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題名 CoVaR風險值對金融機構風險管理之重要性─以台灣金融控股公司為例
The importance of CoVaR to financial institutions risk management from Taiwanese financial holding company’s perspective作者 陳怡君
Chen, Yi Chun貢獻者 李桐豪
陳怡君
Chen, Yi Chun關鍵詞 VaR
CoVaR
分量迴歸
總體審慎監理
VaR
CoVaR
Quantile Regression
Macroprudential日期 2010 上傳時間 4-Sep-2013 10:06:23 (UTC+8) 摘要 本研究欲以分量迴歸的方法估計出台灣上市櫃金融控股公司的VaR、CoVaR及其對台灣金融市場的風險溢出，做為總體審慎監理原則下具有抗景氣特色之風險衡量參考指標。我們亦透過金控公司間之CoVaR，觀察金控公司間風險交互影響程度，盼可提供各金控公司做為個體審慎監理原則下風險管理之參考指標。本研究包含四大特色：一、運用前期市場資料可估計下期含有條件、共變、傳染、貢獻等特性之風險值，也就是CoVaR；二、透過各家金控對市場之∆CoVaR可觀察各金控公司系統風險貢獻程度差異；三、可觀察金控公司間相互交叉影響程度；四、運用金融機構特性預測未來系統風險。本研究以信用利差、長短期利差、流動性利差、匯率變動、加權指數報酬、隱含波動度變動、金控股價報酬等市場資料，透過分量迴歸估計損失機率為1%及5%之台灣金融控股公司VaR及CoVaR，並計算市場風險溢出─∆CoVaR研究各金融機構對系統風險之邊際貢獻。且以槓桿比率、市值帳面比、相對規模及資產負債不對稱比例等金融機構特性相關變數預測未來∆CoVaR，做為總體審慎監理原則下之風險管理參考指標。 本研究結果發現對台灣金融市場系統風險溢出貢獻較大的為玉山金、中信金、台新金及國泰金；國票金、永豐金、第一金及元大金則為系統風險溢出貢獻較低者。預測結果部分發現損失機率為1%時，以預測未來兩季之∆CoVaR效果較佳，預測損失機率為5%時則以預測未來三季之∆CoVaR效果較佳，顯示資料對不同的尾端損失機率分配影響顯現時間也不相同。
In this thesis, we intend to estimate Taiwanese financial holding company’s VaR, CoVaR and risk spillover to Taiwan financial market, and apply these to macroprudential risk management. In addition, we intend to develop crossover CoVaR between financial holding companies, offering risk management referral benchmark under microprudential principle to those companies.There are four features in this thesis. First, we use previous market data to estimate the conditional, comovement, contagion, and contributing VaR - CoVaR. Second, by ∆CoVaR of the institutions to the market, we can observe the holding companies’ systematic risk contribution. Third, we can observe the crossover effect of the holding companies. Last, we could use the characteristics of the institutions to predict future systematic risk. We particularly use credit spread, slope of yield curve, liquidity spread, change of exchange rate, return of market stock index, change of implied volatility and holding company’s stock price, by quantile regression, to predict the VaR and CoVaR of Taiwan’s holding companies when the probability to loss is 1% and 5%. Then we calculate market systematic risk spillover, ∆CoVaR, to observe the marginal systematic risk contribution of the institutions. Moreover, we use leverage, market-to-book ratio, relative size and maturity mismatch to predict forward ∆CoVaR, offering a reference to macroprudential risk management.Our empirical results show that in Taiwan financial market, the top four systematic risk contributors of holding companies are Esun Financial Holding, Chinatrust Financial Holding, Taishin Financial Holding and Cathay Financial Holding; the smallest ones are Waterland Financial Holding, Sino Financial Holding, First Financial Holding and Yuanta Financial Holding. We also find out that when loss probability is 1%, predicting ∆CoVaR after two seasons is better; when loss probability is 5%, predicting ∆CoVaR after three seasons is more significant. It shows that when the tail is different, the effect time is also different.參考文獻 Alexander, C. O., and C. T. Leigh(1997), “On the Covariances Matrices Used in Value at Risk Models,” Journal of Derivatives, Spring 1997, Vol. 4, No. 3, pp. 50-62.Baillie, R.T., and T. Bollerslev (1989), “The Message in Daily Exchange Rates: A Conditional Variance Tale.” Journal of Business and Economic Statistics, 1989, Vol. 7, Issue 3, pp. 297-305.Beder, T. S. (1995), “VAR: Seductive but Dangerous”, Financial Analysts Journal, 1995, Vol. 51, No. 5, September-October, pp. 12-24.Bollerslev, T. (1987), “A conditional heteroskedastic time series model for speculative prices and rates of returns”, Review of Economics and Statistics, 1987, Vol. 69, No. 3, pp. 542–547.Brunnermeier, M. K. (2009), “Deciphering the Liquidity and Credit Crunch 2007-08,”Journal of Economic Perspectives, Winter 2009, Vol. 23, No. 1, pp. 77-100.Brunnermeier, M. K., A. Crocket, G. Goodhart, A. D. Perssaud, and H. Shin (2009), “The Fundamental Principles of Financial Regulation,” 11th Geneva Report on the World Economy.Brunnermeier, M. K., and T. Adrian (2010), “CoVaR”, 2010, Working Paper.Chen M. Y. and J. E. Chen (2005), “Application of Quantile Regression to Estimation of Value at Risk”, Review of Financial Risk Management, Jun 2005, Vol. 1, Issue 2, pp. 1-15.Chernozhukov, V., and L. Umantsev, (2001), “Conditional Value-at-Risk: Aspects of Modeling and Estimation,” Empirical Economics, 2001, Vol. 26, Issue 1, pp. 271-292.Christoffersen, P., J. Hahn, and A. Inoue (2001), “Testing and comparing Value at Risk measures”, Journal of Empirical Finance, Jul 2001, Vol. 8, Issue 3, pp. 325-342.Diamond, D. W., and R, G. Rajan (2009), “Fear of Fire Sales and the Credit Freeze”, 2009, NBER Working Paper No. 14925.Duffee, G.R. (1996), "Treasury Yields and Corporate Bond Yield Spreads: An Empirical Analysis”, 1996, Federal Reserve Board Working paper.Duffie, D. and J. Pan (1997), “An overview of value at risk”, Journal of Derivatives, Spring 1997, Vol. 4, No.3, pp. 7–49.Engle, R.F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation”, Econometrica, 1982, Vol. 50, Issue 4, pp. 987-1008.Engle, R.F. and S. Manganelli (2004), “CAViaR: Conditional Value at Risk by Quantile Regression”, Journal of Business & Economic Statistics, Oct 2004, Vol. 22, Issue 4, pp. 367-381.Estrella,A., and M. R. Turbin (2006), “The yield curve as a leading indicator: some practical issues”, Federal Reserve Bank of New York, Current Issues In Economic and Finance, July/August 2006, Vol. 12, No. 5, pp. 1-6.Goodhart, C. A. E., and A. D. Persaud (2008a), “How to Avoid the Next Crash”, Financial Times, 2008, January 30.── (2008b), “A Party Pooper’s Guide to Financial Stability”, Financial Times, 2008, June 5.Hanson, S., A. K. Kashyap, and J. C. Stein (2011), “A Macroprudential Approach to Financial Regulation,” Journal of Economic Perspectives, Winter 2011, Vol. 25, Issue 1, pp. 3-28.Harvey, C. R., and A. Siddique (1999), ‘‘Autoregressive Conditional Skewness”, Journal of Financial and Quantitative Analysis, Dec 1999, Vol. 34, No. 4, pp. 465–487. Hendricks, D. (1996), "Evaluation of Value-at-Risk Models Using Historical Data”, Federal Reserve Bank of New York, Economic Policy Review, 1996, Issue Apr, pp. 39-69.Hull, J. and A. White (1998), “Value at risk when daily changes in market variables are not normally distributed”, Journal of Derivatives, Spring 1998, Vol. 5, No. 3, pp. 9–19.Jackson, P., D.J. Maude and W. Peerraudin (1997), “Bank Capital and Value at Risk”, Journal of Derivatives, 1997, Vol. 4, 73-89.Jorion P. (2006), “Value at Risk - The New Benchmark for Managing Financial Risk”, The McGraw Hill Company, 2006, 3rd Edition.J. P. Morgan (1996), “RiskMetrics Technical Document”, 1996, 4th edition. New York.Koenker, R. and B. Park (1996), “An Interior Point Algorithm for Nonlinear Quantile Regression”, Journal of Econometrics, 1996, Vol. 71, Issue 1-2, pp. 265–283.Koenker, R., and G. W. Bassett (1978), “Regression Quantiles”, Econometrica, Jan 19878, Vol. 46, Issue 1, pp. 33-50.Kroner, K.F., K.P. Kneafsey, and S. Claessens (1995), “Forecasting Volatility in Commodity Markets”, Journal of Forecasting, 1995, Vol. 14, 77-95.Kuester, K., S. Mittnik, and M. S. Paolella (2006), “Value-at-Risk Prediction: A Comparison of Alternative Strategies”, Journal of Financial Econometrics, 2006, Vol. 4, No. 1, pp. 53–89.Lee, T. H., and B. Saltoglu (2004), “Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets:A Reality Check” Working paper.Pedersen, C. S., and S. E. Satchell (1998), “An Extended Family of Financial-Risk Measures”, Geneva Papers on Risk and Insurance Theory, 1998, Vol. 23, 89–117.Persaud A. (2009). “Macro-Prudential Regulation,” The World Bank Group, Jul 2009, Note No. 6.Rockinger, M., and E. Jondeau (2002), “Entropy Densities with an Application to Autoregressive Conditional Skewness and Kurtosis”, Journal of Econometrics, 2002, Vol. 106, Issue 1, pp. 119–142.Shleifer, A., and Vishny, R. W. (2010), "Unstable Banking," Journal of Financial Economics, Elsevier, Sep 2010, Vol. 97, Issue 3, pp. 306-318. Stein, J. C. (2010), “Monetary Policy as Financial-Stability Regulation”, 2010, NBER Working Paper No. 16883. Taylor, J. W. (1999), “A Quantile Regression Approach to Estimating the Distribution of Multiperiod Returns”, Journal of Derivatives, 1999, Vol. 7, Issue 1, 64–78.古永嘉、孫瑞霙、張美玲(2003)，「台灣股票報酬與匯率變動波動性外溢效果之再探討」，輔仁管理評論，2003年，第10卷，第2期，民92年，頁139-62。康信鴻、劉靜芳(1996)，「股票市場報酬率總體外匯風險之衡量」，企業管理學報，1996年，第39期，頁115-162。 藍麗惠、廖源星、林育志(2007)，「台灣金融機構之外匯風險」，中央經濟研究院經濟研究所，台灣經濟預測與政策，2007年，第38卷，第1期，頁127-151。 描述 碩士
國立政治大學
金融研究所
98352006
99資料來源 http://thesis.lib.nccu.edu.tw/record/#G0098352006 資料類型 thesis dc.contributor.advisor 李桐豪 zh_TW dc.contributor.author (Authors) 陳怡君 zh_TW dc.contributor.author (Authors) Chen, Yi Chun en_US dc.creator (作者) 陳怡君 zh_TW dc.creator (作者) Chen, Yi Chun en_US dc.date (日期) 2010 en_US dc.date.accessioned 4-Sep-2013 10:06:23 (UTC+8) - dc.date.available 4-Sep-2013 10:06:23 (UTC+8) - dc.date.issued (上傳時間) 4-Sep-2013 10:06:23 (UTC+8) - dc.identifier (Other Identifiers) G0098352006 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/59961 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融研究所 zh_TW dc.description (描述) 98352006 zh_TW dc.description (描述) 99 zh_TW dc.description.abstract (摘要) 本研究欲以分量迴歸的方法估計出台灣上市櫃金融控股公司的VaR、CoVaR及其對台灣金融市場的風險溢出，做為總體審慎監理原則下具有抗景氣特色之風險衡量參考指標。我們亦透過金控公司間之CoVaR，觀察金控公司間風險交互影響程度，盼可提供各金控公司做為個體審慎監理原則下風險管理之參考指標。本研究包含四大特色：一、運用前期市場資料可估計下期含有條件、共變、傳染、貢獻等特性之風險值，也就是CoVaR；二、透過各家金控對市場之∆CoVaR可觀察各金控公司系統風險貢獻程度差異；三、可觀察金控公司間相互交叉影響程度；四、運用金融機構特性預測未來系統風險。本研究以信用利差、長短期利差、流動性利差、匯率變動、加權指數報酬、隱含波動度變動、金控股價報酬等市場資料，透過分量迴歸估計損失機率為1%及5%之台灣金融控股公司VaR及CoVaR，並計算市場風險溢出─∆CoVaR研究各金融機構對系統風險之邊際貢獻。且以槓桿比率、市值帳面比、相對規模及資產負債不對稱比例等金融機構特性相關變數預測未來∆CoVaR，做為總體審慎監理原則下之風險管理參考指標。 本研究結果發現對台灣金融市場系統風險溢出貢獻較大的為玉山金、中信金、台新金及國泰金；國票金、永豐金、第一金及元大金則為系統風險溢出貢獻較低者。預測結果部分發現損失機率為1%時，以預測未來兩季之∆CoVaR效果較佳，預測損失機率為5%時則以預測未來三季之∆CoVaR效果較佳，顯示資料對不同的尾端損失機率分配影響顯現時間也不相同。 zh_TW dc.description.abstract (摘要) In this thesis, we intend to estimate Taiwanese financial holding company’s VaR, CoVaR and risk spillover to Taiwan financial market, and apply these to macroprudential risk management. In addition, we intend to develop crossover CoVaR between financial holding companies, offering risk management referral benchmark under microprudential principle to those companies.There are four features in this thesis. First, we use previous market data to estimate the conditional, comovement, contagion, and contributing VaR - CoVaR. Second, by ∆CoVaR of the institutions to the market, we can observe the holding companies’ systematic risk contribution. Third, we can observe the crossover effect of the holding companies. Last, we could use the characteristics of the institutions to predict future systematic risk. We particularly use credit spread, slope of yield curve, liquidity spread, change of exchange rate, return of market stock index, change of implied volatility and holding company’s stock price, by quantile regression, to predict the VaR and CoVaR of Taiwan’s holding companies when the probability to loss is 1% and 5%. Then we calculate market systematic risk spillover, ∆CoVaR, to observe the marginal systematic risk contribution of the institutions. Moreover, we use leverage, market-to-book ratio, relative size and maturity mismatch to predict forward ∆CoVaR, offering a reference to macroprudential risk management.Our empirical results show that in Taiwan financial market, the top four systematic risk contributors of holding companies are Esun Financial Holding, Chinatrust Financial Holding, Taishin Financial Holding and Cathay Financial Holding; the smallest ones are Waterland Financial Holding, Sino Financial Holding, First Financial Holding and Yuanta Financial Holding. We also find out that when loss probability is 1%, predicting ∆CoVaR after two seasons is better; when loss probability is 5%, predicting ∆CoVaR after three seasons is more significant. It shows that when the tail is different, the effect time is also different. en_US dc.description.tableofcontents 摘要 i英文摘要 ii目次 iv表次 vi圖次 viii第一章、緒論 1第一節、研究動機 1第二節、研究目的 4第二章、文獻探討 6第一節、個體審慎監理(Microprudential regulation) 6第二節、總體審慎監理(Macroprudential regulation) 7第三節、Value-at-risk 8第四節、分量迴歸模型 11第五節、分量迴歸應用於估計VaR 13第三章、研究方法 16第一節、CoVaR定義 16第二節、總資產帳面價值轉換市值 17第三節、CoVaR估計模型 17壹、模型一：隨時間變動CoVaR模型 18貳、模型二：隨時間變動自我相關CoVaR模型 19第四節、預測未來∆CoVaR 20第四章、實證結果與分析 22第一節、資料敘述與統計分析 22壹、研究對象 22貳、CoVaR估計模型之狀態變數 25参、未來∆CoVaR預測模型之狀態變數 27第二節、實證結果─CoVaR估計 31壹、模型一：隨時間變動CoVaR模型 31貳、模型二：隨時間變動自我相關CoVaR模型 41叁、模型一與模型二之比較 51肆、模型二VaR係數相關討論 53第三節、實證結果─未來∆CoVaR^(system|i)預測 55第五章、結論與建議 62第一節、結論 62第二節、未來研究方向 63參考文獻 65附錄A 68附錄B 69 zh_TW dc.format.extent 1840252 bytes - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0098352006 en_US dc.subject (關鍵詞) VaR zh_TW dc.subject (關鍵詞) CoVaR zh_TW dc.subject (關鍵詞) 分量迴歸 zh_TW dc.subject (關鍵詞) 總體審慎監理 zh_TW dc.subject (關鍵詞) VaR en_US dc.subject (關鍵詞) CoVaR en_US dc.subject (關鍵詞) Quantile Regression en_US dc.subject (關鍵詞) Macroprudential en_US dc.title (題名) CoVaR風險值對金融機構風險管理之重要性─以台灣金融控股公司為例 zh_TW dc.title (題名) The importance of CoVaR to financial institutions risk management from Taiwanese financial holding company’s perspective en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) Alexander, C. O., and C. T. Leigh(1997), “On the Covariances Matrices Used in Value at Risk Models,” Journal of Derivatives, Spring 1997, Vol. 4, No. 3, pp. 50-62.Baillie, R.T., and T. Bollerslev (1989), “The Message in Daily Exchange Rates: A Conditional Variance Tale.” Journal of Business and Economic Statistics, 1989, Vol. 7, Issue 3, pp. 297-305.Beder, T. S. (1995), “VAR: Seductive but Dangerous”, Financial Analysts Journal, 1995, Vol. 51, No. 5, September-October, pp. 12-24.Bollerslev, T. (1987), “A conditional heteroskedastic time series model for speculative prices and rates of returns”, Review of Economics and Statistics, 1987, Vol. 69, No. 3, pp. 542–547.Brunnermeier, M. K. (2009), “Deciphering the Liquidity and Credit Crunch 2007-08,”Journal of Economic Perspectives, Winter 2009, Vol. 23, No. 1, pp. 77-100.Brunnermeier, M. K., A. Crocket, G. Goodhart, A. D. Perssaud, and H. Shin (2009), “The Fundamental Principles of Financial Regulation,” 11th Geneva Report on the World Economy.Brunnermeier, M. K., and T. Adrian (2010), “CoVaR”, 2010, Working Paper.Chen M. Y. and J. E. Chen (2005), “Application of Quantile Regression to Estimation of Value at Risk”, Review of Financial Risk Management, Jun 2005, Vol. 1, Issue 2, pp. 1-15.Chernozhukov, V., and L. Umantsev, (2001), “Conditional Value-at-Risk: Aspects of Modeling and Estimation,” Empirical Economics, 2001, Vol. 26, Issue 1, pp. 271-292.Christoffersen, P., J. Hahn, and A. Inoue (2001), “Testing and comparing Value at Risk measures”, Journal of Empirical Finance, Jul 2001, Vol. 8, Issue 3, pp. 325-342.Diamond, D. W., and R, G. Rajan (2009), “Fear of Fire Sales and the Credit Freeze”, 2009, NBER Working Paper No. 14925.Duffee, G.R. (1996), "Treasury Yields and Corporate Bond Yield Spreads: An Empirical Analysis”, 1996, Federal Reserve Board Working paper.Duffie, D. and J. Pan (1997), “An overview of value at risk”, Journal of Derivatives, Spring 1997, Vol. 4, No.3, pp. 7–49.Engle, R.F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation”, Econometrica, 1982, Vol. 50, Issue 4, pp. 987-1008.Engle, R.F. and S. Manganelli (2004), “CAViaR: Conditional Value at Risk by Quantile Regression”, Journal of Business & Economic Statistics, Oct 2004, Vol. 22, Issue 4, pp. 367-381.Estrella,A., and M. R. Turbin (2006), “The yield curve as a leading indicator: some practical issues”, Federal Reserve Bank of New York, Current Issues In Economic and Finance, July/August 2006, Vol. 12, No. 5, pp. 1-6.Goodhart, C. A. E., and A. D. Persaud (2008a), “How to Avoid the Next Crash”, Financial Times, 2008, January 30.── (2008b), “A Party Pooper’s Guide to Financial Stability”, Financial Times, 2008, June 5.Hanson, S., A. K. Kashyap, and J. C. 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