Market Risk Measurement, Beyond Value at Risk
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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. -
Var and Other Risk Measures
What is Risk? Risk Measures Methods of estimating risk measures Bibliography VaR and other Risk Measures Francisco Ramírez Calixto International Actuarial Association November 27th, 2018 Francisco Ramírez Calixto VaR and other Risk Measures What is Risk? Risk Measures Methods of estimating risk measures Bibliography Outline 1 What is Risk? 2 Risk Measures 3 Methods of estimating risk measures Francisco Ramírez Calixto VaR and other Risk Measures What is Risk? Risk Measures Methods of estimating risk measures Bibliography What is Risk? Risk 6= size of loss or size of a cost Risk lies in the unexpected losses. Francisco Ramírez Calixto VaR and other Risk Measures What is Risk? Risk Measures Methods of estimating risk measures Bibliography Types of Financial Risk In Basel III, there are three major broad risk categories: Credit Risk: Francisco Ramírez Calixto VaR and other Risk Measures What is Risk? Risk Measures Methods of estimating risk measures Bibliography Types of Financial Risk Operational Risk: Francisco Ramírez Calixto VaR and other Risk Measures What is Risk? Risk Measures Methods of estimating risk measures Bibliography Types of Financial Risk Market risk: Each one of these risks must be measured in order to allocate economic capital as a buer so that if a catastrophic event happens, the bank won't go bankrupt. Francisco Ramírez Calixto VaR and other Risk Measures What is Risk? Risk Measures Methods of estimating risk measures Bibliography Risk Measures Def. A risk measure is used to determine the amount of an asset or assets (traditionally currency) to be kept in reserve in order to cover for unexpected losses. -
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, -
Revised Standards for Minimum Capital Requirements for Market Risk by the Basel Committee on Banking Supervision (“The Committee”)
A revised version of this standard was published in January 2019. https://www.bis.org/bcbs/publ/d457.pdf Basel Committee on Banking Supervision STANDARDS Minimum capital requirements for market risk January 2016 A revised version of this standard was published in January 2019. https://www.bis.org/bcbs/publ/d457.pdf This publication is available on the BIS website (www.bis.org). © Bank for International Settlements 2015. All rights reserved. Brief excerpts may be reproduced or translated provided the source is stated. ISBN 978-92-9197-399-6 (print) ISBN 978-92-9197-416-0 (online) A revised version of this standard was published in January 2019. https://www.bis.org/bcbs/publ/d457.pdf Minimum capital requirements for Market Risk Contents Preamble ............................................................................................................................................................................................... 5 Minimum capital requirements for market risk ..................................................................................................................... 5 A. The boundary between the trading book and banking book and the scope of application of the minimum capital requirements for market risk ........................................................................................................... 5 1. Scope of application and methods of measuring market risk ...................................................................... 5 2. Definition of the trading book .................................................................................................................................. -
The Market Risk Premium: Expectational Estimates Using Analysts' Forecasts
The Market Risk Premium: Expectational Estimates Using Analysts' Forecasts Robert S. Harris and Felicia C. Marston Us ing expectatwnal data from f711a11cial a 11 a~r.11s. we e~ t ima t e a market risk premium for US stocks. Using the S&P 500 a.1 a pro1·1•.fin· the market portfolio. the Lll'erage market risk premium i.lfound to be 7. 14% abo1·e yields on /o11g-ter111 US go1·ern 111 e11t honds m·er the period I 982-l 99X. This ri~k premium 1•aries over time; much oft his 1·aria1io11 can he explained by either I he /e1 1el ofi11teres1 mies or readily availahle fonrard-looking proxies for ri.~k . Th e marke1 ri.1k p remium appears to 111 onz inversely with gol'ern111 e11 t interes1 ra/es .rngges1i11g Iha/ required rerurns 011 .~locks are more stable than interest rates themse!Pes. {JEL: GJI. G l 2] Sfhc notion of a market ri sk premium (th e spread choice has some appealing chara cteri sti cs but is between in vestor required returns on safe and average subject to many arb itrary assumptions such as the ri sk assets) has long played a central rol e in finance. 11 releva nt period for tak in g an average. Compound ing is a key factor in asset allocation decisions to determine the difficulty or usi ng historical returns is the we ll the portfolio mi x of debt and equity instruments. noted fa ct that stand ard model s or consum er choice Moreover, the market ri sk premium plays a critica l ro le would predi ct much lower spreads between equity and in th e Capital Asset Pricing Model (CAPM ), the most debt returns than have occurred in US markets- the widely used means of estimating equity hurdle rates by so ca lled equity risk premium puzzle (sec Welch, 2000 practitioners. -
Comparative Analyses of Expected Shortfall and Value-At-Risk Under Market Stress1
Comparative analyses of expected shortfall and value-at-risk under market stress1 Yasuhiro Yamai and Toshinao Yoshiba, Bank of Japan Abstract In this paper, we compare value-at-risk (VaR) and expected shortfall under market stress. Assuming that the multivariate extreme value distribution represents asset returns under market stress, we simulate asset returns with this distribution. With these simulated asset returns, we examine whether market stress affects the properties of VaR and expected shortfall. Our findings are as follows. First, VaR and expected shortfall may underestimate the risk of securities with fat-tailed properties and a high potential for large losses. Second, VaR and expected shortfall may both disregard the tail dependence of asset returns. Third, expected shortfall has less of a problem in disregarding the fat tails and the tail dependence than VaR does. 1. Introduction It is a well known fact that value-at-risk2 (VaR) models do not work under market stress. VaR models are usually based on normal asset returns and do not work under extreme price fluctuations. The case in point is the financial market crisis of autumn 1998. Concerning this crisis, CGFS (1999) notes that “a large majority of interviewees admitted that last autumn’s events were in the “tails” of distributions and that VaR models were useless for measuring and monitoring market risk”. Our question is this: Is this a problem of the estimation methods, or of VaR as a risk measure? The estimation methods used for standard VaR models have problems for measuring extreme price movements. They assume that the asset returns follow a normal distribution. -
Capital Adequacy Requirements (CAR)
Guideline Subject: Capital Adequacy Requirements (CAR) Chapter 3 – Credit Risk – Standardized Approach Effective Date: November 2017 / January 20181 The Capital Adequacy Requirements (CAR) for banks (including federal credit unions), bank holding companies, federally regulated trust companies, federally regulated loan companies and cooperative retail associations are set out in nine chapters, each of which has been issued as a separate document. This document, Chapter 3 – Credit Risk – Standardized Approach, should be read in conjunction with the other CAR chapters which include: Chapter 1 Overview Chapter 2 Definition of Capital Chapter 3 Credit Risk – Standardized Approach Chapter 4 Settlement and Counterparty Risk Chapter 5 Credit Risk Mitigation Chapter 6 Credit Risk- Internal Ratings Based Approach Chapter 7 Structured Credit Products Chapter 8 Operational Risk Chapter 9 Market Risk 1 For institutions with a fiscal year ending October 31 or December 31, respectively Banks/BHC/T&L/CRA Credit Risk-Standardized Approach November 2017 Chapter 3 - Page 1 Table of Contents 3.1. Risk Weight Categories ............................................................................................. 4 3.1.1. Claims on sovereigns ............................................................................... 4 3.1.2. Claims on unrated sovereigns ................................................................. 5 3.1.3. Claims on non-central government public sector entities (PSEs) ........... 5 3.1.4. Claims on multilateral development banks (MDBs) -
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). -
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). -
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). -
Value-At-Risk (Var) Application at Hypothetical Portfolios in Jakarta Islamic Index
VALUE-AT-RISK (VAR) APPLICATION AT HYPOTHETICAL PORTFOLIOS IN JAKARTA ISLAMIC INDEX Dewi Tamara1 BINUS University International, Jakarta Grigory Ryabtsev2 BINUS University International, Jakarta ABSTRACT The paper is an exploratory study to apply the method of historical simulation based on the concept of Value at Risk on hypothetical portfolios on Jakarta Islamic Index (JII). Value at Risk is a tool to measure a portfolio’s exposure to market risk. We construct four portfolios based on the frequencies of the companies in Jakarta Islamic Index on the period of 1 January 2008 to 2 August 2010. The portfolio A has 12 companies, Portfolio B has 9 companies, portfolio C has 6 companies and portfolio D has 4 companies. We put the initial investment equivalent to USD 100 and use the rate of 1 USD=Rp 9500. The result of historical simulation applied in the four portfolios shows significant increasing risk on the year 2008 compared to 2009 and 2010. The bigger number of the member in one portfolio also affects the VaR compared to smaller member. The level of confidence 99% also shows bigger loss compared to 95%. The historical simulation shows the simplest method to estimate the event of increasing risk in Jakarta Islamic Index during the Global Crisis 2008. Keywords: value at risk, market risk, historical simulation, Jakarta Islamic Index. 1 Faculty of Business, School of Accounting & Finance - BINUS Business School, [email protected] 2 Alumni of BINUS Business School Tamara, D. & Ryabtsev, G. / Journal of Applied Finance and Accounting 3(2) 153-180 (153 INTRODUCTION Modern portfolio theory aims to allocate assets by maximising the expected risk premium per unit of risk. -
Legal Risk Section 2070.1
Legal Risk Section 2070.1 An institution’s trading and capital-markets will prove unenforceable. Many trading activi- activities can lead to significant legal risks. ties, such as securities trading, commonly take Failure to correctly document transactions can place without a signed agreement, as each indi- result in legal disputes with counterparties over vidual transaction generally settles within a very the terms of the agreement. Even if adequately short time after the trade. The trade confirma- documented, agreements may prove to be unen- tions generally provide sufficient documentation forceable if the counterparty does not have the for these transactions, which settle in accor- authority to enter into the transaction or if the dance with market conventions. Other trading terms of the agreement are not in accordance activities involving longer-term, more complex with applicable law. Alternatively, the agree- transactions may necessitate more comprehen- ment may be challenged on the grounds that the sive and detailed documentation. Such documen- transaction is not suitable for the counterparty, tation ensures that the institution and its coun- given its level of financial sophistication, finan- terparty agree on the terms applicable to the cial condition, or investment objectives, or on transaction. In addition, documentation satisfies the grounds that the risks of the transaction were other legal requirements, such as the ‘‘statutes of not accurately and completely disclosed to the frauds’’ that may apply in many jurisdictions. investor. Statutes of frauds generally require signed, writ- As part of sound risk management, institu- ten agreements for certain classes of contracts, tions should take steps to guard themselves such as agreements with a duration of more than against legal risk.