政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/59961
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 111314/142224 (78%)
Visitors : 48361608      Online Users : 968
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
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/59961


    Title: 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
    Authors: 陳怡君
    Chen, Yi Chun
    Contributors: 李桐豪
    陳怡君
    Chen, Yi Chun
    Keywords: VaR
    CoVaR
    分量迴歸
    總體審慎監理
    VaR
    CoVaR
    Quantile Regression
    Macroprudential
    Date: 2010
    Issue Date: 2013-09-04 10:06:23 (UTC+8)
    Abstract: 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.
    Reference: 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。
    Description: 碩士
    國立政治大學
    金融研究所
    98352006
    99
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0098352006
    Data Type: thesis
    Appears in Collections:[Department of Money and Banking] Theses

    Files in This Item:

    File Description SizeFormat
    200601.pdf1797KbAdobe PDF21135View/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