Abstract:
This paper introduces a collateral disposal method to deal with the nonperforming loans of defaulted mortgages. The default rate is calculated by discounted cash flow, finding that the present value of recovered fund is quite sensitive to the fluctuation of the disposal period. Probabilistic density of disposal duration is measured using the historical disposal records from a state-owned bank. Monte Carlo simulation results imply that the non-extreme recovered loss given defaults of the non-performing loans approximately obey normal distributions. The Markowitz portfolio theory is modified by grouping the loans into matrix and then obtains the proportion of each asset group. Based on the consideration of the profit extraction and application of the broker supply and demand equilibrium analysis method, this paper elaborated the expected LGD of asset pool and bond issuance interest rate determination mechanism. Finally, considering the correlated default into account, the simulations on different correlations indicate that our conclusion is robust.
张小茜, 党春辉. 基于抵押物处置风险的不良贷款证券化研究——以某国有商业银行的个人住房贷款资产池为例[J]. 金融研究, 2018, 458(8): 102-119.
ZHANG Xiaoqian, DANG Chunhui. Securitization of NPL Based on Collateral Disposal Risk:Case from Asset Pool of Residential Mortgage Loans of a State-owned Commercial Bank. Journal of Financial Research, 2018, 458(8): 102-119.
Agarwal, S., Y. Chang and A. Yavas, 2012, “Adverse Selection in Mortgage Securitization”, Journal of Financial Economics, 105(9), pp. 640~660.
[20]
Araten, M., M. Jacobs and P. Varshney, 2004, “Measuring LGD on Commercial Loans: An 18-year Internal Study”, RMA Journal, 86(5), pp. 96~103.
[21]
Casu, B., A. Sarkisyan, A. Clare and S. Thomas, 2013, “Securitization and Bank Performance”, Journal of Money Credit & Banking, 45(12), pp.1617~1658.
[22]
Duffie, D., A., Eckner, G., Horel, and L., Saita, 2009, “Frailty Correlated Default”, The Journal of Finance, 64(9), pp. 2089~2123.
[23]
Giuzio, M., D. Ferrari and S. Paterlini, 2016, “Sparse and Robust Normal and t- portfolios by Penalized Lq-likelihood Minimization”, European Journal of Operational Research, 250(4), pp.251~261.
[24]
Gupton, G. M. and R. M. Stein, 2002, “LossCalc: Model for Predicting Loss Given Default (LGD)”, Working Paper, Moody's Investors Service.
[25]
Hanson, S. G. and A. Sunderam, 2013, “Are there too Many Safe Securities? Securitization and the Incentives for Information Production”, Journal of Financial Economics, 108(6), pp.565~584.
[26]
Loutskina, E. and P. E. Strahan, 2009, “Securitization and the Declining Impact of Bank Finance on Loan Supply: Evidence from Mortgage Originations”, The Journal of Finance, 64(4), pp. 861~889.
[27]
Loutskina, E., 2011, “The Role of Securitization in Bank Liquidity and Funding Management”, Journal of Financial Economics, 100(6), pp. 663~684.
[28]
Nadauld, T. D. and S. M. Sherlund, 2013, “The Impact of Securitization on the Expansion of Subprime Credit”, Journal of Financial Economics, 107(2), pp. 454~476.
[29]
Piskorski, T., A. Seru, and V. Vig, 2010, “Securitization and Distressed Loan Renegotiation: Evidence from the Subprime Mortgage Crisis”, Journal of Financial Economics, 97(9), pp. 369~397.
[30]
Rajan, U., A. Seru, and V. Vig, 2015, “The Failure of Models that Predict Failure: Distance, Incentives and Defaults”, Journal of Financial Economics, 115(2), pp.237-260.
[31]
Stein, J. C., 2010, “Securitization, Shadow Banking & Financial Fragility”, Daedalus, 139(9), pp. 41~51.