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I)GARP 2020 EXAM PART II Risk Management and Investment Management Pearson Copyright 2019,2018,2017,2016,2015,2014,2013,2012 by Pearson Education,Inc.All rights reserved.Pearson Custom Edition.This copyright covers material written expressly for this volume by the editor/s as well as the compilation itself.It does not cover the individual selections herein that first appeared elsewhere.Permission to reprint these has been obtained by Pearson Education,Inc.for this edition only.Further reproduction by any means,electronic or mechanical,including photocopying and recording,or by any information storage or retrieval system,must be arranged with the individual copyright holders noted.Grateful acknowledgment is made to the following sources for permission to reprint material copyrighted or controlled by them:Factor Theory,Factors,Alpha(and the Low-Risk Anomaly)by Andrew Ang,reprinted from Asset Management:A Systematic Approach to Factor Investing(2014),by permission of Oxford University Press.Portfolio Construction,by Richard Gringold and Ronald Kahn,reprinted from Active Porfolio Management:A Quantitative Approach for Producing Superior Returns and Controlling Risk,2nd edition,(2000),by permission of McGraw-Hill Companies.Portfolio Risk:Analytical Methods and VaR and Risk Budgeting in Investment Management by Philippe Jorion,reprinted from Value at Risk:The New Benchmark for Managing Financial Risk,3rd edition,(2007),by permission of McGraw-Hill Companies.Risk Monitoring and Performance Management,by Bob Litterman and the Quantitative Resources Group,reprinted from Modern Investment Management:An Equilibrium Approach(2003),by permission of John Wiley&Sons,Inc.Portfolio Performance Evaluation,by Zvi Bodie and Alan J.Marcus,reprinted from Investments,11th edition(2018),by permission of the McGraw-Hill Companies.Hedge Funds,by William Fung and David A.Hsieh,reprinted from Handbook of the Economics of Finance,Volume 28 edited by George M.Constantinides et al(2013),by permission of North-Holland/Elsevier.Performing Due Diligence on Specific Managers and Funds,by Kevin R.Mirabile,reprinted from Hedge Fund Investing:A Practical Approach to Understanding Investor Motivation,Manager Profits,and Fund Performance,Second Edition(2016),by permission of John Wiley&Sons,Inc.Learning Objectives provided by the Global Association of Risk Professionals.All trademarks,service marks,registered trademarks,and registered service marks are the property of their respective owners and are used herein for identification purposes only.Pearson Education,Inc.,330 Hudson Street,New York,New York 10013 A Pearson Education Company Printed in the United States of America ScoutAutomatedPrintCode 000200010272205734 EEB/KW Pearson ISBN 10:0135967074 ISBN 13:9780135967072 Chapter 1 Factor Theory 1 1.1 Chapter Summary 2 1.2 The 2008-2009 Financial Crisis 2 1.3 Factor Theory 1.4 CAPM CAPM Lesson 1:Dont Hold an Individual 2 4 Asset,Hold the Factor 4 CAPM Lesson 2:Each Investor Has His Own Optimal Exposure of Factor Risk 5 CAPM Lesson 3:The Average Investor Holds the Market 5 CAPM Lesson 4:The Factor Risk Premium Has an Economic Story 5 CAPM Lesson 5:Risk Is Factor Exposure 6 CAPM Lesson 6:Assets Paying Off in Bad Times Have Low Risk Premiums 6 1.5 Multifactor Models 7 Pricing Kernels 7 Pricing Kernels versus Discount Rate Models 7 Multifactor Model Lessons 8 1.6 Failures of the CAPM 9 1.7 The Fall of Efficient Market Theory 10 1.8 The 2008-2009 Financial Crisis Redux 11 Chapter 2 Factors 2.1 Chapter Summary 2.2 Value Investing 2.3 Macro Factors Economic Growth Inflation Volatility Other Macro Factors 2.4 Dynamic Factors Fama-French(1993)Model Size Factor 2.5 Value Factor Rational Theories of the Value Premium Behavioral Theories of the Value Premium 13 14 14 14 15 15 17 19 21 21 22 23 23 24 111 Value in Other Asset Classes Momentum 2.6 Value Investing Redux 25 25 27 Chapter 3 Alpha(and the-LowRisk Anomaly)29 3.1 Chapter Summary 3.2 GM Asset Management and Martingale 3.3 Active Management Definition of Alpha Benchmarks Matter Creating Alpha 3.4 Factor Benchmarks Factor Regressions Doing without Risk-Free Assets Time-Varying Factor Exposures Non-Linear Payoffs Does Alpha Even Exist?3.5 Low Risk Anomaly History Volatility Anomaly Beta Anomaly Risk Anomaly Factors Explanations 3.6 GM Asset Management and Martingale Redux Chapter 4 Portfolio Construction 4.1 Introduction 4.2 Alphas and Portfolio Construction IV Contents 30 30 30 31 31 32 34 34 37 39 43 44 44 44 45 46 47 49 51 53 54 54 4.3 Alpha Analysis Scale the Alphas Trim Alpha Outliers Neutralization Benchmark-and Cash-Neutral Alphas Risk-Factor-Neutral Alphas 4.4 Transactions Costs 4.5 Practical Details 56 56 56 56 57 57 57 58 4.6 Portfolio Revisions 59 4.7 Techniques for Portfolio Construction 60 Screens 61 Stratification 61 Linear Programming 61 Quadratic Programming 62 4.8 Tests of Portfolio Construction Methods 62 4.9 Alternatives to Mean/Variance Optimization 63 4.10 Dispersion 64 Example 65 Characterizing Dispersion 65 Managing Dispersion 65 Chapter 5 Portfolio Risk:Analytical Methods 69 5.1 Portfolio VaR 70 5.2 VaR Tools 73 Marginal VaR 73 Incremental VaR 73 Component VaR 75 5.3 Summary 76 5.4 Examples 77 Summary67A Global Portfolio Equity Report Barings:An Example in Risks 5.5 VaR Tools for General Distributions 5.6 From VaR to Portfolio 77 77 79 Management 80 From Risk Measurement to Risk Management 80 From Risk Management to Portfolio Management 80 Conclusions 82 Chapter 6 VaR and Risk Budgeting in Investment Management 83 6.1 VaR Applications to Investment Management 84 Sell Side versus Buy Side 84 Investment Process 85 Hedge Funds 85 6.2 What Are the Risks?86 Absolute and Relative Risks 86 Policy Mix and Active Management Risk 86 Funding Risk 87 Sponsor Risk 88 6.3 Using VaR to Monitor and Control Risks 88 Using VaR to Check Compliance 88 Using VaR to Monitor Risk 89 The Role of the Global Custodian 90 The Role of the Money Manager 90 6.4 Using VaR to Manage Risks 90 Using VaR to Design Guidelines 90 Using VaR for the Investment Process 91 6.5 Risk Budgeting 92 Budgeting across Asset Classes Budgeting across Active Managers Conclusions Chapter 7 Risk Monitoring 92 93 94 and Performance Measurement 95 7.1 Overview 96 7.2 The Three Legs of Financial Accounting Control:Planning,Budgeting,and Variance Monitoring 97 7.3 Building the Three-Legged Risk Management Stool:The Risk Plan,the Risk Budget,and the Risk Monitoring Process 98 The Risk Plan 98 The Risk Budget 99 Risk Monitoring 101 7.4 Risk Monitoring Rationale and Activities 101 Objectives of an Independent Risk Management Unit 102 Examples of the Risk Management Unit in Action 103 Quantifying llliquidity Concerns 106 Credit Risk Monitoring 107 7.5 Performance Measurement-Tools and Theory 107 Reasons That Support Using Multiple Performance Measurement Tools 107 How to Improve the Meaningfulness of Performance Measurement Tools 107 Tool#1-The Green Zone 108 Tool#2-Attribution of Returns 110 Tool#3-The Sharpe and Information Ratios 110 Tool#4 Alpha versus the Benchmark 111 Contents v 1.1 CHAPTER SUMMARY Assets earn risk premiums because they are exposed to underlying factor risks.The capital asset pricing model(CAPM),the first theory of factor risk,states that assets that crash when the market loses money are risky and therefore must reward their holders with high risk premiums.While the CAPM defines bad times as times of low market returns,multifactor models capture multiple definitions of bad times across many factors and states of nature.1.2 THE 2008-2009 FINANCIAL CRISIS During the financial crisis of 2008 and 2009,the price of most risky assets plunged.Table 1.1 shows that U.S.large cap equities returned-37%;international and emerging markets equities had even larger losses.The riskier fixed income securities,like corporate bonds,emerging market bonds,and high yield bonds,also fell,tumbling along with real estate.Alternative investments like hedge funds,which trumpeted their immunity to market disruptions,were no safe refuge:equity hedge funds and their fixed income counterparts fell approximately 20%.Commodities had losses exceeding 30%.The only assets to go up during 2008 were cash(U.S.Treasury bills)and safe-haven sovereign bonds,especially long-term U.S.Treasuries.I Table 1.11 Returns of Asset Classes in 2008 Cash Three-month T-bill Core Bonds Barcap Aggregate Index Why did so many asset classes crash all at once?And given that they did,was the concept of diversification dead?In this chapter,we develop a theory of factor risk premiums.The factor risks constitute different flavors of bad times and the investors who bear these factor risks need to be compensated in equilibrium by earning factor risk premiums.Assets have risk premiums not because the assets themselves earn risk premiurns;assets are bundles of factor risks,and it is the exposures to the underlying factor risks that earn risk premiums.These factor risks manifest during bad times such as the financial crisis in late 2008 and early 2009.1.3 FACTOR THEORY Factors are to assets what nutrients are to food.Table 1.2 is from the Food and Nutrition Board,which is part of the Institute of Medicine of the National Academies,and lists recommended intakes of the five macronutrientswater,carbohydrates,pro-tein,fiber,and fatfor an average male,female,and child.Carbohydrates can be obtained from food made from cereals and grains.Protein is obtained from meat and dairy products.Fiber is available from wheat and rice.Fat we can consume from animals but also certain plant foods such as peanuts.Each type of food is a bundle of nutrients.Many foods contain more than just one macronutrient:for example,rice contains both 1.3%5.2%Global Bonds Citigroup World Government 10.9%TIPS Citigroup US Inflation Linked Emerging Market Bonds JPM Emerging Markets Bond Index US High Yield Merrill Lynch High Yield Master Large Cap Equity S&P 500Small Cap Equity Russell 2000International Equity MSCI World ex US Emerging Markets Equity IFC Emerging Markets Public Real Estate NAREIT Equity REITS Private Real Estate NCREIF Property Index Private Capital Venture Economics(Venture and Buyouts)Equity Hedge Funds HFRI Equity Hedge Index Fixed Income Hedge Funds HFRI Fixed Income Index Commodities Dow Jones AIG Commodity Index 2 Financial Risk Manager Exam Part II:Risk Management and Investment Management-1.2%-9.7%-26.3%-37.0%-33.8%-43.2%-53.2%-37.7%-16.9%-20.0%-20.6%-17.8%-35.7%Haugen and Heins(197 5)use data from 1926 to 1971 and also investigate the relation between beta and volatility risk measures and returns.They report(my italics):The results of our empirical effort do not support the conventional hypothesis that risksystematic or otherwisegenerates a special reward.Indeed,our results indicate that,over the long run,stock portfolios with lesser variance in monthly returns have experienced greater average returns than riskier counterparts.Must of these results were forgotten.But these old results recently have come roaring back.Volatility Anomaly I was fortunate to write one paper that helped launch the new risk anomaly literature in 2006 with Robert Hodrick,one of my colleagues at Columbia Business School,and two of our former students,Yuhang Xing and Xiaoyan Zhang,who are now professors at Rice University and Purdue University,respectively.We found that the returns of high-volatility stocks were abysmally low.So low that they had zero average returns.This paper now generates the most cites per year of all my papers and has spawned a follow-up literature attempting to replicate,explain,and refute the results.24First,should there even be a relation between volatility and returns?The whole point of the CAPM and the many multifactor extensions(see Chapter 2)was that stock return volatility itself should not matter.Expected returns,according to these models,are determined by how assets covary with factor risks.Idiosyncratic volatility,or tracking error(see Equation(3.3),should definitely not have any relation to expected returns under the CAPM.But in markets that are segmented due to clientele effectswhere some agents cannot diversify or where some agents prefer to hold some assets over others for exogenous reasonsidiosyncratic volatility should be positively related to returns.Intuitively,agents have to be paid for bearing idiosyncratic,risk,resulting in a positive relation between idiosyncratic risk and volatility in equilibrium.In later models with II noise traders,11 who trade for random reasons 24 Volatility makes many appearances,of course,in tests of cross-sec-tional asset pricing models before Ang et.al.(2006),but most of them are negative results or show a slight positive relation.For example,in Fama and MacBeths(1973)seminal test of the CAPM,volatility is included and carries an insignificant coefficient.Eric Falkenstein(2012)recounts that he uncovered a negative relation between volatility and stock returns in his PhD dissertation in 1994,which was never published.unrelated to fundamental valuation,higher volatilities are associated with higher risk premiums.25The Ang et.al.(2006)results show exactly the opposite.Particularly notable is the robustness of the negative relation between both idiosyncratic and total volatility with returns.We employed a large number of controls for size,value,leverage,liquidity risk,volume,turnover,bid-ask spreads,co-skewness risk,dispersion in analystsforecasts,and momentum.We also did not find that aggregate volatility risk explained our resulteven though volatility risk is a pervasive risk factor(see Chapter 2).In subsequent work,Ang et.al.(2009),we showed that the volatility effect existed in each G7 country and across all developed stock markets.We also controlled for private information,transactions costs,analyst coverage,institutional ownership,and delay measures,which recorded how fast information is impounded into stock prices.Skewness did not explain the puzzle.Lagged Volatility and Future Returns To see the volatility anomaly,I take U.S.stocks,rebalance quarterly from September 1963 to December 2011,and form quintile portfolios.I construct monthly frequency returns.I sort on idiosyncratic volatility using the Fama-French(1993)factors with daily data over the past quarter.(Ranking on total volatility produces very similar results.)I market weight within each quintile similar to Ang et.al.(2006,2009).In Figure 3.8,I report the mean and standard deviations of the quintile portfolios on the left-hand axis in the two bars.The volatilities increase going from the low-to high-volatility quintiles,by construction.The average returns are above 10%for the first three quintiles,fall to 6.8%for quintile 4,and then plummet to 0.1%for the highest volatility stocks.High volatility stocks certainly do have abysmally low returns.The right-hand axis reports raw Sharpe ratios,which are the ratios of the means to the standard deviations.These monotonically decline from 0.8 to 0.0 going