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A pricing model with dynamic repayment flows for guaranteed consumer loans

Author

Listed:
  • Chen, Shou
  • Jiang, Xiangqian
  • He, Hongbo
  • Zhou, Xi

Abstract

In the literature, the pricing of guaranteed consumer loans remains largely unexplored. This paper proposes a pricing model with dynamic repayment flows for guaranteed consumer loans based on the expected net present value (ENPV) method. Our study contributes to the literature by providing a practical guide for individual lenders to invest in consumer loans. Specifically, we conduct an illustrative analysis of peer-to-peer consumer loans guaranteed by the Risk Protection Scheme (RPS) in China. The ENPV shows that the value of guaranteed loans is jointly determined by the borrowers’ Markov repayment behavior and the compensation ability of the RPS. Numerical simulations show that both the returns and losses of guaranteed loans are smaller than those of non-guaranteed loans. Further, the lenders who invest in a loan guaranteed by the RPS receive some repayments, although the borrower no longer repays the debt and the balance of the guarantee account is negative.

Suggested Citation

  • Chen, Shou & Jiang, Xiangqian & He, Hongbo & Zhou, Xi, 2020. "A pricing model with dynamic repayment flows for guaranteed consumer loans," Economic Modelling, Elsevier, vol. 91(C), pages 1-11.
  • Handle: RePEc:eee:ecmode:v:91:y:2020:i:c:p:1-11
    DOI: 10.1016/j.econmod.2020.05.013
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    References listed on IDEAS

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    1. Guo, Yanhong & Zhou, Wenjun & Luo, Chunyu & Liu, Chuanren & Xiong, Hui, 2016. "Instance-based credit risk assessment for investment decisions in P2P lending," European Journal of Operational Research, Elsevier, vol. 249(2), pages 417-426.
    2. de Andrade, Fabio Wendling Muniz & Thomas, Lyn, 2007. "Structural models in consumer credit," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1569-1581, December.
    3. He, Ping & Hua, Zhongsheng & Liu, Zhixin, 2015. "A quantification method for the collection effect on consumer term loans," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 17-26.
    4. Musto, David K. & Souleles, Nicholas S., 2006. "A portfolio view of consumer credit," Journal of Monetary Economics, Elsevier, vol. 53(1), pages 59-84, January.
    5. Perli, Roberto & Nayda, William I., 2004. "Economic and regulatory capital allocation for revolving retail exposures," Journal of Banking & Finance, Elsevier, vol. 28(4), pages 789-809, April.
    6. Leow, Mindy & Crook, Jonathan, 2014. "Intensity models and transition probabilities for credit card loan delinquencies," European Journal of Operational Research, Elsevier, vol. 236(2), pages 685-694.
    7. A. Wayne Corcoran, 1978. "The Use of Exponentially-Smoothed Transition Matrices to Improve Forecasting of Cash Flows from Accounts Receivable," Management Science, INFORMS, vol. 24(7), pages 732-739, March.
    8. Zhiyong Li & Ke Li & Xiao Yao & Qing Wen, 2019. "Predicting Prepayment and Default Risks of Unsecured Consumer Loans in Online Lending," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(1), pages 118-132, January.
    9. Katarzyna Bijak & Lyn C Thomas, 2015. "Modelling LGD for unsecured retail loans using Bayesian methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(2), pages 342-352, February.
    10. Chen, Xiao & Huang, Bihong & Ye, Dezhu, 2018. "The role of punctuation in P2P lending: Evidence from China," Economic Modelling, Elsevier, vol. 68(C), pages 634-643.
    11. Xuchen Lin & Xiaolong Li & Zhong Zheng, 2017. "Evaluating borrower’s default risk in peer-to-peer lending: evidence from a lending platform in China," Applied Economics, Taylor & Francis Journals, vol. 49(35), pages 3538-3545, July.
    12. R. M. Cyert & H. J. Davidson & G. L. Thompson, 1962. "Estimation of the Allowance for Doubtful Accounts by Markov Chains," Management Science, INFORMS, vol. 8(3), pages 287-303, April.
    13. Matuszyk, Anna & So, Mee Chi & Mues, Christophe & Moore, Angela, 2016. "Modelling repayment patterns in the collections process for unsecured consumer debt: A case studyAuthor-Name: Thomas, Lyn C," European Journal of Operational Research, Elsevier, vol. 249(2), pages 476-486.
    14. Zhixin Liu & Ping He & Bo Chen, 2019. "A Markov decision model for consumer term-loan collections," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 1043-1064, May.
    15. Jarrow, Robert A., 2011. "Credit market equilibrium theory and evidence: Revisiting the structural versus reduced form credit risk model debate," Finance Research Letters, Elsevier, vol. 8(1), pages 2-7, March.
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    Cited by:

    1. Dong, Linjia & Yang, Zhaojun, 2023. "Investment and financing analysis for a venture capital alternative," Economic Modelling, Elsevier, vol. 126(C).

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    More about this item

    Keywords

    Repayment flow; Consumer loan; Guarantee; P2P loan; Risk protection scheme;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

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