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題名 擔保房貸憑證(CMOs)之評價：應用機器學習方法預測提前還款率
Pricing Collateralized Mortgage Obligations: Using Machine Learning to Predict Prepayment Rate作者 吳海棠
Wu, Hai-Tang貢獻者 林士貴<br>莊明哲
Lin, Shih-Kuei<br>Chuang, Ming-Che
吳海棠
Wu, Hai-Tang關鍵詞 擔保房貸憑證
提前還款模型
機器學習
Hull & White 利率模型
Collateralized mortgage obligations
Prepayment model
Machine learning
Hull & White Interest Rate Model日期 2019 上傳時間 7-Aug-2019 16:13:31 (UTC+8) 摘要 本研究使用機器學習模型預測擔保房貸憑證(Collateral Mortgage Obligation, CMOs)之提前還款率並評價，且和两种傳统的提前還款率的模型進行比較。第一種是靜態的提前還款模型，使用聯邦住宅管理局經驗法(Federal Home Administration, FHA)、條件提前還款率(Conditional Prepayment Rate, CPR)或以美國公共證券協會(The Public Securities Association, PSA)提前還款基準作為提前還款預測的模型。第二種是動態的提前還款，由美國儲蓄機構管理局(Office Thrift Supervision, OTS)提出的30年期固定利率房屋抵押貸款動態提前還款模型。由於評價CMOs時會將現金流進行折現，且票面利息的計算會使用到倫敦銀行同業隔夜拆款利率(London Interbank Offered Rate, Libor)。因此，本研究使用Hull & White利率模型模擬即期利率路徑，再通過遠期利率協定(Forward Rate Agreement, FRA)轉換成遠期Libor的路徑計算現金流。通過Fannie Mae發行的一檔CMOs商品的公開資料用於實證，實證結果證實機器學習預測提前還款優於傳统模型。
In this paper we predict the prepayment rate and price the Collateral Mortgage Obligation by using Machine Learning, and compare the results with two traditional prepayment models. The first one is static prepayment model, which uses Federal Housing Administration (FHA) Model, Conditional Prepayment Rate (CPR) Model or the Public Securities Association (PSA) prepayment benchmark for the prepayment model. The second one is the dynamic prepayment model from Office Thrift Supervision (OTS), which uses 30 years fixed mortgage rate. Because the high relationship between coupon rate of CMO trench and Libor rate, this paper uses Hull & White interest rate model to simulate the spot interest rate as the discount rate, and converts it to the Libor rate with the help of Forward Rate Agreement (FRA). The empirical analysis based on a CMOs issued by Fannie Mae illustrated that for Machine Learning, the efficiency in predicting the prepayment rate is better than traditional models.參考文獻 中文文獻1. 王立偉，2008，「提前還款對住房抵押貸款支持證券定價影響的效果」，大連理工大學金融工程系碩士班碩士論文。2高心怡，2000，「結合HULL-WHITE利率模型與PHM提前清償模型評價CMO利率衍生性商品」，國立台灣大學財務金融系碩士班碩士論文。3.張繼文，2010，「擔保房貸憑證（CMOs）評價-以BGM利率模型為例」，國立政治大學金融系碩士班碩士論文。4.張憲明，2018，「擔保房貸憑證（CMOs）之評價：應用類神經網路預測提前還款率」，國立政治大學金融系碩士班碩士論文。5.廖伯媛，2001，「不動產抵押貸款證券化之分析與評價」，國立政治大學金融系碩士班碩士論文。6.劉展宏、張金鶚，2001，「購屋貸款提前清償行為之研究」，住宅學報，10 卷 1 期：29~49。英文文獻1. Andreas, K, and Rudi, Z, 2008,”A Hybrid-Form Model for the Prepayment-Risk-Neutral Valuation of Mortgage-Backed Securities”, The International Journal of Theoretical and Applied Finance, 11, pp.635-6562. Clauretie and Sirmans, 1999, “Real Estate Finance Theory Practice”, Longman Higher Education Division ; 3rd edition3. Deng Y., 1977, ”Mortgage Termination: An Empirical Hazard Model with Stochastic Term Structure ”, Journal of Real Estate Finance and Economics,Vol.14,pp.309-3314. Dunn, K. B. and McConnell, J. J. (1981), “Valuation of GINNIE MAE Mortgage-Backed Securities,” Journal of Finance, Vol.36, pp.599-6165. Dunn, K. B. and McConnell, J. J. (1981), “A Compare of Alternative Models for Pricing GINNIE MAE Mortgage-Back Securities,” Journal of Finance Vol.36, pp.471-484.6. Green ,J , and J. B. Shoven ,1983,”The Effect of Interest Rates on Mortgage Prepayments”, Journal of Money, Credit and Banking 18,41-597. Gurrieri , M. Nakabayashi & T. Wong (2009), “Calibration methods of Hull–White model”, Working paper.8. Jone, J. Mcconnell and Manoj Singh,1993,”Valuation and Analysis of Collateralized Mortgage Obligations”, Management Science, Vol.39, No. 6,pp.692-7099. Jone J. Mcconnell and Manoj Singh, 1994,”Rational Prepayments and the Valuation of Collateralized Mortgage Obligations”, The Journal of Finance, Vol. 49, No. 3,pp.891-92110. Ronald w. Spahr and Mark A. Sunderman,”The Effect of Prepayment Modeling in Pricing Mortgage-Backed Securities”, Journal of Housing Research, Vol. 3,pp.381-40011. R Riksen , 2017,”Using Articial Neural Networks in the Calculation of Mortgage Prepayment Risk”, University of Amsterdam, Korteweg-de Vries Institute for Mathematics12. Scott F. Richard Roll, 1989,”Prepayments on Fixed-Rate Mortgage-Backed Securities”, Journal of Portfolio Management 15,pp.73-8213. Schwartz, Eduardo S., and Walter N. Torous, 1989, "Prepayment and the Valuation of Mortgage-Backed Securities," Journal of Finance, 44, 375-3914. Waller B. and Aiken M.,1998, “Predicting Prepayment of Residential Mortgages: A Neural Network Approach ”, Information and Management Sciences, 9, pp.37-44 描述 碩士
國立政治大學
金融學系
106352046資料來源 http://thesis.lib.nccu.edu.tw/record/#G0106352046 資料類型 thesis dc.contributor.advisor 林士貴<br>莊明哲 zh_TW dc.contributor.advisor Lin, Shih-Kuei<br>Chuang, Ming-Che en_US dc.contributor.author (Authors) 吳海棠 zh_TW dc.contributor.author (Authors) Wu, Hai-Tang en_US dc.creator (作者) 吳海棠 zh_TW dc.creator (作者) Wu, Hai-Tang en_US dc.date (日期) 2019 en_US dc.date.accessioned 7-Aug-2019 16:13:31 (UTC+8) - dc.date.available 7-Aug-2019 16:13:31 (UTC+8) - dc.date.issued (上傳時間) 7-Aug-2019 16:13:31 (UTC+8) - dc.identifier (Other Identifiers) G0106352046 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/124743 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 金融學系 zh_TW dc.description (描述) 106352046 zh_TW dc.description.abstract (摘要) 本研究使用機器學習模型預測擔保房貸憑證(Collateral Mortgage Obligation, CMOs)之提前還款率並評價，且和两种傳统的提前還款率的模型進行比較。第一種是靜態的提前還款模型，使用聯邦住宅管理局經驗法(Federal Home Administration, FHA)、條件提前還款率(Conditional Prepayment Rate, CPR)或以美國公共證券協會(The Public Securities Association, PSA)提前還款基準作為提前還款預測的模型。第二種是動態的提前還款，由美國儲蓄機構管理局(Office Thrift Supervision, OTS)提出的30年期固定利率房屋抵押貸款動態提前還款模型。由於評價CMOs時會將現金流進行折現，且票面利息的計算會使用到倫敦銀行同業隔夜拆款利率(London Interbank Offered Rate, Libor)。因此，本研究使用Hull & White利率模型模擬即期利率路徑，再通過遠期利率協定(Forward Rate Agreement, FRA)轉換成遠期Libor的路徑計算現金流。通過Fannie Mae發行的一檔CMOs商品的公開資料用於實證，實證結果證實機器學習預測提前還款優於傳统模型。 zh_TW dc.description.abstract (摘要) In this paper we predict the prepayment rate and price the Collateral Mortgage Obligation by using Machine Learning, and compare the results with two traditional prepayment models. The first one is static prepayment model, which uses Federal Housing Administration (FHA) Model, Conditional Prepayment Rate (CPR) Model or the Public Securities Association (PSA) prepayment benchmark for the prepayment model. The second one is the dynamic prepayment model from Office Thrift Supervision (OTS), which uses 30 years fixed mortgage rate. Because the high relationship between coupon rate of CMO trench and Libor rate, this paper uses Hull & White interest rate model to simulate the spot interest rate as the discount rate, and converts it to the Libor rate with the help of Forward Rate Agreement (FRA). The empirical analysis based on a CMOs issued by Fannie Mae illustrated that for Machine Learning, the efficiency in predicting the prepayment rate is better than traditional models. en_US dc.description.tableofcontents 第一章 緒論 31.1研究背景 31.2研究目的 51.3研究流程圖 6第二章 商品介紹 72.1轉支付證券(Pay-Through Securities) 72.2擔保房貸憑證(CMOs) 7第三章 文獻回顧 123.1 CMO商品 123.2利率模型 123.3 提前還款模型 13第四章 研究方法 154.1利率模型 154.1.1利率動態過程 154.1.2. 遠期LIBOR 174.1.3 模擬30年期公債殖利率 184.1.4模擬流程 194.2提前還款模型 224.2.1 聯邦住宅管理局經驗法 224.2.2條件提前還款模型 234.2.3 OTS提前還款模型 244.2.4機器學習提前還款模型 264.3現金流計算 28第五章 商品及契約介紹 305.1契約給付形式 305.2 商品契約說明 31第六章 實證分析 346.1 利率模擬 346.2不同提前還款模型預測結果 366.3 評價結果 426.3.1 FHA提前還款模型評價結果 436.3.2 CPR提前還款模型評價結果 446.3.3 OTS提前還款模型評價結果 456.3.4 機器學習提前還款模型評價結果 466.4敏感度分析 47第七章 結論與建議 49參考文獻 50 zh_TW dc.format.extent 2089803 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106352046 en_US dc.subject (關鍵詞) 擔保房貸憑證 zh_TW dc.subject (關鍵詞) 提前還款模型 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) Hull & White 利率模型 zh_TW dc.subject (關鍵詞) Collateralized mortgage obligations en_US dc.subject (關鍵詞) Prepayment model en_US dc.subject (關鍵詞) Machine learning en_US dc.subject (關鍵詞) Hull & White Interest Rate Model en_US dc.title (題名) 擔保房貸憑證(CMOs)之評價：應用機器學習方法預測提前還款率 zh_TW dc.title (題名) Pricing Collateralized Mortgage Obligations: Using Machine Learning to Predict Prepayment Rate en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文文獻1. 王立偉，2008，「提前還款對住房抵押貸款支持證券定價影響的效果」，大連理工大學金融工程系碩士班碩士論文。2高心怡，2000，「結合HULL-WHITE利率模型與PHM提前清償模型評價CMO利率衍生性商品」，國立台灣大學財務金融系碩士班碩士論文。3.張繼文，2010，「擔保房貸憑證（CMOs）評價-以BGM利率模型為例」，國立政治大學金融系碩士班碩士論文。4.張憲明，2018，「擔保房貸憑證（CMOs）之評價：應用類神經網路預測提前還款率」，國立政治大學金融系碩士班碩士論文。5.廖伯媛，2001，「不動產抵押貸款證券化之分析與評價」，國立政治大學金融系碩士班碩士論文。6.劉展宏、張金鶚，2001，「購屋貸款提前清償行為之研究」，住宅學報，10 卷 1 期：29~49。英文文獻1. Andreas, K, and Rudi, Z, 2008,”A Hybrid-Form Model for the Prepayment-Risk-Neutral Valuation of Mortgage-Backed Securities”, The International Journal of Theoretical and Applied Finance, 11, pp.635-6562. Clauretie and Sirmans, 1999, “Real Estate Finance Theory Practice”, Longman Higher Education Division ; 3rd edition3. Deng Y., 1977, ”Mortgage Termination: An Empirical Hazard Model with Stochastic Term Structure ”, Journal of Real Estate Finance and Economics,Vol.14,pp.309-3314. Dunn, K. B. and McConnell, J. J. (1981), “Valuation of GINNIE MAE Mortgage-Backed Securities,” Journal of Finance, Vol.36, pp.599-6165. Dunn, K. B. and McConnell, J. J. (1981), “A Compare of Alternative Models for Pricing GINNIE MAE Mortgage-Back Securities,” Journal of Finance Vol.36, pp.471-484.6. Green ,J , and J. B. Shoven ,1983,”The Effect of Interest Rates on Mortgage Prepayments”, Journal of Money, Credit and Banking 18,41-597. Gurrieri , M. Nakabayashi & T. Wong (2009), “Calibration methods of Hull–White model”, Working paper.8. Jone, J. Mcconnell and Manoj Singh,1993,”Valuation and Analysis of Collateralized Mortgage Obligations”, Management Science, Vol.39, No. 6,pp.692-7099. Jone J. Mcconnell and Manoj Singh, 1994,”Rational Prepayments and the Valuation of Collateralized Mortgage Obligations”, The Journal of Finance, Vol. 49, No. 3,pp.891-92110. Ronald w. Spahr and Mark A. Sunderman,”The Effect of Prepayment Modeling in Pricing Mortgage-Backed Securities”, Journal of Housing Research, Vol. 3,pp.381-40011. R Riksen , 2017,”Using Articial Neural Networks in the Calculation of Mortgage Prepayment Risk”, University of Amsterdam, Korteweg-de Vries Institute for Mathematics12. Scott F. Richard Roll, 1989,”Prepayments on Fixed-Rate Mortgage-Backed Securities”, Journal of Portfolio Management 15,pp.73-8213. Schwartz, Eduardo S., and Walter N. Torous, 1989, "Prepayment and the Valuation of Mortgage-Backed Securities," Journal of Finance, 44, 375-3914. Waller B. and Aiken M.,1998, “Predicting Prepayment of Residential Mortgages: A Neural Network Approach ”, Information and Management Sciences, 9, pp.37-44 zh_TW dc.identifier.doi (DOI) 10.6814/NCCU201900177 en_US
