Aplikasi Metode Accelerated Failure Time (AFT): Analisis Risiko Prepayment pada Kredit Kendaraan Bermotor

  • Yessy Noviyanti Kawi Universitas Indonesia
  • Yogo Purwono Universitas Indonesia

Abstract

This study aims to measure the probability distribution of the time of payment in advance and explain the factors that can affect the risk of credit loans using the life analysis method. This research method is descriptive quantitative method. The sample of this research is motor vehicle loan financing debtors from private banks in Indonesia with an observation period between 2017-2019. The results of the study indicate that there is a 50% chance that the debtor of motorcycle and car loan financing will have an upfront payment after 21 months and 24 months from the initial loan application period. After the 44th and 68th months, the estimated probability of the debtor having a prepayment is > 99%. In conclusion, the tenor factor, credit limit, occupation, income, gender, marital status, age, and education level affect the variation in time until the payment in advance for motorcycle credit financing. Meanwhile, in addition to these factors, the additional factor of the area of ​​residence also affects the variation in the time of advance payments for car loans.

 

Keywords: Survival Analysis, Banking, Credit Loans, Accelerated Failure Time, Prepayment Time

References

Archer, W. R., Ling, D. C., & McGill, G. A. (1996). The Effect of Income and Collateral Constraints on Residential Mortgage Terminations. Regional Science and Urban Economics, 26(3–4), 235–261. doi: 10.1016/0166-0462(95)02115-9

Brennan, M. J., & Schwartz, E. S. (1985). Evaluating Natural Resource Investments. The Journal of Business, 58(2), 135-157. doi: 10.1086/296288

Consalvi, M. F. G. (2010). Measuring Prepayment Risk: an Application to Unicredit Family Financing. Italy: Knight of Labor Ugo Foscolo Foundation

Gallati, R. (2003). Risk Management & Capital Adequacy. New york: McGraw-Hill Inc

Green, J., & Shoven, B. (1986). The Effect of Interest Rates on Mortgage Prepayments. Journal of Money, Creit and Banking, 18(1), 41–59. https://doi.org/10.2307/1992319

Hakim, E. S. (2008). Analisa Survival Kredit. Jakarta: Universitas Indonesia

Harrell, F. E. (2001). Regression Modeling Strategies. New York: Springer New York (Springer Series in Statistics)

Jacobs, J. P. A. M. & Koning, E. S. R. H. (2005). Modelling Prepayment Risk. Belanda: University of Groningen

Kang, P., & Zenios, S. A. (1992). Complete Prepayment Models for Mortgage-Backed Securities. Management Science, 38(11), 1665–1685. doi: 10.1287/mnsc.38.11.1665.

Saroinsong, A. N. (2014). Fungsi Bank dalam Sistem Penyaluran Kredit Perbankan. Lex Privatum, 2(3), 130-137. https://ejournal.unsrat.ac.id/index.php/lexprivatum/article/view/6166

Thomas, L. E. D. (2002). Credit Scoring and Its Applications. in Monographs on Mathematical Modeling and Computation. Philadelphia: Society for Industrial and Applied Mathematics

Wang, M. (2019) Determinants of Repayment Risk in Automobile Loan Market– An Empirical Analysis. Kanada: Hec Montréal
Published
2022-06-05
Abstract viewed = 65 times
pdf downloaded = 99 times