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An econometric model to quantify benchmark downturn LGD on residential mortgages

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  • Morone, Marco
  • Cornaglia, Anna

Abstract

The paper describes a theoretical approach to determine the downturn LGD for residential mortgages, which is compliant with the regulatory requirement and thus suited to be used for validation, at least as it can give benchmark results. The link between default rates and recovery rates is in fact acknowledged by the regulatory framework as the driver of the downturn LGD, but data constraints do not usually allow for direct estimation of such a dependency. Both default rates and LGD parameters can anyway be related to macroeconomic variables: in the case of mortgages, real estate prices are the common driver. Household default rates are modelled inside a Vector Autoregressive Model incorporating a few other macroeconomic variables, which is estimated on Italian data. Assuming that LGD historical data series are not available, real estate prices influence on recovery rates is described through a theoretical Bayesian approach: possession probability conditional to Loan to Value can thus be quantified, which determines the magnitude of the effect of a price increase on LGD. Macroeconomic variables are then simulated on a five years path in order to determine the loss distribution (default rates times LGD per unit of EAD), both in the case of stochastic price dependent LGD and of deterministic LGD (but still variable default rates). The ratio between the two measures of loss, calculated at the 99.9th percentile for consistency with the regulatory formulas, corresponds to the downturn effect on LGD. In fact, the numerator of the ratio takes into account correlations between DR and LGD. Some results are presented for different combinations of average LGD and unconditional possession probability, which are specific for each bank.

Suggested Citation

  • Morone, Marco & Cornaglia, Anna, 2010. "An econometric model to quantify benchmark downturn LGD on residential mortgages," MPRA Paper 25588, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:25588
    as

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    File URL: https://mpra.ub.uni-muenchen.de/25588/1/MPRA_paper_25588.pdf
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    References listed on IDEAS

    as
    1. Glenn Hoggarth & Steffen Sorensen & Lea Zicchino, 2005. "Stress tests of UK banks using a VAR approach," Bank of England working papers 282, Bank of England.
    2. Dirk Tasche, 2006. "Validation of internal rating systems and PD estimates," Papers physics/0606071, arXiv.org.
    3. repec:dau:papers:123456789/11161 is not listed on IDEAS
    4. Avouyi-Dovi, S. & Bardos, M. & Jardet, C. & Kendaoui, L. & Moquet , J., 2009. "Macro stress testing with a macroeconomic credit risk model: Application to the French manufacturing sector," Working papers 238, Banque de France.
    5. Marcucci, Juri & Quagliariello, Mario, 2008. "Is bank portfolio riskiness procyclical: Evidence from Italy using a vector autoregression," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(1), pages 46-63, February.
    6. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    7. Düllmann, Klaus & Trapp, Monika, 2004. "Systematic Risk in Recovery Rates: An Empirical Analysis of US Corporate Credit Exposures," Discussion Paper Series 2: Banking and Financial Studies 2004,02, Deutsche Bundesbank.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    downturn LGD; default and recovery rates correlation; mortgage; Loan to Value; real estate price; possession probability; Bayesian approach; stress testing; Vector Autoregression;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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