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Guidance on Operational Risk BIA And
CENTRAL BANK OF NIGERIA Guidance Notes on the Calculation of Capital Requirement for Operational Risk Basic Indicator Approach (BIA) and the Standardized Approach (TSA) TABLE OF CONTENTS OPERATIONAL RISK CAPITAL REQUIREMENT ........................................................................... 3 1.0 INTRODUCTION ........................................................................................................................ 3 1.1 Calculation Approaches ................................................................................................................. 3 1.2 Adoption of Approaches ................................................................................................................. 4 2.0 GOVERNANCE AND MANAGEMENT OF OPERATIONAL RISKS ................................... 4 2.1 Board and Management ................................................................................................................ 4 2.3 Processes and Procedure ............................................................................................................... 4 2.4 Reversion of Approaches ................................................................................................................ 5 2.5 Sound Practices for Operational Risk Management ........................................................... 5 3.0 BASIC INDICATOR APPROACH (BIA) ................................................................................. 5 3.1 Calculation Method ......................................................................................................................... -
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. -
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. -
Estimating Value at Risk
Estimating Value at Risk Eric Marsden <[email protected]> Do you know how risky your bank is? Learning objectives 1 Understand measures of financial risk, including Value at Risk 2 Understand the impact of correlated risks 3 Know how to use copulas to sample from a multivariate probability distribution, including correlation The information presented here is pedagogical in nature and does not constitute investment advice! Methods used here can also be applied to model natural hazards 2 / 41 Warmup. Before reading this material, we suggest you consult the following associated slides: ▷ Modelling correlations using Python ▷ Statistical modelling with Python Available from risk-engineering.org 3 / 41 Risk in finance There are 1011 stars in the galaxy. That used to be a huge number. But it’s only a hundred billion. It’s less than the national deficit! We used to call them astronomical numbers. ‘‘ Now we should call them economical numbers. — Richard Feynman 4 / 41 Terminology in finance Names of some instruments used in finance: ▷ A bond issued by a company or a government is just a loan • bond buyer lends money to bond issuer • issuer will return money plus some interest when the bond matures ▷ A stock gives you (a small fraction of) ownership in a “listed company” • a stock has a price, and can be bought and sold on the stock market ▷ A future is a promise to do a transaction at a later date • refers to some “underlying” product which will be bought or sold at a later time • example: farmer can sell her crop before harvest, -
The History and Ideas Behind Var
Available online at www.sciencedirect.com ScienceDirect Procedia Economics and Finance 24 ( 2015 ) 18 – 24 International Conference on Applied Economics, ICOAE 2015, 2-4 July 2015, Kazan, Russia The history and ideas behind VaR Peter Adamkoa,*, Erika Spuchľákováb, Katarína Valáškováb aDepartment of Quantitative Methods and Economic Informatics, Faculty of Operations and Economic of Transport and Communications, University of Žilina, Univerzitná 1, 010 26 Žilina, Slovakia b,cDepartment of Economics, Faculty of Operations and Economic of Transport and Communications, University of Žilina, Univerzitná 1, 010 26 Žilina, Slovakia Abstract The value at risk is one of the most essential risk measures used in the financial industry. Even though from time to time criticized, the VaR is a valuable method for many investors. This paper describes how the VaR is computed in practice, and gives a short overview of value at risk history. Finally, paper describes the basic types of methods and compares their similarities and differences. © 20152015 The The Authors. Authors. Published Published by Elsevier by Elsevier B.V. This B.V. is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or and/or peer peer-review-review under under responsibility responsibility of the Organizing of the Organizing Committee Committee of ICOAE 2015. of ICOAE 2015. Keywords: Value at risk; VaR; risk modeling; volatility; 1. Introduction The risk management defines risk as deviation from the expected result (as negative as well as positive). Typical indicators of risk are therefore statistical concepts called variance or standard deviation. These concepts are the cornerstone of the amount of financial theories, Cisko and Kliestik (2013). -
Capital Requirements Directive IV Framework Operational Risk Allen & Overy Client Briefing Paper 13 | January 2014
Capital Requirements Directive IV Framework Operational Risk Allen & Overy Client Briefing Paper 13 | January 2014 www.allenovery.com 2 CRD IV Framework: Operational Risk | January 2014 CRD IV Framework: Operational Risk This briefing paper is part of a series of briefings on relevant to the in-house lawyer. This briefing is for the implementation of Basel III in Europe via the general guidance only and does not constitute Capital Requirements Directive IV1 (CRD IV) and definitive advice. the Capital Requirements Regulation2 (CRR), replacing the Banking Consolidation Directive3 and NOTE: In relation to the topics discussed in the Capital Adequacy Directive.4 The legislation is this briefing, the CRR contains a number of highly complex: these briefings are intended to discretions for member states in relation to provide a high-level overview of the architecture of national implementation. The regime may the regulatory capital and liquidity framework and therefore differ across member states in a to draw attention to the legal issues likely to be number of respects. 1 2013/36/EU. This briefing paper is based on information 2 Regulation 575/2013. 3 2006/48/EU. available as of 17 January 2014. 4 2006/49/EU. Background and scope Sources Basel II introduced a requirement for credit institutions CRD IV (Directive 2013/36/EU): Article 85. and investment firms to hold capital specifically to cover operational risk losses. Operational risk (defined CRR (Regulation 575/2013): Article 20, Article 95, as the risk of loss resulting from inadequate or failed Title III (Articles 312-324), Article 446, Article 454, internal processes, people and systems or from external Article 500, Recital (52). -
Draft Guidance Notes on Operational Risk Calculation.Pdf
CENTRAL BANK OF NIGERIA GUIDANCE NOTES ON THE CALCULATION OF CAPITAL REQUIREMENT FOR OPERATIONAL RISK FOR NON-INTEREST FINANCIAL INSTITUTIONS IN NIGERIA BASIC INDICATOR APPROACH AND THE STANDARDIZED APPROACH AUGUST, 2018 TABLE OF CONTENTS Definition of terms ............................................................................................................... 3 1.0 Operational Risk Capital Requirement ....................................................................... 4 1.1 Introduction ............................................................................................................ 4 1.2 Calculation Approaches ......................................................................................... 4 1.3 Adoption of Approaches ......................................................................................... 4 2.0 GOVERNANCE AND MANAGEMENT OF OPERATIONAL RISKS .................... 4 2.1 Board, Management and Advisory Committee of Experts (ACE)........................ 4 2.2 Processes and Procedures ....................................................................................... 5 2.3 Reversion of Approaches ........................................................................................ 5 2.4 Sound Practices for Operational Risk Management ............................................. 5 3.0 BASIC INDICATOR APPROACH (BIA) ................................................................... 5 3.1 Calculation Method ............................................................................................... -
Value-At-Risk Under Systematic Risks: a Simulation Approach
International Research Journal of Finance and Economics ISSN 1450-2887 Issue 162 July, 2017 http://www.internationalresearchjournaloffinanceandeconomics.com Value-at-Risk Under Systematic Risks: A Simulation Approach Arthur L. Dryver Graduate School of Business Administration National Institute of Development Administration, Thailand E-mail: [email protected] Quan N. Tran Graduate School of Business Administration National Institute of Development Administration, Thailand Vesarach Aumeboonsuke International College National Institute of Development Administration, Thailand Abstract Daily Value-at-Risk (VaR) is frequently reported by banks and financial institutions as a result of regulatory requirements, competitive pressures, and risk management standards. However, recent events of economic crises, such as the US subprime mortgage crisis, the European Debt Crisis, and the downfall of the China Stock Market, haveprovided strong evidence that bad days come in streaks and also that the reported daily VaR may be misleading in order to prevent losses. This paper compares VaR measures of an S&P 500 index portfolio under systematic risks to ones under normal market conditions using a historical simulation method. Analysis indicates that the reported VaR under normal conditions seriously masks and understates the true losses of the portfolio when systematic risks are present. For highly leveraged positions, this means only one VaR hit during a single day or a single week can wipe the firms out of business. Keywords: Value-at-Risks, Simulation methods, Systematic Risks, Market Risks JEL Classification: D81, G32 1. Introduction Definition Value at risk (VaR) is a common statistical technique that is used to measure the dollar amount of the potential downside in value of a risky asset or a portfolio from adverse market moves given a specific time frame and a specific confident interval (Jorion, 2007). -
(RCAP) Assessment of Basel III Regulations –
Basel Committee on Banking Supervision Regulatory Consistency Assessment Programme (RCAP) Assessment of Basel III regulations – United States of America December 2014 This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2014. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISBN 978-92-9197-012-4 (print) ISBN 978-92-9197-011-7 (online) Contents Glossary ................................................................................................................................................................................................ 1 Preface ................................................................................................................................................................................................ 3 Executive summary ........................................................................................................................................................................... 5 Response from United States ....................................................................................................................................................... 7 1 Context, scope and main assessment findings ................................................................................................... 8 1.1 Context ............................................................................................................................................................................... -
Regulatory Capital and Risk Management Pillar 3 Disclosures
2021 HSBC Bank Canada Regulatory Capital & Risk Management Pillar 3 Supplementary Disclosures As at March 31, 2021 HSBC Bank Canada Pillar 3 Supplementary Disclosure First Quarter 2021 1 Contents Page Notes to users 3 Road map to Pillar 3 disclosures 4 Capital and RWA 5 Regulatory Capital Disclosure - CC1 5 Overview of Risk Weighted Assets (RWA) - OV1 6 Credit Risk 6 RWA flow statements of credit risk exposures under IRB - CR8 6 Market Risk 6 RWA flow statements of market risk exposures under an Internal Model Approach (IMA) - MR2 7 Leverage Ratio 7 Summary comparison of accounting assets vs. leverage ratio exposure measure (LR1) 7 Leverage Ratio Common Disclosure (LR2) 8 Glossary 9 2 HSBC Bank Canada Pillar 3 Supplementary Disclosure First Quarter 2021 Notes to users Regulatory Capital and Risk Management Pillar 3 Disclosures The Office of the Superintendent of Financial Institutions (“OSFI”) supervises HSBC Bank Canada (the “Bank”) on a consolidated basis. OSFI has approved the Bank’s application to apply the Advanced Internal Ratings Based (“AIRB”) approach to credit risk on our portfolio and the Standardized Approach for measuring Operational Risk. Please refer to the Annual Report and Accounts 2020 for further information on the Bank’s risk and capital management framework. Further information regarding HSBC Group Risk Management Processes can be found in HSBC Holdings plc Capital and Risk Management Pillar 3 Disclosures available on HSBC Group’s investor relations web site. The Pillar 3 Supplemental Disclosures are additional summary -
How to Analyse Risk in Securitisation Portfolios: a Case Study of European SME-Loan-Backed Deals1
18.10.2016 Number: 16-44a Memo How to Analyse Risk in Securitisation Portfolios: A Case Study of European SME-loan-backed deals1 Executive summary Returns on securitisation tranches depend on the performance of the pool of assets against which the tranches are secured. The non-linear nature of the dependence can create the appearance of regime changes in securitisation return distributions as tranches move more or less “into the money”. Reliable risk management requires an approach that allows for this non-linearity by modelling tranche returns in a ‘look through’ manner. This involves modelling risk in the underlying loan pool and then tracing through the implications for the value of the securitisation tranches that sit on top. This note describes a rigorous method for calculating risk in securitisation portfolios using such a look through approach. Pool performance is simulated using Monte Carlo techniques. Cash payments are channelled to different tranches based on equations describing the cash flow waterfall. Tranches are re-priced using statistical pricing functions calibrated through a prior Monte Carlo exercise. The approach is implemented within Risk ControllerTM, a multi-asset-class portfolio model. The framework permits the user to analyse risk return trade-offs and generate portfolio-level risk measures (such as Value at Risk (VaR), Expected Shortfall (ES), portfolio volatility and Sharpe ratios), and exposure-level measures (including marginal VaR, marginal ES and position-specific volatilities and Sharpe ratios). We implement the approach for a portfolio of Spanish and Portuguese SME exposures. Before the crisis, SME securitisations comprised the second most important sector of the European market (second only to residential mortgage backed securitisations).