Modelling and calibration errors in measures of portfolio credit risk

by Nikola A. Tarashev

Publisher: Bank for International Settlements in Basel, Switzerland

Written in English
Published: Downloads: 348
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This paper develops an empirical procedure for analyzing the impact of model misspecification and calibration errors on measures of portfolio credit risk. When applied to large simulated portfolios with realistic characteristics, this procedure reveals that violations of key assumptions of the well-known Asymptotic Single-Risk Factor (ASRF) model are virtually inconsequential. By contrast, flaws in the calibrated interdependence of credit risk across exposures, which are driven by plausible small-sample estimation errors or popular rule-of-thumb values of asset return correlations, can lead to significant inaccuracies in measures of portfolio credit risk. Similar inaccuracies arise under erroneous, albeit standard, assumptions regarding the tails of the distribution of asset returns.

Edition Notes

Statementby Nikola Tarashev and Haibin Zhu.
SeriesBIS working papers -- no. 230
ContributionsZhu, Haibin, 1972-, Bank for International Settlements. Monetary and Economic Dept.
Classifications
LC ClassificationsHG3879
The Physical Object
FormatElectronic resource
ID Numbers
Open LibraryOL16370411M
LC Control Number2007619403

An Introduction to Credit Risk Modeling Credit risk is a critical area in banking and is of concern to a variety of stakehold-ers: institutions, consumers and regulators. It has been the subject of considerable research interest in banking and nance communities, and . credit risk, delta hedging and counterparty risk measured through credit valuation adjustment. Keywords: Risk measures; Validation of pricing models; Derivatives risk management; Risk management JEL Classification: C63, D81 1. Introduction Risk measurement relies on modelling assumptions. Errors in these assumptions introduce errors in risk. Portfolio Credit Risk Specification & Calibration Errors Asymptotic Single-Risk Factor (ASRF) model [fe3] Default Correlation & Copula Functionals Applications Capital Adequacy Basel II 9 Internal Rating-Based Approach (IRB) Problem To quantify/rank impacts of 4 non-overlapping sources of model errors 2 specification errors: (i) granularity. As credit risk modeling is an extremely broad topic, the scope of the survey is narrowed to particular asset classes and functions. We explore the data to identify what characteristics about a firm appear to coincide with the robustness and complexity of their approach to credit risk. There were 22 respondents.

Definition. Credit Portfolio Model denotes any mathematical framework that aims to emulate the evolution of credit systems. Model Types. There is a large variety of possible credit portfolio models reflecting the diversity of both underlying credit systems (the scope and nature of the collection of Debtor, Counterparty that comprise the Credit Portfolio). It is common to blame the inadequacy of credit risk models for the fact that the financial crisis has caught many market participants by surprise. On closer inspection, though, it often appears that market participants failed to understand or to use the models correctly. The recent events therefore do not invalidate traditional credit risk modeling as described in the first edition of the book.   Counterparty risk is a type (or sub-class) of credit risk and is the risk of default by the counterparty in many forms of derivative contracts. Let's contrast counterparty risk to loan default risk.   Credit risk models are commonly based on large internal data sets to produce reliable estimates of the probability of default (PD) that should be validated with time. However, in the real world, a substantial portion of the exposures is included in low-default portfolios (LDPs) in which the number of defaulted loans is usually much lower than the number of non-default observations. Modelling.

  The credit process inherently has risks. But as the OCC instructs examiners in its Comptroller’s Handbook, loan portfolio management is the process by which these inherent risks are managed and controlled. In a recent poll, bankers seem to feel lukewarm about their ability to measure and interpret their credit quality. In finance, model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial securities. However, model risk is more and more prevalent in activities other than financial securities valuation, such as assigning consumer credit scores, real-time probability prediction of fraudulent credit card. Credit Model Calibration. MountainView can assist you with your compliance with FASB’s current expected credit loss standard released last the new standard will be phased in over the next few years, we can help you implement a new CECL model which must incorporate historical information, current conditions and reasonable forecasts to estimate expected losses over the life of your.

Modelling and calibration errors in measures of portfolio credit risk by Nikola A. Tarashev Download PDF EPUB FB2

Modelling and calibration errors in measures of portfolio credit risk. [Nikola A Tarashev; Haibin Zhu; Bank for International Settlements. Monetary and Economic Department.] -- This paper develops an empirical procedure for analyzing the impact of model misspecification and calibration errors on measures of portfolio credit risk.

This paper develops an empirical procedure for analyzing the impact of model misspecification and calibration errors on measures of portfolio credit risk.

When applied to large simulated portfolios with realistic characteristics, this procedure reveals that violations of key assumptions of the well-known Asymptotic Single-Risk Factor (ASRF.

Specification and Calibration Errors in Measures of Portfolio Credit Risk: The Case of the ASRF Model [Tarashev, Nikola, Zhu, Haibun, International Journal of Central Banking] on *FREE* shipping on qualifying offers. This paper focuses on the asymptotic single-risk-factor (ASRF) model in order to analyze the impact of specification and calibration errors on popular measures Cited by: Request PDF | Modeling and Calibration Errors in Measures of Portfolio Credit Risk | This paper develops an empirical procedure for analyzing the impact of model misspecification and calibration.

A model-based assessment of credit risk is subject to both specification and calibration errors. Focusing on a well known credit risk model, we propose a methodology for quantifying the relative. Downloadable. This paper focuses on the asymptotic single-risk-factor (ASRF) model in order to analyze the impact of specification and calibration errors on popular measures of portfolio credit risk.

Violations of key assumptions of this model are found to be virtually inconsequential, especially for large, welldiversified portfolios. By contrast, flaws in the calibrated interdependence of.

Measuring portfolio credit risk: modelling versus calibration errors1 A model-based assessment of credit risk is subject to both specification and calibration errors.

Focusing on a well known credit risk model, we propose a methodology for quantifying the relative importance of alternative sources of such errors and apply this. tion and calibration errors on popular measures of portfolio credit risk. Violations of key assumptions of this model are found to be virtually inconsequential, especially for large, well-diversified portfolios.

By contrast, flaws in the calibrated inter-dependence of credit risk across exposures, caused by plausi-ble small-sample estimation. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper focuses on the asymptotic single-risk-factor (ASRF) model in order to analyze the impact of specification and calibration errors on popular measures of portfolio credit risk.

Violations of key assumptions of this model are found to be virtually inconsequential, especially for large, welldiversified portfolios. Credit Model Calibration – Post-Basel II –Maximising Data & Model Accuracy RiskMinds - Decem Amsterdam, Netherlands [email protected] Dr Scott.

Prior studies have examined multi-factor models for credit-risk portfolio and compared them with the one-factor model.

For example, Düllmann et al. () provide a comparison of the correlation and the VaR estimates among a one-factor model, a multi-factor model (based on the Moody’s KMV model), and the Basel II IRB model. Moody’s Analytics RiskFrontier™. To address the challenges faced by credit risk or credit portfolio managers, RiskFrontier models a credit investment’s value at the analysis date, its value distribution at some investment horizon, as well as the portfolio -referent risk of every instrument in the portfolio.

Downloadable. A model-based assessment of credit risk is subject to both specification and calibration errors. Focusing on a well known credit risk model, we propose a methodology for quantifying the relative importance of alternative sources of such errors and apply this methodology to a large data set.

We find that flawed calibration of the model can substantially affect the measured level. BibTeX @ARTICLE{Tarashev07modellingand, author = {Nikola Tarashev and Haibin Zhu and Nikola Tarashev and Haibin Zhu}, title = {Modelling and calibration errors in measures of portfolio credit risk}, journal = {BIS Working Paper}, year = {}, volume = {}}.

This paper focuses on the asymptotic single-risk-factor (ASRF) model in order to analyze the impact of specification and calibration errors on popular measures of portfolio credit risk. Violations of key assumptions of this model are found to be virtually inconsequential.

Summary Existing credit risk measurement techniques measure credit risks on a relative scale. The Basel II Accord attempts to transform relative risk measures into absolute risk measures.

To support the transformation process, the Accord has identified four drivers of credit risk: exposure, probability of default, loss given default, and maturity. Modelling and calibration errors in measures of portfolio credit risk.

By Nikola A. Tarashev and Haibin Zhu. Abstract. This paper develops an empirical procedure for analyzing the impact of model misspecification and calibration errors on measures of portfolio credit risk.

When applied to large simulated portfolios with realistic. Why should risk management systems account for parameter uncertainty.

In order to answer this question, this paper lets an investor in a credit portfolio face non-diversifiable estimation-driven uncertainty about two parameters: probability of default and asset-return correlation. Bayesian inference reveals that - for realistic assumptions about the portfolio's credit quality and the data.

Models vs Standard VaR and credit risk in the Trading Book Low sensitivity to extreme events Banking Book vs Trading Book Arbitrage Basel Stressed VaR IRC/CRM Hypo vs Actual Backtesting Impact of Basel Capital charge for wider asset classes (i.e Sovereign Increase of capital requirement for Internal Model Banking Book vs Trading Book.

Focusing on a well known credit risk model, we propose a methodology for quantifying the relative importance of alternative sources of such errors and apply this methodology to a large data set. We find that flawed calibration of the model can substantially affect the measured level of portfolio credit risk.

By contrast, a model. Model risk is a type of risk that occurs when a financial model used to measure a firm's market risks or value transactions fails or performs inadequately.

IFRS 9 and CECL Credit Risk Modelling and Validation covers a hot topic in risk management. Both IFRS 9 and CECL accounting standards require Banks to adopt a new perspective in assessing Expected Credit Losses.

The book explores a wide range of models and corresponding validation procedures. of the model. Some measure of the materiality of the model or portfolio should be included (common measures include the portfolio balance or exposure at default).

While the existence of a complete listing of models in use and associated materiality may seem like a basic component of risk management, it has been cited as a gap by. Modelling and calibration errors in measures of portfolio credit risk.

Abstract. errors in measures of portfolio credit ris Topics: JEL Classification Numbers, G21, G28, G13, C Keywords, Correlated defaults, value at risk, multiple common factors value at risk, multiple common factors. Wilson ). The measurement of portfolio credit risk in retail loan portfolios has not received as much attention.

In this paper, we develop a methodology to measure the credit risk of a retail portfolio. The method is based on the general portfolio credit risk framework described in Iscoe et al.

We discuss the practical estimation and. Typically, the dependencies among obligators in a credit portfolio model is modeled by the risk-factor framework, involving a set of latent common factors 𝑴= (𝑀.

1, 𝑀. 2, 𝑀. 𝑝) 𝑇. dri. However, the latter source of dependence is often lack of proper measures and has some overlapping with the former source. This book provides comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing importance for practitioners.

The last five years have witnessed a great momentum in the research into measures of financial risk. After many years of ad-hoc and non-consistent measures, now the problem is finally well formulated and some useful and very user-friendly solutions have been proposed.

These new measures of risk should be of great interest for investors, financial institutions as well as for regulators.

Section 3 details specific EDF9 model updates and changes, as well as practical implementations of the model. Section 4 discusses model changes pertaining to the calibration of the model’s DD-to-EDF transformation.

Section 5 provides general measures of model performance. Section 6. What is Credit Risk Modelling. Credit risk modeling refers to data driven risk models which calculates the chances of a borrower defaults on loan (or credit card).

If a borrower fails to repay loan, how much amount he/she owes at the time of default and how much lender would lose from the outstanding amount. General characteristics of commercial credit models – Strictly define what elements are considered in the measurement of credit risk – Strictly define the relative weight of those items considered in the measurement of credit risk – Can produce either a relative measure of credit risk or a specific measure of credit risk depending on whether.Pillar II - Modelling credit and concentration risk on the portfolio of a commercial bank FOCUS ON: Case studies, Credit Risk, The client was seeking to develop a more risk sensitive measure of credit and concentration risks for internal steering purposes, and to comply with supervisory requirements.

For example, available credit risk models often classify obligors into discrete rating categories. The simulations suggest that such approximations add little noise to measures of portfolio risk if the holdings of a portfolio are not dominated by individual borrowers, industries or other groups of borrowers with correlated estimation errors.