scholarly journals The Impact Of PD-LGD Correlation On Expected Loss And Economic Capital

2017 ◽  
Vol 16 (3) ◽  
pp. 157-170 ◽  
Author(s):  
Gary Van Vuuren ◽  
Riaan De Jongh ◽  
Tanja Verster
Keyword(s):  
Credit Risk ◽  
Real Time ◽  
Economic Capital ◽  
Probability Of Default ◽  
Expected Loss ◽  
Loan Portfolio ◽  
Correlation Dependence ◽  
Loss Given Default ◽  
Default Data ◽  
The Impact

The Basel regulatory credit risk rules for expected losses require banks use downturn loss given default (LGD) estimates because the correlation between the probability of default (PD) and LGD is not captured, even though this has been repeatedly demonstrated by empirical research. A model is examined which captures this correlation using empirically-observed default frequencies and simulated LGD and default data of a loan portfolio. The model is tested under various conditions dictated by input parameters. Having established an estimate of the impact on expected losses, it is speculated that the model be calibrated using banks' own loss data to compensate for the omission of correlation dependence. Because the model relies on observed default frequencies, it could be used to adapt in real time, forcing provisions to be dynamically allocated.

The Credit Securitisation Process as a Tool of Portfolio Credit Risk Managing

2012 ◽  
pp. 5-28
Author(s):  
Di Clemente Annalisa
Keyword(s):  
Credit Risk ◽  
Simulation Analysis ◽  
Risk Mitigation ◽  
Probability Of Default ◽  
Loan Portfolio ◽  
Asset Return ◽  
Portfolio Credit Risk ◽  
The Impact ◽  
Monte Carlo Simulation Model

This study explores the role of the credit securitisation process in managing the credit risk amount of the banking loan portfolio, when the bank originator retains a residual equitylike class as illiquid first loss position (FLP). An Importance Sampling Monte Carlo simulation model has been implemented for estimating the portfolio credit risk amount, taking into account the portfolio credit risk mitigation effect provided by the credit securitisation process. This study identifies the credit asset pool able to produce the larger effect of credit risk reduction on the loan portfolio, when the asset pool is unloaded off the banking book. Moreover, this simulation analysis quantifies the extent of the portfolio credit risk mitigation, produced by the securitisation process of the asset pool previously identified. The impact of the securitisation activity has been also investigated when the probability of default and the asset return correlation of the obligors in portfolio are changing.

scholarly journals An Assessment of the Impact of Credit Risk Management and Performance on Loan Portfolio at International Bank Liberia

2021 ◽  
pp. 55-64
Author(s):  
Alvin Boye Dolo
Keyword(s):  
Risk Management ◽  
Credit Risk ◽  
The Body ◽  
Management Policy ◽  
Credit Risk Management ◽  
Loan Portfolio ◽  
Net Worth ◽  
International Bank ◽  
And Performance ◽  
The Impact

This research entitled “An Assessment of the impact of credit risk management and performance on loan portfolio at International Bank Liberia Limited from 2015-2017 contributed to the body of knowledge to the beneficiaries. It findings are also important for the Central Bank to use in monitoring credit scoring and history across all commercial bank with in the country. This study was quantitative in nature, and involves mathematical modelling in order to determine the effect of changes in interest rates on profit and net worth of the sampled banks. This study uses panel data and assumes that the effect of interest rate changes vary across the observations and over time, therefore the use of stochastic econometric (panel regression analysis) process is appropriate. The population of the study will consist of 150 credit staffs and other staffs of IBLL. The study adopt a census study and collect data for two years from 1st January, 2015 to 31st December, 2017 and the researcher used sample out 85 respondents representing 57% as the sample size from the population of 150 persons from the study area. The findings reveals that it was established from the study that 25% of the respondents who were picked from the institution agreed that credit score is one of the major system used by the bank in determining loan and 32% selected credit history. It was also observed that that bank operate within a defined credit granting criteria. The findings also show that IBLL established a system of independent, ongoing assessment of the bank‟s credit risk management. It was proven that 48% of the respondents agree while 41% strongly agree. It was established that IBLL have a loan risk management policy in place. This policy is very crucial in providing guidelines on how to manage the various risks the bank encounter in their lending activities. Members of the bank and regulators are those responsible for the formulation of the credit policy with less input from employees.

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 Credit Risk Modeling: Combining Classification And Regression Algorithms to Predict Expected Loss

2014 ◽  
Vol 8 (4) ◽  
pp. 4-10
Author(s):  
Tim Kreienkamp ◽  
Andrey Kateshov
Keyword(s):  
Credit Risk ◽  
Risk Model ◽  
Machine Learning Techniques ◽  
Risk Modeling ◽  
Expected Loss ◽  
Financial Industry ◽  
Loan Default ◽  
Loss Given Default ◽  
Learning Techniques ◽  
Selection Algorithms

Credit risk assessment is of paramount importance in the financial industry. Machine learning techniques have been used successfully over the last two decades to predict the probability of loan default (PD). This way, credit decisions can be automated and risk can be reduced significantly. In the more recent parts, intensified regulatory requirements led to the need to include another parameter – loss given default (LGD), the share of the loan which cannot be recovered in case of loan default – in risk models. We aim to build a unified credit risk model by estimating both parameters jointly to estimate expected loss. A large, highdimensional, real world dataset is used to benchmark several combinations of classification, regression and feature selection algorithms. The results indicate that non-linear techniques work especially well to model expected loss.

scholarly journals The Impact of the Internal Audit in Reducing Credit Risk in Commercial Banks in Kosovo

2016 ◽  
Vol 12 (4) ◽  
pp. 268
Author(s):  
Shqipdona Hashani Siqani ◽  
Edona Sekiraca
Keyword(s):  
Credit Risk ◽  
Commercial Bank ◽  
Commercial Banks ◽  
Internal Audit ◽  
Portfolio Diversification ◽  
Credit Risk Management ◽  
Loan Portfolio ◽  
Survey Results ◽  
The Republic ◽  
The Impact

Credit risk represents the vast majority of the risk in the context of estimating the capacity of the transfer of risk from commercial banks. Any commercial bank operating, in Kosovo, must have a system for managing credit risk. An important and essential process, such as the management of the credit risk, cannot be carried out without the aid of internal audit. From the survey results, it was concluded that the process of auditing the banks recommended the implementation of policies for managing credit risk of the respective commercial bank’s policy. This also include the policy of credit risk management of the Central Bank of the Republic of Kosovo, implementation of procedures, regulations and rules for credit exposure, loan portfolio diversification, training of staff of the credit risk involved in completing the loan files, etc.

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.

scholarly journals Collateral sufficiency as an adapt financial covenant in bank crediting

2021 ◽  
Vol 26 (1) ◽  
pp. 83-106
Author(s):  
Aleksandr M. KARMINSKII ◽  
Ol'ga D. KHON
Keyword(s):  
Credit Risk ◽  
Three Dimensions ◽  
Probability Of Default ◽  
Russian Market ◽  
New Approach ◽  
Credit Risks ◽  
Econometric Methods ◽  
Psychological Risk ◽  
The Impact ◽  
Linear Regressions

Subject. The article examines the Loan-to-Value ratio in three dimensions. First, as a measure of leverage, helpful to understand the spread of systemic risk in the economy. Second, we identify LTV throughout financial covenants. Finally, we implement LTV to indicate the probability of default. Objectives. The goal of the paper is to study the impact of collateral sufficiency on credit risk throughout adjusted financial covenants for bank corporate loans. Methods. To conduct the research, the authors implement econometric methods, linear regressions and binary models. Results. We have revealed the prevalence of the posterior theory of the impact of the collateral sufficiency on the credit risk evaluation by corporate loans. We have also revealed that the higher credit risks, the higher collateral requirements to pledge the loans. Conclusions and Relevance. We have considered a new approach to identify collateral requirements, throughout LTV measures, as adjusted financial covenants on the Russian market. Lender’s preferences are being stronger at the time of downturns in economic activity. At the same time, economic growth neutralizes any visible behavioral favors/patterns. Hereby psychological risk components are quite essential, and need studying in modern banking.

scholarly journals Determinants of Bank Value: Evidence from Nepalese Commercial Banks

2021 ◽  
Vol 6 (1) ◽  
pp. 29-38
Author(s):  
Madhusudan Gautam
Keyword(s):  
Credit Risk ◽  
Commercial Banks ◽  
Easy Access ◽  
Return On Assets ◽  
Loan Portfolio ◽  
Explanatory Variables ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Impact ◽  
Positive Effect

Commercial banks have a pivotal role in an economy as they provide easy access for firms to fulfill financing needs and help stimulate economic development. This study aims to analyze the impact of key bank-specific determinants on bank value in Nepalese commercial banks, covering 133 observations from 19 commercial banks over the period 2012/13 to 2018/19. Bank value is measured through M/B and Tobin’s Q. Size, profitability, credit risk, loan, deposit and capital are used as explanatory variables. Panel data regression models have been used for analysis purpose. The results of this paper show that profitability, deposit and loans are major determinants of bank value. Moreover, return on assets and bank deposit have positive effect on bank value whereas loan has negative explaining power on bank value. Thus, this paper concludes that Nepalese commercial banks have to pay special attention for the efficient and effective utilization of assets to increase profits and try to increase the size of deposits to increase loan portfolio. Steps and enforcement actions need to be taken by policy level authorities for effective loan management to minimize credit risk and increase bank value.

Post-merger changes in bank credit risk: 1991-2006

2014 ◽  
Vol 40 (1) ◽  
pp. 51-71 ◽  
Author(s):  
Morris Knapp ◽  
Alan Gart
Keyword(s):  
Credit Risk ◽  
Financial Analysts ◽  
Risk Profile ◽  
Subsequent Paper ◽  
Investment Portfolio ◽  
Loan Portfolio ◽  
Content Type ◽  
Holding Companies ◽  
The Impact ◽  
Credit Losses

Purpose – This paper aims to examine the post-merger changes in the credit risk profile of merging bank holding companies and tests whether there is an increase in credit risk after a merger due to changes in the mix of loans in the portfolio. Design/methodology/approach – The authors use the expected variability of the credit risk of a loan portfolio based on the mix of loan types in the portfolio and the variability of the industry credit losses of each type following the standard Markowitz procedure for finding the standard deviation of an investment portfolio. The authors then test to see whether there has been a significant change in the expected variability (the credit risk profile) after a merger. Findings – The authors find that there are significant differences in both the level and variability of loan charge-offs and non-performing loans (NPL) among the various loan categories. The authors also find significant changes in the mix of loan categories in the loan portfolio after a merger. In addition, the authors find that the expected variability in both the charge-off rate and the NPL rate rises significantly after a merger. Research limitations/implications – This is the first of two papers looking at post-merger changes in credit risk based simply on the changes in the mix of loan types; it does not consider the actual post-merger credit performance of the specific mergers. That will be addressed in a subsequent paper. Practical implications – Financial analysts evaluating banking merger announcements may wish to include the impact of the likely shifts in loan mix and credit risk shown in this paper as they project the likely impact of the merger. Originality/value – This paper addresses an aspect of bank mergers that has not been addressed in the literature, the impact of mergers on credit risk. The results are likely to be useful to investors, financial analysts and regulators.

scholarly journals Quantifying the impact of different copulas in a generalized CreditRisk+ framework An empirical study

2014 ◽  
Vol 2 ◽  
pp. 1-21 ◽  
Author(s):  
Kevin Jakob ◽  
Matthias Fischer
Keyword(s):  
Empirical Study ◽  
Banking Industry ◽  
Economic Capital ◽  
Regulatory Requirements ◽  
Economic Sectors ◽  
Loan Portfolio ◽  
Credit Portfolio ◽  
The Impact ◽  
Credit Losses ◽  
Credit Portfolio Model

AbstractWithout any doubt, credit risk is one of the most important risk types in the classical banking industry. Consequently, banks are required by supervisory audits to allocate economic capital to cover unexpected future credit losses. Typically, the amount of economical capital is determined with a credit portfolio model, e.g. using the popular CreditRisk+ framework (1997) or one of its recent generalizations (e.g. [8] or [15]). Relying on specific distributional assumptions, the credit loss distribution of the CreditRisk+ class can be determined analytically and in real time. With respect to the current regulatory requirements (see, e.g. [4, p. 9-16] or [2]), banks are also required to quantify how sensitive their models (and the resulting risk figures) are if fundamental assumptions are modified. Against this background, we focus on the impact of different dependence structures (between the counterparties of the bank’s portfolio) within a (generalized) CreditRisk+ framework which can be represented in terms of copulas. Concretely, we present some results on the unknown (implicit) copula of generalized CreditRisk+ models and quantify the effect of the choice of the copula (between economic sectors) on the risk figures for a hypothetical loan portfolio and a variety of parametric copulas.

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