Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/122139| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 徐士勛 | zh_TW |
| dc.contributor.author | 陳思成 | zh_TW |
| dc.contributor.author | Chen, Szu-Cheng | en_US |
| dc.creator | 陳思成 | zh_TW |
| dc.creator | Chen, Szu-Cheng | en_US |
| dc.date | 2018 | en_US |
| dc.date.accessioned | 2019-01-23T07:29:54Z | - |
| dc.date.available | 2019-01-23T07:29:54Z | - |
| dc.date.issued | 2019-01-23T07:29:54Z | - |
| dc.identifier | G0105258016 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/122139 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 經濟學系 | zh_TW |
| dc.description | 105258016 | zh_TW |
| dc.description.abstract | 本文應本文應用Hädle et al. (2016) 提出FRM (Financial Risk Meter) 指標,建構亞洲金融系統風險的評估模型,並進一步分析FRM這個創新的金融系統 風險評估指標在亞洲市場的適用性。Härdle et al. (2017) 提出的FRM所使用的模型是以美國前100大市值的金融機構作為基礎,而本文則以亞洲區前75大的金融機構與台灣前32大金融機構作為基礎, 編制亞洲區與台灣的FRM指標。我們所採用的計量模型是應用 Tibshirani et al. (1996) 提出的Lasso方法,觀察其中懲罰參數λ 的變化, 並納入 Li and Zhu (2008) 針對Lasso所提出的分量迴歸模型, 進而分析尾端風險對於整個金融體系的影響。我們發現台灣 FRM 指標相對而言與國際連動性較高, 而亞洲 FRM 指標連 動性相對低; 此結果因為各國金融體系的大小、與監管程度不同而有所差異。 | zh_TW |
| dc.description.tableofcontents | 1 緒論 1\n2 Financial Risk Meter 實證模型建構 4\n2.1 Lasso線性分量迴歸模型 4\n2.2 懲罰項λ建構FRM指標 6\n2.3 Lasso作為最適化方法 7\n2.4 影響懲罰項λ的主要因素 8\n2.5 影響懲罰項λ主要因素在FRM指標中所代表的意義 9\n3 實證方法 10 3.1 資料說明 11\n3.2 研究方法 13\n4 實證結果 15\n4.1 美國FRM指標 15\n4.2 台灣FRM指標 17\n4.3 亞洲FRM指標 18\n4.4 台灣與亞洲FRM指標比較 21\n4.5 FRM指標各分量比較 22\n5 結論 23 | zh_TW |
| dc.format.extent | 1751283 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0105258016 | en_US |
| dc.subject | 分量迴歸 | zh_TW |
| dc.subject | 系統風險 | zh_TW |
| dc.subject | 亞洲 | zh_TW |
| dc.subject | 台灣 | zh_TW |
| dc.subject | LASSO | en_US |
| dc.subject | Quantile | en_US |
| dc.subject | Systemic risk | en_US |
| dc.subject | FRM | en_US |
| dc.subject | Asia | en_US |
| dc.subject | Taiwan | en_US |
| dc.title | 以Lasso分量迴歸模型建構亞洲與台灣金融系統風險指標 | zh_TW |
| dc.title | Lasso Quantile Regression Model to Construct Asia and Taiwan Systemic Risk Measurement | en_US |
| dc.type | thesis | en_US |
| dc.relation.reference | 鍾經樊, (2011), “涵蓋信用風險、銀行間傳染風險、與流動性風險的台灣金融 系統風險量化模型”。 中央銀行季刊, 第三十三卷第二期, 13–39.\n俞明德 、馮立功 、陳韋達 、林逸苓, (2012), “金融系統流動性風險之評估”。 中央銀行季刊, 第三十四卷第三期, 3–50.\n陸婷、張明, (2017), “如何度量金融系统風險: 一個文獻述評”。 金融監管研究 院, 66, 1–15.\nAdrian, T. and Brunnermeier, M. K. (2016). CoVaR., American Economic Review., 106(7), 1705–1741.\nChen, K. and Chan, K. S. (2011). Subset ARMA Selection via the Adaptive Lasso., Statistics and Its Interface., 4, 197–205.\nChan-Lau, J. A. (2010). Balance Sheet Network Analysis of Too-Connected- to-Fail Risk in Global and Domestic Banking Systems., IMF Working Paper., 10/107, 229-242.\nFan, Y., Ha ̈rdle, W. K., Wang, W. and Zhu, L. (2016). Composite Quantile Regression for the Single-Index Model., Journal of Business economics and statistics., 1–42.\nGiglio, S., Kelly, B. and Pruitt, S. (2016). Systemic Risk and the Macroeconomy: An Empirical Evaluation., Journal of Financial Economics., 119, 457–471.\nGlasserman, P and Young, P. (2016). Contagion in Financial Networks., Journal of Economic Literature., 54(3), 779–831.\nHa ̈rdle, W. K., Wang, W. and Yu, L. (2016). TENET: Tail-Event Driven NETwork Risk., Journal of Econometrics., 192(2), 499–513.\nHa ̈rdle, W. K., Zbonakova, L., and Wang, W. (2016). Time Varying Quantile Lasso., SFB 649 Discussion Paper 2016-047, Sonderforschungsbereich 649, Humboldt Universita ̈ta ̈zu Berlin, Germany., 1–22.\nHa ̈rdle, W. K., Yu, L., Borke, T., Benschop, T. (2017). FRM: a Finan- cial Risk Meter Based on Penalizing Tail Events Occurrence., SFB 649 Discussion Paper 2017-003, Sonderforschungsbereich 649, Hum- boldt Universita ̈ta ̈zu Berlin, Germany., 1–40.\nHautsch, N., Schaumburg, J. and Schienle, M. (2015). Financial Network Systemic Risk Contributions., Review of Finance., 19(2), 685–738.\nLi, Y. and Zhu, J. (2008). L1-Norm Quantile Regression., Journal of Com- putational and Graphical Statistics., 17(1), 1–23.\nRennhack, R. (2010). Balance Sheet Network Analysis of Too-Connected-to- Fail Risk in Global and Domestic Banking Systems., Statistics and Its | zh_TW |
| dc.identifier.doi | 10.6814/THE.NCCU.ECONO.002.2019.F06 | en_US |
| item.fulltext | With Fulltext | - |
| item.grantfulltext | open | - |
| item.cerifentitytype | Publications | - |
| item.openairetype | thesis | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| Appears in Collections: | 學位論文 | |
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| File | Size | Format | |
|---|---|---|---|
| 801601.pdf | 1.71 MB | Adobe PDF2 | View/Open |
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