Morone, Marco and Cornaglia, Anna (2010): An econometric model to quantify benchmark downturn LGD on residential mortgages.
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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.
|Item Type:||MPRA Paper|
|Original Title:||An econometric model to quantify benchmark downturn LGD on residential mortgages|
|Keywords:||downturn LGD; default and recovery rates correlation; mortgage; Loan to Value; real estate price; possession probability; Bayesian approach; stress testing; Vector Autoregression;|
|Subjects:||C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General
G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill
C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages
|Depositing User:||Marco Morone|
|Date Deposited:||01. Oct 2010 19:31|
|Last Modified:||12. Feb 2013 08:10|
Altman, E., Resti A. and Sironi A. (2001). Analyzing and explaining default recovery rates. Report submitted to the International Swaps and Derivatives Associations, London, December. Altman, E., Brooks B., Resti A. and Sironi A. (2005). The link between default and recovery rates: theory, empirical evidence and implications. Journal of Business 78 (6), 203-227. 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. Banque de France. Document de travail 238. Banco De Espana (2007). Loss given default estimates under downturn conditions (DLGD) in mortgage loan portfolios in Spain. Directorate General Banking Supervision. Validation document1 Barco, M. (2007). Going Downturn. Cutting Edge, Credit Risk Model, 39-75. Basel Committee on Banking Supervision (2005). Guidance on Paragraph 468 of the Framework Document. Bank for International Settlements. Chabaane, A., Laurent, J.P., Salomon J. (2004). Double impact: Credit Risk Assessment and Collateral Value. Revue Finance, 25, 157-178. Committee of European Banking Supervisors (2008). Range of practices on some Basel II implementation issues. Financial Services Authority (2008). Residential Mortgage Downturn LGDs. FSA paper. Dullmann, K., Trapp M. (2004). Systematic risk in recovery rates – an empirical analysis of US corporate credit exposures. Deutsche Bundesbank discussion paper 2. Hillebrand, M. (2006). Modelling and estimating dependent loss given default. Risk September, 120-125. Hoggarth, G., Sorensen, S., Zicchino, L. (2005). Stress tests of UK banks using a VAR approach. Bank of England. Working Paper 282. Marcucci, J., Quagliariello, M. (2005). Is Bank Portfolio Riskiness Procyclical? Evidence from Italy using a Vector Autoregression. The University of York. Discussion Papers in Economics, 2005/09. Miu P., Ozdemir B. (2006). Basel requirements of downturn LGD: modeling and estimating probability of default and loss given default correlations. Journal of Credit Risk 2 (2), 43-68. Qi, M., Yang, X. (2007). Loss Given Default of High Loan-to-Value Residential Mortgages. Economics and Policy Analysis Working Paper 2007-4. Tasche D. (2006). Validation of internal rating systems and PD estimates. Working paper. Van Order, R. (2008). Modelling and evaluating the credit risk of mortgage loans: a primer. The Journal of Risk Model Validation 2(2), 63-82. Virolainen, K. (2004). Macro stress-testing with a macroeconomic credit risk model for Finland. Bank of Finland. Bank of Finland discussion Papers 18. Wong, J., Choi, k., Fong, T. (2006). A framework for macro stress testing the credit risk of banks in Hong Kong. Hong Kong Monetary Authority Quarterly Bullettin.