Credit Risk Measurement: Avoiding Unintended Results Part 1
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Basel III: Post-Crisis Reforms
Basel III: Post-Crisis Reforms Implementation Timeline Focus: Capital Definitions, Capital Focus: Capital Requirements Buffers and Liquidity Requirements Basel lll 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 1 January 2022 Full implementation of: 1. Revised standardised approach for credit risk; 2. Revised IRB framework; 1 January 3. Revised CVA framework; 1 January 1 January 1 January 1 January 1 January 2018 4. Revised operational risk framework; 2027 5. Revised market risk framework (Fundamental Review of 2023 2024 2025 2026 Full implementation of Leverage Trading Book); and Output 6. Leverage Ratio (revised exposure definition). Output Output Output Output Ratio (Existing exposure floor: Transitional implementation floor: 55% floor: 60% floor: 65% floor: 70% definition) Output floor: 50% 72.5% Capital Ratios 0% - 2.5% 0% - 2.5% Countercyclical 0% - 2.5% 2.5% Buffer 2.5% Conservation 2.5% Buffer 8% 6% Minimum Capital 4.5% Requirement Core Equity Tier 1 (CET 1) Tier 1 (T1) Total Capital (Tier 1 + Tier 2) Standardised Approach for Credit Risk New Categories of Revisions to the Existing Standardised Approach Exposures • Exposures to Banks • Exposure to Covered Bonds Bank exposures will be risk-weighted based on either the External Credit Risk Assessment Approach (ECRA) or Standardised Credit Risk Rated covered bonds will be risk Assessment Approach (SCRA). Banks are to apply ECRA where regulators do allow the use of external ratings for regulatory purposes and weighted based on issue SCRA for regulators that don’t. specific rating while risk weights for unrated covered bonds will • Exposures to Multilateral Development Banks (MDBs) be inferred from the issuer’s For exposures that do not fulfil the eligibility criteria, risk weights are to be determined by either SCRA or ECRA. -
Predicting the Probability of Corporate Default Using Logistic Regression
UGC Approval No:40934 CASS-ISSN:2581-6403 Predicting the Probability of Corporate HEB CASS Default using Logistic Regression *Deepika Verma *Research Scholar, School of Management Science, Department of Commerce, IGNOU Address for Correspondence: [email protected] ABSTRACT This paper studies the probability of default of Indian Listed companies. Study aims to develop a model by using Logistic regression to predict the probability of default of corporate. Study has undertaken 90 Indian Companies listed in BSE; financial data is collected for the financial year 2010- 2014. Financial ratios have been considered as the independent predictors of the defaulting companies. The empirical result demonstrates that the model is best fit and well capable to predict the default on loan with 92% of accuracy. Access this Article Online http://heb-nic.in/cass-studies Quick Response Code: Received on 25/03/2019 Accepted on 11/04/2019@HEB All rights reserved April 2019 – Vol. 3, Issue- 1, Addendum 7 (Special Issue) Page-161 UGC Approval No:40934 CASS-ISSN:2581-6403 INTRODUCTION Credit risk is eternal for the banking and financial institutions, there is always an uncertainty in receiving the repayments of the loans granted to the corporate (Sirirattanaphonkun&Pattarathammas, 2012). Many models have been developed for predicting the corporate failures in advance. To run the economy on the right pace the banking system of that economy must be strong enough to gauge the failure of the corporate in the repayment of loan. Financial crisis has been witnessed across the world from Latin America, to Asia, from Nordic Countries to East and Central European countries (CIMPOERU, 2015). -
Contagion in the Interbank Market with Stochastic Loss Given Default∗
Contagion in the Interbank Market with Stochastic Loss Given Default∗ Christoph Memmel,a Angelika Sachs,b and Ingrid Steina aDeutsche Bundesbank bLMU Munich This paper investigates contagion in the German inter- bank market under the assumption of a stochastic loss given default (LGD). We combine a unique data set about the LGD of interbank loans with detailed data about interbank expo- sures. We find that the frequency distribution of the LGD is markedly U-shaped. Our simulations show that contagion in the German interbank market may happen. For the point in time under consideration, the assumption of a stochastic LGD leads on average to a more fragile banking system than under the assumption of a constant LGD. JEL Codes: D53, E47, G21. 1. Introduction The collapse of Lehman Brothers turned the 2007/2008 turmoil into a deep global financial crisis. But even before the Lehman default, interbank markets ceased to function properly. In particular, the ∗The views expressed in this paper are those of the authors and do not nec- essarily reflect the opinions of the Deutsche Bundesbank. We thank Gabriel Frahm, Gerhard Illing, Ulrich Kr¨uger, Peter Raupach, Sebastian Watzka, two anonymous referees, and the participants at the Annual Meeting of the Euro- pean Economic Association 2011, the Annual Meeting of the German Finance Association 2011, the 1st Conference of the MaRs Network of the ESCB, the FSC workshop on stress testing and network analysis, as well as the research seminars of the Deutsche Bundesbank and the University of Munich for valu- able comments. Author contact: Memmel and Stein: Deutsche Bundesbank, Wilhelm-Epstein-Strasse 14, D-60431 Frankfurt, Germany; Tel: +49 (0) 69 9566 8531 (Memmel), +49 (0) 69 9566 8348 (Stein). -
Chapter 5 Credit Risk
Chapter 5 Credit risk 5.1 Basic definitions Credit risk is a risk of a loss resulting from the fact that a borrower or counterparty fails to fulfill its obligations under the agreed terms (because he or she either cannot or does not want to pay). Besides this definition, the credit risk also includes the following risks: Sovereign risk is the risk of a government or central bank being unwilling or • unable to meet its contractual obligations. Concentration risk is the risk resulting from the concentration of transactions with • regard to a person, a group of economically associated persons, a government, a geographic region or an economic sector. It is the risk associated with any single exposure or group of exposures with the potential to produce large enough losses to threaten a bank's core operations, mainly due to a low level of diversification of the portfolio. Settlement risk is the risk resulting from a situation when a transaction settlement • does not take place according to the agreed conditions. For example, when trading bonds, it is common that the securities are delivered two days after the trade has been agreed and the payment has been made. The risk that this delivery does not occur is called settlement risk. Counterparty risk is the credit risk resulting from the position in a trading in- • strument. As an example, this includes the case when the counterparty does not honour its obligation resulting from an in-the-money option at the time of its ma- turity. It also important to note that the credit risk is related to almost all types of financial instruments. -
An Explanatory Note on the Basel II IRB Risk Weight Functions
Basel Committee on Banking Supervision An Explanatory Note on the Basel II IRB Risk Weight Functions July 2005 Requests for copies of publications, or for additions/changes to the mailing list, should be sent to: Bank for International Settlements Press & Communications CH-4002 Basel, Switzerland E-mail: [email protected] Fax: +41 61 280 9100 and +41 61 280 8100 © Bank for International Settlements 20054. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISBN print: 92-9131-673-3 Table of Contents 1. Introduction......................................................................................................................1 2. Economic foundations of the risk weight formulas ..........................................................1 3. Regulatory requirements to the Basel credit risk model..................................................4 4. Model specification..........................................................................................................4 4.1. The ASRF framework.............................................................................................4 4.2. Average and conditional PDs.................................................................................5 4.3. Loss Given Default.................................................................................................6 4.4. Expected versus Unexpected Losses ....................................................................7 4.5. Asset correlations...................................................................................................8 -
What Do One Million Credit Line Observations Tell Us About Exposure at Default? a Study of Credit Line Usage by Spanish Firms
What Do One Million Credit Line Observations Tell Us about Exposure at Default? A Study of Credit Line Usage by Spanish Firms Gabriel Jiménez Banco de España [email protected] Jose A. Lopez Federal Reserve Bank of San Francisco [email protected] Jesús Saurina Banco de España [email protected] DRAFT…….Please do not cite without authors’ permission Draft date: June 16, 2006 ABSTRACT Bank credit lines are a major source of funding and liquidity for firms and a key source of credit risk for the underwriting banks. In fact, credit line usage is addressed directly in the current Basel II capital framework through the exposure at default (EAD) calculation, one of the three key components of regulatory capital calculations. Using a large database of Spanish credit lines across banks and years, we model the determinants of credit line usage by firms. We find that the risk profile of the borrowing firm, the risk profile of the lender, and the business cycle have a significant impact on credit line use. During recessions, credit line usage increases, particularly among the more fragile borrowers. More importantly, we provide robust evidence of more intensive use of credit lines by borrowers that later default on those lines. Our data set allows us to enter the policy debate on the EAD components of the Basel II capital requirements through the calculation of credit conversion factors (CCF) and loan equivalent exposures (LEQ). We find that EAD exhibits procyclical characteristics and is affected by credit line characteristics, such as commitment size, maturity, and collateral requirements. -
Internal Rating Based Approach- Regulatory Expectations
1 Internal Rating Based Approach- Regulatory Expectations Ajay Kumar Choudhary General Manager Department of Banking Operation and Development Reserve Bank of India What was Basel 2 designed to achieve? • Risk sensitive minimum bank capital requirements • A framework for formal dialogue with your regulator • Disclosures to enhance market discipline 3 Basel II • Implemented in two stages. • Foreign banks operating in India and the Indian banks having operational presence outside India migrated to Basel II from March 31, 2008. • All other scheduled commercial banks migrated to these approaches from March 31, 2009. • Followed Standardized Approach for Credit Risk and Basic Indicator Approach for Operational Risk. • As regards market risk, banks continued to follow SMM, adopted under Basel I framework, under Basel II also. • All the scheduled commercial banks in India have been Basel II compliant as per the standardised approach with effect from April 1, 2009. 4 Journey towards Basel II Advanced Approaches • In July 2009, the time table for the phased adoption of advanced approaches had also been put in public domain. • Banks desirous of moving to advanced approaches under Basel II have been advised that they can apply for migrating to advanced approaches of Basel II for capital calculation on a voluntary basis based on their preparedness and subject to RBI approval. • The appropriate guidelines for advanced approaches of market risk (IMA), operational risk (AMA) and credit risk (IRB) were issued in April 2010, April 2011 and December 2011 respectively. 5 Status of Indian banks towards implementation of Basel II advanced approaches • Credit Risk • Initially 14 banks had applied to RBI for adoption of Internal Rating Based (IRB) Approaches of credit risk capital calculation under Basel II framework. -
Estimating Loss Given Default Based on Time of Default
ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS { N. 44{2020 (1017{1032) 1017 Estimating loss given default based on time of default Jamil J. Jaber∗ The University of Jordan Department of Risk Management and Insurance Jordan Aqaba Branch and Department of Risk Management & Insurance The University of Jordan Jordan [email protected] [email protected] Noriszura Ismail National University of Malaysia School of Mathematical Sciences Malaysia Siti Norafidah Mohd Ramli National University of Malaysia School of Mathematical Sciences Malaysia Baker Albadareen National University of Malaysia School of Mathematical Sciences Malaysia Abstract. The Basel II capital structure requires a minimum capital to cover the exposures of credit, market, and operational risks in banks. The Basel Committee gives three methodologies to estimate the required capital; standardized approach, Internal Ratings-Based (IRB) approach, and Advanced IRB approach. The IRB approach is typically favored contrasted with the standard approach because of its higher accuracy and lower capital charges. The loss given default (LGD) is a key parameter in credit risk management. The models are fit to a sample data of credit portfolio obtained from a bank in Jordan for the period of January 2010 until December 2014. The best para- metric models are selected using several goodness-of-fit criteria. The results show that LGD fitted with Gamma distribution. After that, the financial variables as a covariate that affect on two parameters in Gamma regression will find. Keywords: Credit risk, LGD, Survival model, Gamma regression ∗. Corresponding author 1018 J.J. JABER, N. ISMAIL, S. NORAFIDAH MOHD RAMLI and B. ALBADAREEN 1. Introduction Survival analysis is a statistical method whose outcome variable of interest is the time to the occurrence of an event which is often referred to as failure time, survival time, or event time. -
2019 Q1 Results
Glossary of terms ‘A-IRB’ / ‘Advanced-Internal Ratings Based’ See ‘Internal Ratings Based (IRB)’. ‘ABS CDO Super Senior’ Super senior tranches of debt linked to collateralised debt obligations of asset backed securities (defined below). Payment of super senior tranches takes priority over other obligations. ‘Acceptances and endorsements’ An acceptance is an undertaking by a bank to pay a bill of exchange drawn on a customer. The Barclays Group expects most acceptances to be presented, but reimbursement by the customer is normally immediate. Endorsements are residual liabilities of the Barclays Group in respect of bills of exchange which have been paid and subsequently rediscounted. ‘Additional Tier 1 (AT1) capital’ AT1 capital largely comprises eligible non-common equity capital securities and any related share premium. ‘Additional Tier 1 (AT1) securities’ Non-common equity securities that are eligible as AT1 capital. ‘Advanced Measurement Approach (AMA)’ Under the AMA, banks are allowed to develop their own empirical model to quantify required capital for operational risk. Banks can only use this approach subject to approval from their local regulators. ‘Agencies’ Bonds issued by state and / or government agencies or government-sponsored entities. ‘Agency Mortgage-Backed Securities’ Mortgage-Backed Securities issued by government-sponsored entities. ‘All price risk (APR)’ An estimate of all the material market risks, including rating migration and default for the correlation trading portfolio. ‘American Depository Receipts (ADR)’ A negotiable certificate that represents the ownership of shares in a non-US company (for example Barclays) trading in US financial markets. ‘Americas’ Geographic segment comprising the USA, Canada and countries where Barclays operates within Latin America. -
Practical Application Examples from the IAA Banking Webinar
INTERNATIONAL ACTUARIAL ASSOCIATION Practical Application Examples for the Banking Webinar 29 March 2017 16:00 SAST, 15:00 BST 1. Practical Application: Expected Credit Loss Modelling where actuarial and statistical principles are heavily applied. Expected Loss = PD x LGD x EAD Estimating Probability of Default (“PD”) • One common methodology for estimating the PD is to develop a rating scorecard, which aims to assign scores to borrowers based on a list of factors. These scorecards can be “application” or “behavioural” in nature, and hence the factors captured will reflect different characteristics. Application scorecards are used at the point of origination, and therefore look to capture the default risk on new loans, while behavioural scorecards look to capture risks on loans that have been on the book for at least 6 months. • Scorecard models are usually based on regression equations with a logit link, so as to restrict the PD between 0 and 1. Alternatively, a linear or non-linear equation can be used to derive the score, which is then mapped to the Bank’s estimate of PD using a matrix. For example, a score of 700 may refer to a PD of 2%. • Another common methodology is to use a roll-rate or transition matrix approach. Under this approach, the Bank will measure the migrations (between credit ratings, or delinquency indicators) over a fixed observation window. The same group of loans is therefore monitored from the start of the period to the end to obtain the proportions moving from one state to the next. The Bank is then able to estimate the likelihood of a loan in any state moving to another state, in this case default. -
Probability of Default (PD) Is the Core Credit Product of the Credit Research Initiative (CRI)
2019 The Credit Research Initiative (CRI) National University of Singapore First version: March 2, 2017, this version: January 8, 2019 Probability of Default (PD) is the core credit product of the Credit Research Initiative (CRI). The CRI system is built on the forward intensity model developed by Duan et al. (2012, Journal of Econometrics). This white paper describes the fundamental principles and the implementation of the model. Details of the theoretical foundations and numerical realization are presented in RMI-CRI Technical Report (Version 2017). This white paper contains five sections. Among them, Sections II & III describe the methodology and performance of the model respectively, and Section IV relates to the examples of how CRI PD can be used. ........................................................................ 2 ............................................................... 3 .................................................. 8 ............................................................... 11 ................................................................. 14 .................... 15 Please cite this document in the following way: “The Credit Research Initiative of the National University of Singapore (2019), Probability of Default (PD) White Paper”, Accessible via https://www.rmicri.org/en/white_paper/. 1 | Probability of Default | White Paper Probability of Default (PD) is the core credit measure of the CRI corporate default prediction system built on the forward intensity model of Duan et al. (2012)1. This forward -
Mortgage Finance and Climate Change: Securitization Dynamics in the Aftermath of Natural Disasters
NBER WORKING PAPER SERIES MORTGAGE FINANCE AND CLIMATE CHANGE: SECURITIZATION DYNAMICS IN THE AFTERMATH OF NATURAL DISASTERS Amine Ouazad Matthew E. Kahn Working Paper 26322 http://www.nber.org/papers/w26322 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 2019, Revised February 2021 We thank Jeanne Sorin for excellent research assistance in the early stages of the project. We would like to thank Asaf Bernstein, Justin Contat, Thomas Davido , Matthew Eby, Lynn Fisher, Ambika Gandhi, Richard K. Green, Jesse M. Keenan, Michael Lacour-Little, William Larson, Stephen Oliner, Ying Pan, Tsur Sommerville, Stan Veuger, James Vickery, Susan Wachter, for comments on early versions of our paper, as well as the audience of the seminar series at George Washington University, the 2018 annual meeting of the Urban Economics Association at Columbia University, Stanford University, the Canadian Urban Economics Conference, the University of Southern California, the American Enterprise Institute. The usual disclaimers apply. Amine Ouazad would like to thank the HEC Montreal Foundation for nancial support through the research professorship in Urban and Real Estate Economics. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2019 by Amine Ouazad and Matthew E. Kahn. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.