Effects of Unobserved Defaults on Correlation between Probability of Default and Loss Given Default on Mortgage Loans

2009 ◽  
Author(s):  
Peter Palmroos
Keyword(s):  
Probability Of Default ◽  
Mortgage Loans ◽  
Loss Given Default

scholarly journals Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default

2020 ◽  
Vol 8 (4) ◽  
pp. 68
Author(s):  
Giuseppe Orlando ◽  
Roberta Pelosi
Keyword(s):  
Risk Taking ◽  
Economic Losses ◽  
Probability Of Default ◽  
Legal Environment ◽  
Loss Given Default ◽  
Recovery System ◽  
Main Driver ◽  
Long Time ◽  
The Subject ◽  
Time Required

Within bank activities, which is normally defined as the joint exercise of savings collection and credit supply, risk-taking is natural, as in many human activities. Among risks related to credit intermediation, credit risk assumes particular importance. It is most simply defined as the potential that a bank borrower or counterparty fails to fulfil correctly at maturity the pecuniary obligations assumed as principal and interest. Whenever this happens, a loan is non-performing. Among the main risk components, the Probability of Default (PD) and the Loss Given Default (LGD) have been the subject of greater interest for research. In this paper, logit model is used to predict both components. Financial ratios are used to estimate the PD. Time of recovery and presence of collateral are used as covariates of the LGD. Here, we confirm that the main driver of economic losses is the bureaucratically encumbered recovery system and the related legal environment. The long time required by Italian bureaucratic procedures, simply put, seems to lower dramatically the chance of recovery from defaulting counterparties.

scholarly journals Econometric model to estimate the Probability of Default and Loss Given Default in the EBA stress test in 2016

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Salvador Climent-Serrano
Keyword(s):  
Panel Data ◽  
Econometric Model ◽  
Stress Test ◽  
A Priori ◽  
Ordinary Least Squares ◽  
Probability Of Default ◽  
Macroeconomic Factors ◽  
Factors Affecting ◽  
Loss Given Default ◽  
Test Methodology

In this research, an econometric with panel data using Ordinary least squares OLS model is constructed following the guidelines recommended by the EBA stress test methodology for 2016. The findings indicate that macroeconomic factors affecting defaults are the expected ones in the Spanish credit institutions. However, loan impairments do not follow the patterns that a priori would be normal. Divergent is outcomes in defaults and impairments: the Non-Performing Loans (NPL) is pro-cyclical and impairment losses are counter-cyclical.

scholarly journals Aggregation of Incidence and Intensity Risk Variables to Achieve Reconciliation

Risks ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 107
Author(s):  
Clive Hunt ◽  
Ross Taplin
Keyword(s):  
Intensity Ratio ◽  
Important Task ◽  
Utilization Rate ◽  
Probability Of Default ◽  
Aggregate Level ◽  
Expected Loss ◽  
Total Risk ◽  
Loss Given Default ◽  
The Individual ◽  
Ratio Variables

The aggregation of individual risks into total risk using a weighting variable multiplied by two ratio variables representing incidence and intensity is an important task for risk professionals. For example, expected loss (EL) of a loan is the product of exposure at default (EAD), probability of default (PD), and loss given default (LGD) of the loan. Simple weighted (by EAD) means of PD and LGD are intuitive summaries however they do not satisfy a reconciliation property whereby their product with the total EAD equals the sum of the individual expected losses. This makes their interpretation problematic, especially when trying to ascertain whether changes in EAD, PD, or LGD are responsible for a change in EL. We propose means for PD and LGD that have the property of reconciling at the aggregate level. Properties of the new means are explored, including how changes in EL can be attributed to changes in EAD, PD, and LGD. Other applications such as insurance where the incidence ratio is utilization rate (UR) and the intensity ratio is an average benefit (AB) are discussed and the generalization to products of more than two ratio variables provided.

scholarly journals Kebijakan Loan Loss Provision pada Pembiayaan Mudharaba dan dampaknya pada Kinerja Keuangan Bank Umum Syariah di Indonesia

2019 ◽  
Vol 4 (1) ◽  
pp. 43-52
Author(s):  
Zulfikar Zulfikar ◽  
Mujiyati Mujiyati ◽  
Andy Dwi Bayu Bawono ◽  
Sri Wahyuni
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Probability Of Default ◽  
Loan Loss ◽  
Loss Given Default ◽  
Loan Loss Provision ◽  
Operating Margin

Penelitian ini menginvestigasi peran kebijakan loan loss provision (LLP) pembiayaan mudharabapada kinerja keuangan Bank Umum Syariah (BUS) di Indonesia. Structural Equation Modeling-Partial Least Square (SEM-PLS) digunakan untuk menguji keterkaitan loan loss provision dengankinerja keuangan pada 13 Bank Umum Syariah (BUS) selama 4,5 tahun. Analisis outer modelmenunjukkan bahwa probability of default dan loss given default merupakan faktor penentuloan loss provision. Sedangkan kinerja keuangan ditentukan oleh return on asset, nonperforming financial, net operating margin, dan biaya operasional terhadap pendapatanoperasional. Hasil penelitan ini menunjukkan bahwa loan loss provision berpengaruh langsungterhadap kinerja keuangan. Investigasi lebih lanjut menunjukkan bahwa pendapatanmudharaba berperan meningkatkan pengaruh loan loss provision terhadap kinerja keuangan(pengaruh tidak langsung).

Part II Commercial Banking, 10 The Internal Ratings-Based Approach

2018 ◽  
Author(s):  
Gleeson Simon
Keyword(s):  
Internal Models ◽  
Commercial Banking ◽  
Probability Of Default ◽  
Expected Loss ◽  
Loss Given Default ◽  
Total Capital ◽  
Regulatory Standard ◽  
Model Probability ◽  
Irb Approach

This chapter discusses the internal ratings-based approach (IRB). The IRB permits a bank to use its internal models to derive risk weights for particular exposures. There are two available bases for the IRB: foundation (F-IRB), which permits the bank to model Probability of Default (PD), but relies on regulatory standard figures to determine Loss Given Default (LGD) and Exposure at Default (EAD); and advanced (A-IRB), in which all three of these are modelled. The A-IRB IRB approach models PD, LGD, EAD, and M. Both IRB approaches model both expected loss (EL) and unexpected loss (UL), and IRB banks are expected to recognise the EL derived from their models in their capital calculations. Consequently, a bank using an IRB approach will generally have a different total capital level from that which it would have if it were an SA bank.

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