Research Article
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Year 2024, Volume: 11 Issue: 1, 43 - 59, 02.06.2024

Sukriye Tuysuz

https://doi.org/10.17261/Pressacademia.2024.1896

Abstract

References

  • S. Agarwal, Y. Chang, and A. Yavaz (2012). Adverse selection in mortgage securitization. Journal of Financial Economics, 105, 640–660.
  • V. Agarwal and R. Taffler (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, 32(8), 1541–1551.
  • B. Ambrose and C.A. Capone (2000). The hazard rates of first and second default. Journal of Real Estate Finance and Economics, 20(3), 275-293.
  • B. Ambrose and A.B. Sanders (2003). Commercial mortgage-backed securities: prepayment and default. Journal of Real Estate Finance and Economics, 26(2-3), 179–196.
  • M.Y. An and Z. Qi (2012). Competing Risks models using mortgage duration data under proportional hazards assumption. Journal of Real Estate Research 34(1). 15-29.
  • P. Bajari, S. Chu, and M. Park (1997). An Empirical Model of Subprime Mortgage Default from 2000 to 2007. Working Paper (2008).
  • N. Bhutta, J. Dokko, and H. Shan (2010). The Depth of Negative Equity and Mortgage Default Decisions. Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System 2010-35.
  • L. Breiman, (2001). Random forests. Machine Learning, 45, 5–32.
  • M. Brennan and E.S. Schwartz, (1985). Determinants of GNMA mortgage prices. AREUEA Journal, 13, 291-302.
  • JY Campbell and JF. Cocco (2015). A model of mortgage default. Journal of Finance 70(4), 1495–1554.
  • T.S. Campbell and J.K. Dietrich (1983). The determinants of default on insured conventional residential mortgage loans. Journal of Finance 38(5), 1569–1581.
  • R.-R. Chen (1996). Understanding and managing ınterest rate risks. World Scientific- Singapore.
  • S.C. Cheng, J.P. Fine, and L.J. Wei (1998). Prediction of cumulative ıncidence function under the proportional hazards model. Biometrics, 54, 219–228.
  • B.A. Ciochetti et al. (2002). The termination of mortgage contracts through prepayment and default in the commercial mortgage markets: a proportional hazard approach with competing risks. Real Estate Economics, 30(4), 304-321.
  • J.M. Clapp, Y. Deng, and X. An. (2006). Unobserved heterogeneity in models of competing mortgage termination risks. Real Estate Economics 34(2), 243-273.
  • J.M. Clapp, J.P. Harding, and M. LaCour-Little (2000). Expected mobility: Part of the prepayment puzzle. The Journal of Fixed Income, 10(1), 68–78.
  • J.M. Clapp et al. (2001). Movers and shuckers: ınterdependent prepayment decisions. Real Estate Economics, 29(3), 411–450.
  • M. Consalvi and G.S. di Freca (2010). Measuring prepayment risk: an application to UniCredit Family Financing. Tech. Rep. Working Paper Series, UniCredit Universities.
  • Y. Deng, J.M. Quigley, and R. van Order (2000). Mortgage terminations, heterogeneity and the exercise of mortgage options. Econometrica, 68(2), 275-307.
  • K. Dunn and J. McConnell (1981). Valuation of GNMA mortgage-backed securities. Journal of Finance, 36, 599–616.
  • R.M. Dunsky and T.S.Y. Ho (2007). Valuing fixed rate mortgage loans with default and prepayment options. Journal of Fixed Income, 16(4), 7–31.
  • R. Elie et al (2002). A Model of Prepayment for the French Residential Loan Market. Working Paper Groupe de Recherche Operationnelle, Credit Lyonnais http://gro.creditlyonnais.fr.
  • R. Elul et al. (2010). What ’triggers’ mortgage default? The American Economic Review, Papers and Proceedings 200(2), 490–494.
  • D. Faraggi and R. Simon (1995). A neural network model for survival data. Statistics in Medicine, 14, 73–82.
  • J.P. Fine and R.J. Gray (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of American Statistics Association, 94, 496–509.
  • T.A. Gerds et al (2013). Estimating a time-dependent concordance index for survival prediction models with covariate dependent censoring. In: Statistics in Medicine, 32(13), 2173–2184.
  • L.S Goodman et al. (2010). Negative equity trumps unemployment in predicting defaults. Journal of Fixed Income, 19(4), 67–72.
  • E. Graf et al. (1999). Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, 18(17-18), 2529–2545.
  • R.J. Gray (1988). A class of K-sample tests for comparing the cumulative incidence of a competing risk. Annals of Statistics, 16, 1141–1154.
  • J. Green and J. Shoven (1986). The effects of interest rates on mortgage prepayments. Journal of Money, Credit and Banking, 18 (1986), 41–59.
  • L. Guiso, P. Sapienza, and L. Zingales (2013). The determinants of attitudes toward strategic default on mortgages. Journal of Finance, 68(4), 1473– 1515.
  • J. Gyourko and J. Tracy (2014). Reconciling theory and empirics on the role of unemployment in mortgage default. Journal of Urban Economics, 80, 87–96.
  • O. Hamidi et al (2017). Application of random survival forest for competing risks in prediction of cumulative incidence function for progression to AIDS. Epidemiology Biostatistics and Public Health 14(4), 323-241.
  • L. Hayre. (2003). Prepayment modeling and valuation of Dutch mortgages. The Journal of Fixed Income, 12, 25–47.
  • J.V. Hoff (1996). Adjustable and fixed rate mortgage termination, option values and local market conditions: An empirical analysis. Real Estate Economics, 24(3), 379–406.
  • H. Ishwaran et al. (2014). Random survival forests for competing risks. Biostatistics, 15(4). 757–773.
  • H. Ishwaran et al. (2010). Random survival forests for high-dimensional data. Statistical Analysis and Data Mining, 4, 115–132.
  • J.B. Kau et al. (1993). Option theory and floating-rate securities with a comparison of adjustable and fixed-rate mortgages. Journal of Business, 66(4), 595–618.
  • M. LaCour-Little (2008). Mortgage termination risk: a review of the recent literature. Journal of Real Estate Literature, 16(3), 295–326.
  • D. Lando and T.M. Skodeberg (2002). Analyzing rating transitions and rating drift with continuous observations. Journal of Banking and Finance, 26(2-3), 423–444.
  • E.C. Lawrence, L.D. Smith, and M. Rhoades (1992). An analysis of default risk in mobile home credit. Journal of Banking and Finance 16(2), 299–312.
  • C. Lee, J. Yoon, and M. van der Schaar (2019). Dynamic-deephit: a deep learning approach for dynamic survival analysis with competing risks based on longitudinal data. IEEE Ttransactions on Biomedical Engineering 20(5), 1–12.
  • C. Lee et al. (2018). Deephit: A deep learning approach to survival analysis with competing risk. Procedia, 32th AAAI Conf. Artif. Intell, 2314–2321.
  • A. Levin and A. Davidson (2005). Prepayment and option adjusted valuation of MBS. The Journal of Portfolio Management 31(4), 286-299.
  • Z. Li et al. (2019). Predicting prepayment and default risks of unsecured consumer loans in online lending. Emerging Markets Finance and Trade 55(1), 118–132.
  • F. Longstaff (2002). Optimal recursive refinancing and the valuation of mortgage-backed securities. Working paper, University of California, Los Angeles.
  • M. Lunn and D. McNeil. (1995). Applying Cox regression to competing risks. Biometrics, 51, 524–532.
  • R. Lydon and Y. McCarthy (2013). What lies beneath? Understanding recent trends in Irish mortgage arrears. The Economic and Social Review, Economic and Social Studies, 44(1), 117–150.
  • C. Mayer, K. Pence, and S. Sherlund (2009). The rise in mortgage defaults. Journal of Economic Perspectives, 23(1), 27–50.
  • F. McCann (2014). Modelling default transitions in the UK mortgage market. Central Bank of Ireland- Research Technical Paper 17/RT/14.
  • D.C. Nijescu (2012). Prepayment risk, impact on credit products. Theoretical and Applied Economics,19(8), 53–62.
  • A.D. Pavlov (2001). Competing risks of mortgage termination: Who refinances, who moves, and who defaults? The Journal of Real Estate Finance and Economics, 23(2), 185–211.
  • S. Richard and R. Roll (1989). Prepayments on fixed-rate mortgage-backed securities. Journal of Portfolio Management, 15, 73–82.
  • T.H. Scheike and M-J. Zhang (2011). Analyzing competing risk data using the R timereg package. Journal of Statistical Software 38(2), 1–15.
  • T.H. Scheike and M.J. Zhang (2002). An additive-multiplicative Cox-Aalen model. Scandinavian Journal of Statistics in Medicine, 28, 75–88.
  • T.H. Scheike and M.J. Zhang (2003). Extensions and applications of the Cox-Aalen survival models. Biometrics 59, 1033–1045.
  • E.S. Schwartz and W.N. Torous (2003). Commercial office space: tests of a real options model with competitive ınteractions. Working Paper, University of California, Los Angeles.
  • Y. Shen and S.C. Cheng (1999). Confidence bands for cumulative ıncidence curves under the additive risk model. Biometrika, 55, 1093–1100.
  • L.D. Smith, E.C. Lawrence, and S.M. Sanchez (2007). A comprehensive model for managing credit risk on home mortgage portfolios. Decision Sciences, 27(2), 291–317.
  • R. Stanton and N. Wallace (2011). The bear lair: Index credit default swaps and the subprime mortgage crisis. Review of Financial Studies 24(10), 3250–3280.
  • K.D. Vandell (1978). Default risk under alternativemortgage ınstruments. The Journal of Finance, 33(5), 1279–1296.
  • K.D. Vandell (1995). How ruthless is mortgage default? A review and synthesis of the evidence” Journal of Housing Research, 6(2), 245–264.
  • M. Wolbers, M.T. Koller, and J.C. Witteman (2013). Concordance for prognostic models with competing risks. Research Report, University of Copenhagen, Department of Biostatistics 3

PREPAYMENT AND DEFAULT RISK: A REVIEW

Year 2024, Volume: 11 Issue: 1, 43 - 59, 02.06.2024

Sukriye Tuysuz

https://doi.org/10.17261/Pressacademia.2024.1896

Abstract

Purpose- The main purpose of this article is to make a comprehensive review of existing studies on prepayment and default (competing risk). This review enables to shed light on the main determinant of prepayment and default as well as on methods used to model competing risk.
Methodology- A comprehensive review of existing studies/articles.
Findings- More recently proposed machine learning methods (Random Survival Forest and Random Competing Risks Forests, as well as the DeepHit model and Dynamic DeepHit model) enable to take into account the complex/no-linear response of prepayment and default to their determinant more efficiently.
Conclusion- To model properly/correctly the prepayment and default risks it is important to consider the fact that the exercise of the prepayment option brings an end to the default option, and vice versa. These both risks should be modelled together: competing risk. Furthermore, models/methods accounting the complex/no-linear impact of explanatory variables on prepayment and default risks should be used; such as the Random Survival Forest and Random Competing Risks Forests, as well as the DeepHit model and Dynamic DeepHit model.

Keywords

Prepayment risk default risk multinomial logit models multi-state model random survival forest Dynamic DeepHit model

References

  • S. Agarwal, Y. Chang, and A. Yavaz (2012). Adverse selection in mortgage securitization. Journal of Financial Economics, 105, 640–660.
  • V. Agarwal and R. Taffler (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking and Finance, 32(8), 1541–1551.
  • B. Ambrose and C.A. Capone (2000). The hazard rates of first and second default. Journal of Real Estate Finance and Economics, 20(3), 275-293.
  • B. Ambrose and A.B. Sanders (2003). Commercial mortgage-backed securities: prepayment and default. Journal of Real Estate Finance and Economics, 26(2-3), 179–196.
  • M.Y. An and Z. Qi (2012). Competing Risks models using mortgage duration data under proportional hazards assumption. Journal of Real Estate Research 34(1). 15-29.
  • P. Bajari, S. Chu, and M. Park (1997). An Empirical Model of Subprime Mortgage Default from 2000 to 2007. Working Paper (2008).
  • N. Bhutta, J. Dokko, and H. Shan (2010). The Depth of Negative Equity and Mortgage Default Decisions. Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System 2010-35.
  • L. Breiman, (2001). Random forests. Machine Learning, 45, 5–32.
  • M. Brennan and E.S. Schwartz, (1985). Determinants of GNMA mortgage prices. AREUEA Journal, 13, 291-302.
  • JY Campbell and JF. Cocco (2015). A model of mortgage default. Journal of Finance 70(4), 1495–1554.
  • T.S. Campbell and J.K. Dietrich (1983). The determinants of default on insured conventional residential mortgage loans. Journal of Finance 38(5), 1569–1581.
  • R.-R. Chen (1996). Understanding and managing ınterest rate risks. World Scientific- Singapore.
  • S.C. Cheng, J.P. Fine, and L.J. Wei (1998). Prediction of cumulative ıncidence function under the proportional hazards model. Biometrics, 54, 219–228.
  • B.A. Ciochetti et al. (2002). The termination of mortgage contracts through prepayment and default in the commercial mortgage markets: a proportional hazard approach with competing risks. Real Estate Economics, 30(4), 304-321.
  • J.M. Clapp, Y. Deng, and X. An. (2006). Unobserved heterogeneity in models of competing mortgage termination risks. Real Estate Economics 34(2), 243-273.
  • J.M. Clapp, J.P. Harding, and M. LaCour-Little (2000). Expected mobility: Part of the prepayment puzzle. The Journal of Fixed Income, 10(1), 68–78.
  • J.M. Clapp et al. (2001). Movers and shuckers: ınterdependent prepayment decisions. Real Estate Economics, 29(3), 411–450.
  • M. Consalvi and G.S. di Freca (2010). Measuring prepayment risk: an application to UniCredit Family Financing. Tech. Rep. Working Paper Series, UniCredit Universities.
  • Y. Deng, J.M. Quigley, and R. van Order (2000). Mortgage terminations, heterogeneity and the exercise of mortgage options. Econometrica, 68(2), 275-307.
  • K. Dunn and J. McConnell (1981). Valuation of GNMA mortgage-backed securities. Journal of Finance, 36, 599–616.
  • R.M. Dunsky and T.S.Y. Ho (2007). Valuing fixed rate mortgage loans with default and prepayment options. Journal of Fixed Income, 16(4), 7–31.
  • R. Elie et al (2002). A Model of Prepayment for the French Residential Loan Market. Working Paper Groupe de Recherche Operationnelle, Credit Lyonnais http://gro.creditlyonnais.fr.
  • R. Elul et al. (2010). What ’triggers’ mortgage default? The American Economic Review, Papers and Proceedings 200(2), 490–494.
  • D. Faraggi and R. Simon (1995). A neural network model for survival data. Statistics in Medicine, 14, 73–82.
  • J.P. Fine and R.J. Gray (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of American Statistics Association, 94, 496–509.
  • T.A. Gerds et al (2013). Estimating a time-dependent concordance index for survival prediction models with covariate dependent censoring. In: Statistics in Medicine, 32(13), 2173–2184.
  • L.S Goodman et al. (2010). Negative equity trumps unemployment in predicting defaults. Journal of Fixed Income, 19(4), 67–72.
  • E. Graf et al. (1999). Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, 18(17-18), 2529–2545.
  • R.J. Gray (1988). A class of K-sample tests for comparing the cumulative incidence of a competing risk. Annals of Statistics, 16, 1141–1154.
  • J. Green and J. Shoven (1986). The effects of interest rates on mortgage prepayments. Journal of Money, Credit and Banking, 18 (1986), 41–59.
  • L. Guiso, P. Sapienza, and L. Zingales (2013). The determinants of attitudes toward strategic default on mortgages. Journal of Finance, 68(4), 1473– 1515.
  • J. Gyourko and J. Tracy (2014). Reconciling theory and empirics on the role of unemployment in mortgage default. Journal of Urban Economics, 80, 87–96.
  • O. Hamidi et al (2017). Application of random survival forest for competing risks in prediction of cumulative incidence function for progression to AIDS. Epidemiology Biostatistics and Public Health 14(4), 323-241.
  • L. Hayre. (2003). Prepayment modeling and valuation of Dutch mortgages. The Journal of Fixed Income, 12, 25–47.
  • J.V. Hoff (1996). Adjustable and fixed rate mortgage termination, option values and local market conditions: An empirical analysis. Real Estate Economics, 24(3), 379–406.
  • H. Ishwaran et al. (2014). Random survival forests for competing risks. Biostatistics, 15(4). 757–773.
  • H. Ishwaran et al. (2010). Random survival forests for high-dimensional data. Statistical Analysis and Data Mining, 4, 115–132.
  • J.B. Kau et al. (1993). Option theory and floating-rate securities with a comparison of adjustable and fixed-rate mortgages. Journal of Business, 66(4), 595–618.
  • M. LaCour-Little (2008). Mortgage termination risk: a review of the recent literature. Journal of Real Estate Literature, 16(3), 295–326.
  • D. Lando and T.M. Skodeberg (2002). Analyzing rating transitions and rating drift with continuous observations. Journal of Banking and Finance, 26(2-3), 423–444.
  • E.C. Lawrence, L.D. Smith, and M. Rhoades (1992). An analysis of default risk in mobile home credit. Journal of Banking and Finance 16(2), 299–312.
  • C. Lee, J. Yoon, and M. van der Schaar (2019). Dynamic-deephit: a deep learning approach for dynamic survival analysis with competing risks based on longitudinal data. IEEE Ttransactions on Biomedical Engineering 20(5), 1–12.
  • C. Lee et al. (2018). Deephit: A deep learning approach to survival analysis with competing risk. Procedia, 32th AAAI Conf. Artif. Intell, 2314–2321.
  • A. Levin and A. Davidson (2005). Prepayment and option adjusted valuation of MBS. The Journal of Portfolio Management 31(4), 286-299.
  • Z. Li et al. (2019). Predicting prepayment and default risks of unsecured consumer loans in online lending. Emerging Markets Finance and Trade 55(1), 118–132.
  • F. Longstaff (2002). Optimal recursive refinancing and the valuation of mortgage-backed securities. Working paper, University of California, Los Angeles.
  • M. Lunn and D. McNeil. (1995). Applying Cox regression to competing risks. Biometrics, 51, 524–532.
  • R. Lydon and Y. McCarthy (2013). What lies beneath? Understanding recent trends in Irish mortgage arrears. The Economic and Social Review, Economic and Social Studies, 44(1), 117–150.
  • C. Mayer, K. Pence, and S. Sherlund (2009). The rise in mortgage defaults. Journal of Economic Perspectives, 23(1), 27–50.
  • F. McCann (2014). Modelling default transitions in the UK mortgage market. Central Bank of Ireland- Research Technical Paper 17/RT/14.
  • D.C. Nijescu (2012). Prepayment risk, impact on credit products. Theoretical and Applied Economics,19(8), 53–62.
  • A.D. Pavlov (2001). Competing risks of mortgage termination: Who refinances, who moves, and who defaults? The Journal of Real Estate Finance and Economics, 23(2), 185–211.
  • S. Richard and R. Roll (1989). Prepayments on fixed-rate mortgage-backed securities. Journal of Portfolio Management, 15, 73–82.
  • T.H. Scheike and M-J. Zhang (2011). Analyzing competing risk data using the R timereg package. Journal of Statistical Software 38(2), 1–15.
  • T.H. Scheike and M.J. Zhang (2002). An additive-multiplicative Cox-Aalen model. Scandinavian Journal of Statistics in Medicine, 28, 75–88.
  • T.H. Scheike and M.J. Zhang (2003). Extensions and applications of the Cox-Aalen survival models. Biometrics 59, 1033–1045.
  • E.S. Schwartz and W.N. Torous (2003). Commercial office space: tests of a real options model with competitive ınteractions. Working Paper, University of California, Los Angeles.
  • Y. Shen and S.C. Cheng (1999). Confidence bands for cumulative ıncidence curves under the additive risk model. Biometrika, 55, 1093–1100.
  • L.D. Smith, E.C. Lawrence, and S.M. Sanchez (2007). A comprehensive model for managing credit risk on home mortgage portfolios. Decision Sciences, 27(2), 291–317.
  • R. Stanton and N. Wallace (2011). The bear lair: Index credit default swaps and the subprime mortgage crisis. Review of Financial Studies 24(10), 3250–3280.
  • K.D. Vandell (1978). Default risk under alternativemortgage ınstruments. The Journal of Finance, 33(5), 1279–1296.
  • K.D. Vandell (1995). How ruthless is mortgage default? A review and synthesis of the evidence” Journal of Housing Research, 6(2), 245–264.
  • M. Wolbers, M.T. Koller, and J.C. Witteman (2013). Concordance for prognostic models with competing risks. Research Report, University of Copenhagen, Department of Biostatistics 3
There are 63 citations in total.

Details

Primary Language English
Subjects Finance, Finance and Investment (Other), Business Administration, Business Systems in Context (Other)
Journal Section Articles
Authors

Sukriye Tuysuz 0000-0001-8391-6521

Publication Date June 2, 2024
Submission Date February 12, 2024
Acceptance Date June 1, 2024
Published in Issue Year 2024 Volume: 11 Issue: 1

Cite

APA Tuysuz, S. (2024). PREPAYMENT AND DEFAULT RISK: A REVIEW. Journal of Economics Finance and Accounting, 11(1), 43-59. https://doi.org/10.17261/Pressacademia.2024.1896

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