New Risk Challenges and Enduring Themes for the Return
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Confronting Financial Crisis: Dodd-Frank's Dangers and the Case for a Systemic Emergency Insurance Fund
Confronting Financial Crisis: Dodd-Frank's Dangers and the Case for a Systemic Emergency Insurance Fund Jeffrey N. Gordon & Christopher Mullert Inherent tensions in the financial sector mean that episodes of extreme stress are inevitable, if unpredictable. This is true even when financial regulatory and supervisory regimes are effective in many respects. The government's capacity to intervene may determine whether distress is confined to the financial sector or breaks out into the real economy Although adequate resolution authority to address a failingfinancialfirm is a necessary objective of the current regulatory reforms, a firm-by-firm approach cannot address a major systemic failure. Major blows to the financial system, such as the financial crisis of 2007-2009, may require capital support of the financial sector to prevent severe economic harm. We therefore propose the creation of a Systemic Emergency Insurance Fund ("SEIF" or "Fund"), initially set at $1 trillion, but periodically rescaled to the size of the U.S. economy. SEIF should be funded (andpartially pre-funded) by risk-adjusted assessments on all large financial firms -including hedge funds -that benefit from systemic stability. The Department of the Treasury ("Treasury") would administer the Fund, the use of which would be triggered by a "triple key" concurrence among the Treasury, the Federal Deposit Insurance Corporation ("FDIC"), and the Federal Reserve ("Fed"). Unlike taxpayer "bailouts," such a fund would mutualize systemic risk among financial firms through a facility overseen by regulators. Moreover, its funding mechanism would give financial firms a greaterincentive to warn regulatorsof growing systemic risk. And this standby emergency authority would avoid the need for high-stakes legislative action mid-crisis, which can be destabilizing even ifsuccessful and catastrophicif not. -
Request for Information and Comment: Financial Institutions' Use of Artificial Intelligence, Including Machine Learning
Request for Information and Comment: Financial Institutions' Use of Artificial Intelligence, including Machine Learning May 21, 2021 Dr. Peter Quell Dr. Joseph L. Breeden Summary An increasing number of business decisions within the financial industry are made in whole or in part by machine learning applications. Since the application of these approaches in business decisions implies various forms of model risks, the Board of Governors of the Federal Reserve System, the Bureau of Consumer Financial Protection, the Federal Deposit Insurance Corporation, the National the Credit Union Administration, and Office of the Comptroller of the Currency issued an request for information and comment on the use of AI in the financial industry. The Model Risk Managers’ International Association MRMIA welcomes the opportunity to comment on the topics stated in the agencies’ document. Our contact is [email protected] . Request for information and comment on AI 2 TABLE OF CONTENTS Summary ..................................................................................................................... 2 1. Introduction to MRMIA ............................................................................................ 4 2. Explainability ........................................................................................................... 4 3. Risks from Broader or More Intensive Data Processing and Usage ................... 7 4. Overfitting ............................................................................................................... -
What Matters Is Meta-Risks
spotlight RISK MANAGEMENT WHAT MATTERS IS META-RISKS JACK GRAY OF GMO RECKONS WE CAN ALL LEARN FROM OTHER PEOPLE’S MISTAKES WHEN IT COMES TO EMPLOYING EFFECTIVE MANAGEMENT TECHNIQUES FOR THOSE RISKS BEYOND THE SCORE OF EXPLICIT FINANCIAL RISKS. eta-risks are the qualitative implicit risks that pass The perceived complexity of quant tools exposes some to the beyond the scope of explicit financial risks. Most are meta-risk of failing to capitalise on their potential. The US Congress born from complex interactions between the behaviour rejected statistical sampling in the 2000 census as it is “less patterns of individuals and organisational structures. accurate”, even though it lowers the risk of miscounting. In the same MThe archetypal meta-risk is moral hazard where the very act of spirit are those who override the discipline of quant. As Nick hedging encourages reckless behaviour. The IMF has been accused of Leeson’s performance reached stellar heights, his supervisors creating moral hazard by providing countries with a safety net that expanded his trading limits. None had the wisdom to narrow them. tempts authorities to accept inappropriate risks. Similarly, Consider an investment committee that makes a global bet on Greenspan’s quick response to the sharp market downturn in 1998 commodities. The manager responsible for North America, who had probably contributed to the US equity bubble. previously been rolled, makes a stand: “Not in my portfolio.” The We are all exposed to the quintessentially human meta-risk of committee reduces the North American bet, but retains its overall hubris. We all risk acting like “masters of the universe”, believing we size by squeezing more into ‘ego-free’ regions. -
Chapter 5 Risk Adjusted Value
1 CHAPTER 5 RISK ADJUSTED VALUE Risk-averse investors will assign lower values to assets that have more risk associated with them than to otherwise similar assets that are less risky. The most common way of adjusting for risk to compute a value that is risk adjusted. In this chapter, we will consider four ways in which we this risk adjustment can be made. The first two approaches are based upon discounted cash flow valuation, where we value an asset by discounting the expected cash flows on it at a discount rate. The risk adjustment here can take the form of a higher discount rate or as a reduction in expected cash flows for risky assets, with the adjustment based upon some measure of asset risk. The third approach is to do a post-valuation adjustment to the value obtained for an asset, with no consideration given for risk, with the adjustment taking the form of a discount for potential downside risk or a premium for upside risk. In the final approach, we adjust for risk by observing how much the market discounts the value of assets of similar risk. While we will present these approaches as separate and potentially self-standing, we will also argue that analysts often employ combinations of approaches. For instance, it is not uncommon for an analyst to estimate value using a risk-adjusted discount rate and then attach an additional discount for liquidity to that value. In the process, they often double count or miscount risk. Discounted Cash Flow Approaches In discounted cash flow valuation, the value of any asset can be written as the present value of the expected cash flows on that asset. -
Model Risk Management: Quantitative and Qualitative Aspects
Model Risk Management Quantitative and qualitative aspects Financial Institutions www.managementsolutions.com Design and Layout Marketing and Communication Department Management Solutions - Spain Photographs Photographic archive of Management Solutions Fotolia © Management Solutions 2014 All rights reserved. Cannot be reproduced, distributed, publicly disclosed, converted, totally or partially, freely or with a charge, in any way or procedure, without the express written authorisation of Management Solutions. The information contained in this publication is merely to be used as a guideline. Management Solutions shall not be held responsible for the use which could be made of this information by third parties. Nobody is entitled to use this material except by express authorisation of Management Solutions. Content Introduction 4 Executive summary 8 Model risk definition and regulations 12 Elements of an objective MRM framework 18 Model risk quantification 26 Bibliography 36 Glossary 37 4 Model Risk Management - Quantitative and qualitative aspects MANAGEMENT SOLUTIONS I n t r o d u c t i o n In recent years there has been a trend in financial institutions Also, customer onboarding, engagement and marketing towards greater use of models in decision making, driven in campaign models have become more prevalent. These models part by regulation but manifest in all areas of management. are used to automatically establish customer loyalty and engagement actions both in the first stage of the relationship In this regard, a high proportion of bank decisions are with the institution and at any time in the customer life cycle. automated through decision models (whether statistical Actions include the cross-selling of products and services that algorithms or sets of rules) 1. -
Best's Enterprise Risk Model: a Value-At-Risk Approach
Best's Enterprise Risk Model: A Value-at-Risk Approach By Seabury Insurance Capital April, 2001 Tim Freestone, Seabury Insurance Capital, 540 Madison Ave, 17th Floor, New York, NY 10022 (212) 284-1141 William Lui, Seabury Insurance Capital, 540 Madison Ave, 17th Floor, New York, NY 10022 (212) 284-1142 1 SEABURY CAPITAL Table of Contents 1. A.M. Best’s Enterprise Risk Model 2. A.M. Best’s Enterprise Risk Model Example 3. Applications of Best’s Enterprise Risk Model 4. Shareholder Value Perspectives 5. Appendix 2 SEABURY CAPITAL Best's Enterprise Risk Model Based on Value-at-Risk methodology, A.M. Best and Seabury jointly created A.M. Best’s Enterprise Risk Model (ERM) which should assess insurance companies’ risks more accurately. Our objectives for ERM are: • Consistent with state of art risk management concepts - Value at Risk (VaR). • Simple and transparent methodology. • Risk parameters are based on current market data. • Risk parameters can be easily updated annually. • Minimum burden imposed on insurance companies to produce inputs. • Explicitly models country risk. • Explicitly calculates the covariance between all of a company’s assets and liabilities globally. • Aggregates all of an insurance company’s risks into a composite risk measure for the whole insurance company. 3 SEABURY CAPITAL Best's Enterprise Risk Model will improve the rating process, but it will also generate some additional costs Advantages: ■ Based on historical market data ■ Most data are publicly available ■ Data is easily updated ■ The model can be easily expanded to include more risk factors Disadvantages: ■ Extra information request for the companies ■ Rating analysts need to verify that the companies understand the information specifications ■ Yearly update of the Variance-Covariance matrix 4 SEABURY CAPITAL Definition of Risk ■ Risk is measured in standard deviations. -
Gauging Systemic Risks from Hard-To-Value Assets in Euro Area Banks’ Balance Sheets
Box 7 Gauging systemic risks from hard-to-value assets in euro area banks’ balance sheets Prepared by Michał Adam and Katri Mikkonen Uncertainty associated with hard-to-value securities on bank balance sheets can affect market perceptions of banks, especially during periods of stress. Fair value assets on bank balance sheets are classified into three categories: (i) those which are easy to value and based on quoted market prices (level 1 (L1) assets); (ii) those that are harder to value and only partially derive from quoted market prices (level 2 (L2) assets); and (iii) those that are particularly complex and the valuation of which is based on models instead of observed prices (level 3 (L3) assets). While the accounting standards provide the principles for the allocation of assets to the L2/L3 categories, they also leave some room for interpretation, which can result in different choices across banks. Valuation uncertainties can be problematic in times of stress should they lead investors to mistrust the value of banks’ assets, and in turn trigger liquidity or deleveraging pressures – not least if valuations behave in a correlated manner across banks or are concentrated in systemic banks. Worsening market liquidity conditions that would possibly also lead to reclassifications of assets into the L3 category could further amplify the effect. Against this background, this box first looks at the magnitude and distribution of L2/L3 assets in euro area banks’ balance sheets, and second at their impact on market perceptions of banks through the lens of price-to-book (P/B) ratios during normal and stressed times. -
2018-09 Interest Rate Risk Management
FEDERAL HOUSING FINANCE AGENCY ADVISORY BULLETIN AB 2018-09: INTEREST RATE RISK MANAGEMENT Purpose This advisory bulletin (AB) provides Federal Housing Finance Agency (FHFA) guidance for interest rate risk management at the Federal Home Loan Banks (Banks), Fannie Mae, and Freddie Mac (the Enterprises), collectively known as the regulated entities. This guidance supersedes the Federal Housing Finance Board’s advisory bulletin, Interest Rate Risk Management (AB 2004-05). Interest rate risk management is a key component in the management of market risk. These guidelines describe principles the regulated entities should follow to identify, measure, monitor, and control interest rate risk. The AB is organized as follows: I. Governance A. Responsibilities of the Board B. Responsibilities of Senior Management C. Risk Management Roles and Responsibilities D. Policies and Procedures II. Interest Rate Risk Strategy, Limits, Mitigation, and Internal Controls A. Limits B. Interest Rate Risk Mitigation C. Internal Controls III. Risk Measurement System, Monitoring, and Reporting A. Interest Rate Risk Measurement System B. Scenario Analysis and Stress Testing C. Monitoring and Reporting Background Interest rate risk is the risk that changes in interest rates may adversely affect financial condition and performance. More specifically, interest rate risk is the sensitivity of cash flows, reported AB 2018-09 (September 28, 2018) Page 1 Public earnings, and economic value to changes in interest rates. As interest rates change, expected cash flows to and from a regulated entity change. The regulated entities may be exposed to changes in: the level of interest rates; the slope and curvature of the yield curve; the volatilities of interest rates; and the spread relationships between assets, liabilities, and derivatives. -
Risk Management (Online Only)
Risk management Risk management (Online Only) 18 Swiss Re | Financial Report 2020 Risk management Swiss Re | Financial Report 2020 19 Risk management Internal control system and risk model regulatory requirements which Swiss Re is subject to, including compliance, legal and tax risks Internal control system • Effectiveness and efficiency of The internal control system is overseen by operations − addressing basic business the Board of Directors of Swiss Re Ltd and objectives, including performance and the Group Executive Committee. It aims to profitability goals, and the safeguarding provide reasonable oversight and assurance of assets covering significant market, in achieving three objectives: credit, liquidity, insurance, technology • Reliability of reporting − addressing the and other risks preparation of reliable reporting arrangements as well as related data Operationally, the internal control system is covering significant financial, economic, based on Swiss Re’s three lines of control regulatory and other reporting risks and comprises five components: • Compliance with applicable laws and regulations − addressing legal and RISK ASSESSMENT CONTROL INFORMATION & MONITORING ACTIVITIES ACTIVITIES COMMUNICATION ACTIVITIES Processes to identify Risk mitigation activities Capturing and sharing Ongoing evaluation of control and assess risks established in policies and information for risk control effectiveness procedures and management decisions • Performed by risk takers • Performed by risk takers (1st • Performed by all lines of • Risk -