The Study of Robustness of Credit Value at Risk under The One Factor Model

碩士 === 東吳大學 === 商用數學系 === 94 === Although Value at Risk (VaR) is only a simple figure, it can measure the unexpected loss. It also can be the index of the risk control and express various kinds of risks clear. So the Basle Committee on Banking Supervision and many financial institutions place import...

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Main Authors:	Ya-mei Chang, 張雅媚
Other Authors:	Yi-ping Chang
Format:	Others
Language:	zh-TW
Published:	2006
Online Access:	http://ndltd.ncl.edu.tw/handle/36982833697422623128

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spelling	ndltd-TW-094SCU053140062015-10-13T16:35:38Z http://ndltd.ncl.edu.tw/handle/36982833697422623128 The Study of Robustness of Credit Value at Risk under The One Factor Model 單因子模型下之信用風險值的穩健性探討 Ya-mei Chang 張雅媚碩士東吳大學商用數學系 94 Although Value at Risk (VaR) is only a simple figure, it can measure the unexpected loss. It also can be the index of the risk control and express various kinds of risks clear. So the Basle Committee on Banking Supervision and many financial institutions place importance on Value at Risk. We use the maximum likelihood estimation to estimate Value at Risk under the one factor model. In the real world, the distribution of the default data is unknown. We estimate Value at Risk by using the different distribution of the systematic random effect and error variables and compare with real Value at Risk. If the estimate value is different, we study the robustness of Value at Risk under the one factor model. Yi-ping Chang Ming-chin Hung 張揖平洪明欽 2006 學位論文 ; thesis 39 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 東吳大學 === 商用數學系 === 94 === Although Value at Risk (VaR) is only a simple figure, it can measure the unexpected loss. It also can be the index of the risk control and express various kinds of risks clear. So the Basle Committee on Banking Supervision and many financial institutions place importance on Value at Risk. We use the maximum likelihood estimation to estimate Value at Risk under the one factor model. In the real world, the distribution of the default data is unknown. We estimate Value at Risk by using the different distribution of the systematic random effect and error variables and compare with real Value at Risk. If the estimate value is different, we study the robustness of Value at Risk under the one factor model.
author2	Yi-ping Chang
author_facet	Yi-ping Chang Ya-mei Chang 張雅媚
author	Ya-mei Chang 張雅媚
spellingShingle	Ya-mei Chang 張雅媚 The Study of Robustness of Credit Value at Risk under The One Factor Model
author_sort	Ya-mei Chang
title	The Study of Robustness of Credit Value at Risk under The One Factor Model
title_short	The Study of Robustness of Credit Value at Risk under The One Factor Model
title_full	The Study of Robustness of Credit Value at Risk under The One Factor Model
title_fullStr	The Study of Robustness of Credit Value at Risk under The One Factor Model
title_full_unstemmed	The Study of Robustness of Credit Value at Risk under The One Factor Model
title_sort	study of robustness of credit value at risk under the one factor model
publishDate	2006
url	http://ndltd.ncl.edu.tw/handle/36982833697422623128
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The Study of Robustness of Credit Value at Risk under The One Factor Model

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