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
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、...
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ndltd-CHENGCHI-G00983520062013-01-07T19:36:43Z 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 陳怡君 Chen, Yi Chun VaR CoVaR 分量迴歸 總體審慎監理 VaR CoVaR Quantile Regression Macroprudential 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. 國立政治大學 http://thesis.lib.nccu.edu.tw/cgi-bin/cdrfb3/gsweb.cgi?o=dstdcdr&i=sid=%22G0098352006%22. text 中文 Copyright © nccu library on behalf of the copyright holders |
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中文 |
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VaR CoVaR 分量迴歸 總體審慎監理 VaR CoVaR Quantile Regression Macroprudential |
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VaR CoVaR 分量迴歸 總體審慎監理 VaR CoVaR Quantile Regression Macroprudential 陳怡君 Chen, Yi Chun 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 |
description |
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.
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author |
陳怡君 Chen, Yi Chun |
author_facet |
陳怡君 Chen, Yi Chun |
author_sort |
陳怡君 |
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 |
title_short |
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 |
title_full |
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 |
title_fullStr |
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 |
title_full_unstemmed |
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 |
title_sort |
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 |
publisher |
國立政治大學 |
url |
http://thesis.lib.nccu.edu.tw/cgi-bin/cdrfb3/gsweb.cgi?o=dstdcdr&i=sid=%22G0098352006%22. |
work_keys_str_mv |
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