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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李賢源 | |
dc.contributor.author | Yu-Yi Liang | en |
dc.contributor.author | 梁友譯 | zh_TW |
dc.date.accessioned | 2021-06-15T12:56:47Z | - |
dc.date.available | 2021-09-13 | |
dc.date.copyright | 2016-09-13 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-07-15 | |
dc.identifier.citation | 1. 陳松男 金融工程學
2. 呂建霖 (2005), 信用衍生性商品-擔保債權憑證之評價與分析 3. 劉大安 (2006), 信用投資組合法及信用循環指標法之比較研究 4. Altman, Edward. I. (1968), “Financial ratios, discriminant analysis and the Prediction of corporate bankruptcy”, Journal of Finance 23(4), pages 589-609 5. Altman, E.I., Haldeman, R.G., and Narayanan, P. (1977), “ZATA analysis: A new model to identify bankruptcy risk of corporations, Journal of Banking and Finance, pages 29-54. 6. Agrawal, D., N. Arora, and J.R. Bohn. (2004), “Parsimony in Practice: An EDF-Based Model of Credit Spreads”, White article, Moody's KMV 7. Black, F., and M. Scholes. (1973), “The Pricing of Options and Corporate Liabilities”, Journal of Political Economy, 81, pages 637-659. 8. Duffie, D. and K. Singleton (1999), “Modeling term structure of defaultable bonds”, Review of Financial Studies, 12, pages 687-720. 9. Jarrow, R. and S. Turnbull(1995), “Pricing derivatives on financial securities subject to credit risk,” Journal of Finance 50, pages 53- 85. 10. Jarrow, R., D. Lando, and S. Turnbull(1997), “A Markov model for the term structure of credit spread,” Review of Financial Studies 10, pages 481- 523. 11. Kealhofer, Stephen, (2003) “Quantifying Credit Risk I: Default Prediction.”, Financial Analysts Journal, pages 30-44. 12. Kay Giesecke (2008), “Portfolio Credit Risk: Top-Down vs. Bottom-Up Approaches”, Frontiers in Quantitative Finance: Credit Risk and Volatility Modeling, R. Cont (Ed.), Wiley 13. Ohlson, J. A. (1980), “Financial Ratios and the Probabilistic Prediction of Bankruptcy”, Journal of Accounting Research, pages 109-31. 14. Robert C. Merton (1974), “ON THE PRICING OF CORPORATE DEBT: THE RISK STRUCTURE OF INTEREST RATES”, Journal of Finance, pages 449–470 15. Robert A. Jarrow, and Fan Yu (2001), “Counterparty Risk and the Pricing of Defaultable Securities”, Journal of Finance, pages 1765–1799. 16. Shumway Tyler (2001), “Forecasting Bankruptcy More Accurately: A Simple Hazard Model,” The Journal of Business 74(1), pages 101-124. 17. Sreedhar T. Bharath and Tyler Shumway (2008), “Forecasting Default with the Merton Distance to Default Model”, The Review of Financial Studies, pages 1339- 1369. 18. Zan Li, Jing Zhang, Christopher Crossen (2012), “A Model-Based Approach to Constructing Corporate Bond Portfolios”, The Journal of Fixed Income, pages 57-71. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50760 | - |
dc.description.abstract | 本文主要說明如何從債券指數中投資被市場低估的債券,建構類似於原來指數,但報酬率較佳、風險較低的投資組合,以結構式模型(Structural-Form Model)的Merton model(1974)得出公司違約機率,再配合違約時的損失程度、發行人的系統性風險程度與經濟環境的相關性等相關參數評估信用溢酬。
當得出每一檔債券的信用溢酬後,接著衡量在每單位的預期損失下,有多少的超額信用風險溢酬,同時將指數中的成份債券依不同的存續期間做分類,每類中的債券再依超額信用風險溢酬做排序,並投資前20%的債券做為我們的投資組合。 實證利用Bloomberg的科技類股、原物料類股債券指數做benchmark,針對我們的投資組合作回溯測試,無論是科技類股或原物料類股,其報酬均優於指數、風險也較小,每年報酬比指數更好且更加穩定,同時能有效降低最大回檔時的損失,也能夠提高Sharpe ratio,尾端風險也顯著下降,特別是在風險較高、年化報酬較低的原物料類股有更佳的效果。 | zh_TW |
dc.description.abstract | This article shows how to invest the undervalued bonds from different indices. We want to construct the portfolios which is similar to indices, but we can gain higher return and lower risk portfolios. By adopting Merton model to obtain the default probability and other parameters like loss given default(LGD) and systematic risk, we can evaluate the credit spread.
When obtaining every credit spreads of bonds, we should measure how much excess credit spread we can get in one unit of loss given default. Meantime, we must use different durations to classify bonds, and then sort bonds by excess credit spreads in every duration. Finally, we invest the top 20% bonds of excess credit spreads to construct portfolios. We use Bloomberg technology and material bond indices as benchmarks. The return and risk of our model portfolios are significantly superior to benchmarks in our back-testing. We find that year-to-year returns of our model portfolios are better and more stable. Also, our model portfolios can reduce the max drawdowns effectively, improve Sharpe ratio, and lower tail risks. Especially, our model has better effect on the material bond index because there are higher risk and lower return in this index. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T12:56:47Z (GMT). No. of bitstreams: 1 ntu-105-R03723051-1.pdf: 721369 bytes, checksum: cd6f8cac91192da877135d5f27c46760 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | 第一章 緒論 2
第二章 文獻回顧 3 第三章 研究方法 13 第一節 Option-implied違約機率模型 14 第二節 債券信用溢酬(Fair-Value Spread, FVS)評估模型 15 第四章 實證方法 18 第一節 資料選取 18 第二節 投資策略 19 第五章 實證結果 22 第一節 報酬率、波動度、Sharpe Ratio比較 22 第二節 科技類股債券投資組合績效比較 23 第三節 原物料類股債券投資組合績效比較 24 第四節 Year-to-Year績效比較 25 第五節 Tail Risk比較 27 第六章 結論與建議 28 參考文獻 30 | |
dc.language.iso | zh-TW | |
dc.title | 信用溢酬債券投資模型:以科技及原物料類股為例 | zh_TW |
dc.title | Using Credit Spread Model to Build Bond Portfolios:A Study of Technology and Material Bond Indices | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳彥行,謝承熹 | |
dc.subject.keyword | 信用違約模型,債券交易策略,Merton選擇權模型,KMV模型,信用違約交換,回溯測試, | zh_TW |
dc.subject.keyword | Credit Default Model,Bond Trading Strategy,Merton Option Model,KMV Model,CDS,Back-testing, | en |
dc.relation.page | 31 | |
dc.identifier.doi | 10.6342/NTU201600934 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2016-07-15 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
顯示於系所單位: | 財務金融學系 |
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