Yang, H. F. (2017). Systemic risk in carry-trade portfolios. Finance Research Letters, 20, 40-46.
Nelsen, R. B. (2006). An Introduction to Copulas. Springer-Verlag : New York.
Patton, A. J. (2002). Applications of copula theory in financial econometrics. San Diego : University of California.
Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556.
Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and renewable energy stock prices?. Energy Economics, 48, 32-45.
Reboredo, J. C., and Ugolini, A. (2015). Systemic risk in European sovereign debt markets: A CoVaR-copula approach. Journal of International Money and Finance, 51, 214-244.
Reboredo, J. C., and Ugolini, A. (2016). Systemic risk of Spanish listed banks: a vine copula CoVaR approach. Spanish Journal of Finance and Accounting Revista Espanola de Financiacion y Contabilidad, 45(1), 1-31.
Reboredo, J. C., and Ugolini, A. (2016). The impact of downward/upward oil price movements on metal prices. Resources Policy, 49, 129-141.
Silvapulle, P., Fenech, J. P., Thomas, A., and Brooks, R. (2016). Determinants of sovereign bond yield spreads and contagion in the peripheral EU countries. Economic Modelling, 58, 83-92.
Sklar, M. (1959). Fonctions de Repartition a n Dimensions et Leurs Marges. Universite Paris, 8, 229-231.
White Jr, H. L., Kim, T. H., and Manganelli, S. (2008). Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR. ECB Working paper No. 957.
White, H., Kim, T. H., and Manganelli, S. (2015). VAR for VaR: Measuring tail dependence using multivariate regression quantiles. Journal of Econometrics, 187(1), 169-188.
Wong, A., and Fong, T. (2010). An analysis of interconnectvity among the Asia-Pacific economies. Hong Kong Monetary Authority Working paper.
Xu, J. J. (1996). Statistical modelling and inference for multivariate and longitudinal discrete response data (Doctoral dissertation, University of British Columbia).
描述 碩士
國立政治大學
經濟學系
104258036
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104258036
資料類型 thesis
dc.contributor.advisor 徐士勛zh_TW
dc.contributor.author (Authors) 陳紹傑zh_TW
dc.contributor.author (Authors) Chen, Shao Jieen_US
dc.creator (作者) 陳紹傑zh_TW
dc.creator (作者) Chen, Shao Jieen_US
dc.date (日期) 2017en_US
dc.date.accessioned 11-Jul-2017 12:07:16 (UTC+8)-
dc.date.available 11-Jul-2017 12:07:16 (UTC+8)-
dc.date.issued (上傳時間) 11-Jul-2017 12:07:16 (UTC+8)-
dc.identifier (Other Identifiers) G0104258036en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110854-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 經濟學系zh_TW
dc.description (描述) 104258036zh_TW
dc.description.abstract (摘要) 本研究以關連結構模型分析 2007 年金融海嘯前後臺灣證券市場與國際主要市場之關係,進而由條件風險值估算當其他市場面臨風險事件下,臺灣證券市場所面臨之潛在系統風險。實證結果顯示,各市場條件風險值估值均低於臺灣證券市場自身在險值水準,顯示如由在險值衡量該風險仍有未盡妥善之處,反應條件風險值與國際市場資訊對於臺灣市場之系統風險衡量有相當之價值。此外,我們亦得以檢視臺灣證券市場是否受不同市場之系統風險影響,結果指出金融海嘯後各市場影響漸趨一致。zh_TW
dc.description.abstract (摘要) In this paper, we first apply copula model to capture the relationship be-tween Taiwan securities market and major international markets across the financial tsunami in 2007. The systemic risk for Taiwan’s securities market is then measured by CoVaR while other markets facing risk events. The main results show that the CoVaR of each market is lower than the VaR of Taiwan securities market, which means there is still room for improvement in the measurement of systemic risk by VaR. They point out that CoVaR and the information of international market are valuable in measuring the systemic risk of Taiwan securities market. Moreover, we can also check the systemic impact of major international markets on Taiwan securities mar-ket. The results indicate that the impact of others market on Taiwan tend to be identical after the financial tsunami.en_US
dc.description.tableofcontents 1 緒論 1
2 文獻回顧 3
3 研究方法 7
3.1 Copula 理論 7
3.2 常見的二元 Copula 函數與參數估計方法 8
3.2.1參數估計方法 11
3.2.2最大概似法 (MLE) 11
3.2.3分步最大概似法 (IFM) 13
3.2.4典型最大概似法 (CML) 14
3.3 CoVaR 14
4 實證研究 17
4.1 樣本資料與敘述統計 17
4.2 邊際分配與 Copula 選擇 21
4.3 Copula CoVaR 分析 27
5 結論 33		zh_TW
dc.format.extent 1291928 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104258036en_US
dc.subject (關鍵詞) 關聯結構zh_TW
dc.subject (關鍵詞) 條件風險值zh_TW
dc.subject (關鍵詞) 系統風險zh_TW
dc.subject (關鍵詞) Copulaen_US
dc.subject (關鍵詞) CoVaRen_US
dc.subject (關鍵詞) Systemic risken_US
dc.title (題名) 以關聯結構條件風險值模型解構臺灣證券市場系統風險zh_TW
dc.title (題名) Application of copula CoVaR models in systemic risk of Taiwan security marketen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Aas, K. (2016). Pair-copula constructions for financial applications: A review. Econometrics, 4(4), 43.
Abadie, A. (2002). Bootstrap tests for distributional treatment effects in instrumental variable models. Journal of the American statistical Association, 97(457), 284-292.
Adrian, T., and Brunnermeier, M. K. (2011). CoVaR (No. w17454). National Bureau of Economic Research.
Algieri, B., and Leccadito, A. (2017). Assessing contagion risk from energy and non-energy commodity markets. Energy Economics, 62, 312-322.
Bartram, S. M., Taylor, S. J., and Wang, Y. H. (2007). The Euro and European financial market dependence. Journal of Banking & Finance, 31(5), 1461-1481.
Bedford, T., and Cooke, R. M. (2001). Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial intelligence, 32(1), 245-268.
Cherubini, U., Luciano, E., and Vecchiato, W. (2004). Copula methods in finance. John Wiley & Sons : New Jersey.
Embrechts, P., McNeil, A., and Straumann, D. (1998). Correlation and dependency in risk management: properties and pitfails. Working paper.
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.
Girardi, G., and Ergun, A. T. (2013). Systemic risk measurement: Multivariate GARCH estimation of CoVaR. Journal of Banking & Finance, 37(8), 3169-3180.
Hansen, L. P. (2012). Challenges in identifying and measuring systemic risk. (No. w18505). National Bureau of Economic Research.
Joe, H. (1997). Multivariate models and multivariate dependence concepts. Chapman & Hall : London.
Joe, H., and Xu, J. J. (1996). The estimation method of inference functions for margins for multivariate models. Technical report,
Jondeau, E., and Rockinger, M. (2006). The copula-garch model of conditional dependencies: An international stock market application. Journal of international money and finance, 25(5), 827-853.
Karimalis, E. N., and Nomikos, N. (2014). Measuring systemic risk in the European banking sector: A Copula CoVaR approach. Working paper.
Koenker, R., and Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 46, 33-50.
Kupiec, P. H. (1995). Techniques for verifying the accuracy of risk measurement models. The journal of Derivatives, 3(2), 73-84.
Liu, C. L., and Yang, H. F. (2017). Systemic risk in carry-trade portfolios. Finance Research Letters, 20, 40-46.
Nelsen, R. B. (2006). An Introduction to Copulas. Springer-Verlag : New York.
Patton, A. J. (2002). Applications of copula theory in financial econometrics. San Diego : University of California.
Patton, A. J. (2006). Modelling asymmetric exchange rate dependence. International economic review, 47(2), 527-556.
Reboredo, J. C. (2015). Is there dependence and systemic risk between oil and renewable energy stock prices?. Energy Economics, 48, 32-45.
Reboredo, J. C., and Ugolini, A. (2015). Systemic risk in European sovereign debt markets: A CoVaR-copula approach. Journal of International Money and Finance, 51, 214-244.
Reboredo, J. C., and Ugolini, A. (2016). Systemic risk of Spanish listed banks: a vine copula CoVaR approach. Spanish Journal of Finance and Accounting Revista Espanola de Financiacion y Contabilidad, 45(1), 1-31.
Reboredo, J. C., and Ugolini, A. (2016). The impact of downward/upward oil price movements on metal prices. Resources Policy, 49, 129-141.
Silvapulle, P., Fenech, J. P., Thomas, A., and Brooks, R. (2016). Determinants of sovereign bond yield spreads and contagion in the peripheral EU countries. Economic Modelling, 58, 83-92.
Sklar, M. (1959). Fonctions de Repartition a n Dimensions et Leurs Marges. Universite Paris, 8, 229-231.
White Jr, H. L., Kim, T. H., and Manganelli, S. (2008). Modeling autoregressive conditional skewness and kurtosis with multi-quantile CAViaR. ECB Working paper No. 957.
White, H., Kim, T. H., and Manganelli, S. (2015). VAR for VaR: Measuring tail dependence using multivariate regression quantiles. Journal of Econometrics, 187(1), 169-188.
Wong, A., and Fong, T. (2010). An analysis of interconnectvity among the Asia-Pacific economies. Hong Kong Monetary Authority Working paper.
Xu, J. J. (1996). Statistical modelling and inference for multivariate and longitudinal discrete response data (Doctoral dissertation, University of British Columbia).		zh_TW