Yurchenko, Yurii (2019): The impact of macroeconomic factors on collateral value within the framework of expected credit loss calculation.
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Abstract
The study examines the impact of macroeconomic factors on the expected credit losses of a financial instrument related to changes in the value of collateral. The author has developed a method of calculating this impact on the basis of econometric models, as well as simulated the effect on expected credit losses and reserves on a financial instrument. Based on the proposed approach, appropriate models have been constructed based on the data of the US and Ukrainian economies for the maximum period available, taking into account the adequacy of the data. In particular, it has been shown that applying the methodology of adjusting collateral value to macroeconomic factors can lead to a reduction of the reserve according to the requirements of the regulator, i.e. from the financial institution's point of view it is possible to release some of the funds additionally.
Item Type: | MPRA Paper |
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Original Title: | The impact of macroeconomic factors on collateral value within the framework of expected credit loss calculation |
English Title: | The impact of macroeconomic factors on collateral value within the framework of expected credit loss calculation |
Language: | English |
Keywords: | LGD, Collateral value, OLS, Credit risk, valuation, GLM |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill |
Item ID: | 97135 |
Depositing User: | Mr Yurii Yurchenko |
Date Deposited: | 27 Nov 2019 13:25 |
Last Modified: | 27 Nov 2019 13:25 |
References: | M. Leow and C. Mues, "Predicting Loss Given Default (LGD) for Residential Mortgage Loans: ATwo-stage Model and Empirical Evidence for UK Bank Data," International Journal of Forecasting, vol. 28, no. 1, pp. 183-195, 2012. B. Zhang, "Fair Lending Analysis of Mortgage Pricing: Does Underwriting Matter?," The Journal of Real Estate Finance and Economics, vol. 46, no. 1, pp. 131-151, 2013. M. Qi and X. Yang, "Loss Given Default of High Loan-to-value Residential Mortgages.," Journal of Banking and FInance, vol. 5, no. 33, pp. 788-799, 2009. M. Araten, M. Jacobs and P. Varshney, "Measuring LGD on Commercial Loans: An 18-year Internal Study," RMA Journal, vol. 8, no. 86, pp. 96-103, 2004. M. LaCour-Little and Y. Zhang, "Default Probability and Loss Given Default for Home Equity Loans," Working paper, pp. 1-21, 2014. V. Lekkas, J. Quigley and R. Order, "Loan Loss Severity and Optimal Mortgage Default," American Real Estate and Urban Economics Association Journal, vol. 4, no. 21, pp. 353-371, 1993. A. Pennington-Cross, "Subprime and Prime Mortgages: Loss Distributions.," Working paper, 2003. J. Dermine and C. Carvalho, "Bank Loan Losses-given-default: A Case Study," Journal of Banking and Finance, vol. 4, no. 30, pp. 1219-1243, 2006. T. Schuermann, "What do We Know about Loss Given Default?," Working paper no 04-01, 2004. F. Sigrist and W. Stahel, "Using the Censored Gamma Distribution for Modeling Fractional Response Variables with an Application to Loss Given Default," ASTIN Bulletin, vol. 2, no. 41, pp. 673-710, 2011. J. Bastos, "Forecsting Bank Loans Loss-give-default," Journal of Banking & Finance, vol. 10, no. 34, pp. 2510-2517, 2010. M. Somers and J. Whittaker, "Quantile Regression for Modelling Distributions of Profit and Loss," European Journal of Operational Research, vol. 3, no. 183, pp. 1477-1487, 2007. T. Bellotti and J. Crook, "Loss Given Default Models Incorporating Macroeconomic Variables for Credit Cards," International Journal of Forecasting, vol. 1, no. 28, pp. 171-182, 2012. O. Yashkir and Y. Yashkir, "Loss Given Default Modeling: Comparative Analysis.," Journal of Risk, vol. 7, no. 1, pp. 25-59, 2013. T. Pereira and F. Cribara-Neto, "A Test for Correct Model Specification in Inflated Beta Regressions," Working Paper, Institute de Matematica, Estatistica e Computaceo Cientifica Universidade Estadual, 2010. E. Altman, B. Brady, A. Resti and A. Sironi, "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implication," Journal of Business-Chicago, vol. 6, no. 78, pp. 2203-2228, 2005. O. N. Arvydas Paškevičius, The Impact of Macroeconomic Indices Upon the Liquidity of the Baltic Capital Markets, Вільнюс: International Business School, 2011. T. Bellini, IFRS 9 and CECL Credit Risk Modelling and Validation: A Practical Guide with Examples worked in R and SAS, Лондон: Academic Press, 2019. OICV_IOSCO, Factors Influencing Liquidity in Emerging Markets, 2007. J. W. Gathuru, The Effect of Macroeconomic Variables on the Value of Real Estates Suppliend in Kenya, Найробі, 2014. D. R. Cox, Regression Models and Life Tables, vol. 34, 1972, pp. 187-220. T. Shumway, "Forecasting Bankruptcy More Accurately: A SImple Hazard Model," Journal of Business 74 (1), pp. 101-124, 2001. O. M. Magnus Laurin, The Influence of Macroeconomic Factors on the Probability of Default, Lund University School of Economics and Management, 2009. E. Ozbay, The Relationship between Stock Returns and Macroeconomic Factors^ Evidence from Turkey, University of Exeter, 2009. J. A. Chan-Lau, "Fundamentals-Based Estimation of Default Probabilities - A Survey,," IMF Working Papers 06/149, 2006. T. Schuermann, What do We Know About Loss Given Default?, Federal Reserve Bank of New York, 2003. B. Yang and M. Tkachenko, "Modeling Exposure at Default and Loss Given Default: Empirical," Journal of Credit Risk, vol. 2, no. 8, pp. 81-102, 2012. EU Directive 2013/36/EU of June 26, 2013, 2013. Law of Ukraine 'On Collateral' of October 2, 1992, Visnuk Verhovnoi Rady Ukrainy , 1992. "IRFS 13 Fair value measurement," 2018. [Online]. Available: https://mof.gov.ua/storage/files/IFRS-13_ukr_2016.pdf. "IFRS 9 Financial instruments," 2018. [Online]. Available: https://mof.gov.ua/storage/files/IFRS_9_Ukrainian-compressed.pdf. K. M. Totmyanina, "An overview of probability of default models," Financial Risk Management, vol. 1, no. 25, pp. 12-24, 2011. A. Karminsky, A. Lozinskaya and E. Ozhegov, "Methods of estimating the losses of a lender in housing lending," HSE Economic Journal, vol. 20, no. 1, pp. 9-51, 2016. D. Nekhaychuk and D. Kurtbedinov, "Valuation ofcollateral in the banking lending," Problems of Material Culture - Economic Sciences, pp. 70-74. S. Figlewski, H. Frydman and W. Liang, Modeling the Effect of Macroeconomic Factors on Corporate Default and Credit Rating Transitions, Нью-Йорк, 2012. K. Carling, T. Jacobson, J. Linde and K. Roszbach, Corporate Credit Risk Modeling and the Macroeconomy, Journal of Banking and Finance 31, 2007. Y. A. Xie, J. Shi and C. Wu, "Do Macroeconomic Variables Matter for Pricing Default Risk?," International Review of Economics and Finance 17, pp. 279-291. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/97135 |