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Using Markov Chains to Estimate Losses from a Portfolio of Mortgages

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  • Betancourt, Luis

Abstract

Under Statement of Financial Accounting Standards Number Five, Accounting for Contingencies (SFAS 5), financial institutions record a provision for loan losses and establish loan loss reserves when impairment of a loan is probable and the loss can be reasonably estimated. Increasingly, Markov chain models are being used to estimate these losses. This paper develops and test the suitability and forecast accuracy of alternate Markov chain models of mortgage payment behavior using transition data from the Federal Home Loan Mortgage Corporation (Freddie Mac). In developing the models, the Freddie Mac transition data is examined to see if it satisfies the Markovian assumptions of stationary transition probabilities and homogenous payment behavior. The data examined in this paper did not satisfy these assumptions. With respect to accuracy in forecasting loan losses, the Markov chain approach, when incorporating recent information on transition probabilities, performed better than a random-walk model of loan losses. Copyright 1999 by Kluwer Academic Publishers

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  • Betancourt, Luis, 1999. "Using Markov Chains to Estimate Losses from a Portfolio of Mortgages," Review of Quantitative Finance and Accounting, Springer, vol. 12(3), pages 303-317, May.
    • Handle: RePEc:kap:rqfnac:v:12:y:1999:i:3:p:303-17
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    Citations

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    Cited by:

    1. 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.
    2. Gaffney, Edward & Kelly, Robert & McCann, Fergal, 2014. "A transitions-based framework for estimating expected credit losses," Research Technical Papers 16/RT/14, Central Bank of Ireland.
    3. María Hierro, 2009. "Modelling the dynamics of internal migration flows in Spain," Papers in Regional Science, Wiley Blackwell, vol. 88(3), pages 683-692, August.
    4. Kelly, Robert & O'Malley, Terence, 2014. "A Transitions-Based Model of Default for Irish Mortgages," Research Technical Papers 17/RT/14, Central Bank of Ireland.
    5. Kelly, Robert & O’Malley, Terence, 2016. "The good, the bad and the impaired: A credit risk model of the Irish mortgage market," Journal of Financial Stability, Elsevier, vol. 22(C), pages 1-9.
    6. L. Smith & Baiqiang Jin, 2007. "Modeling exposure to losses on automobile leases," Review of Quantitative Finance and Accounting, Springer, vol. 29(3), pages 241-266, October.
    7. Konstantinos Skindilias & Chia Lo, 2015. "Local volatility calibration during turbulent periods," Review of Quantitative Finance and Accounting, Springer, vol. 44(3), pages 425-444, April.
    8. Hsin-Hung Wu & Jiunn-I Shieh, 2008. "Applying a markov chain model in quality function deployment," Quality & Quantity: International Journal of Methodology, Springer, vol. 42(5), pages 665-678, October.
    9. Linda Möstel & Marius Pfeuffer & Matthias Fischer, 2020. "Statistical inference for Markov chains with applications to credit risk," Computational Statistics, Springer, vol. 35(4), pages 1659-1684, December.
    10. Jeffrey R. Stokes, 2023. "A nonlinear inversion procedure for modeling the effects of economic factors on credit risk migration," Review of Quantitative Finance and Accounting, Springer, vol. 61(3), pages 855-878, October.
    11. 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.

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