Using Duration Times Spread to Forecast Credit Risk


 Raymond Byron Carr
 2 years ago
 Views:
Transcription
1 Using Duration Times Spread to Forecast Credit Risk European Bond Commission / VBA Patrick Houweling, PhD Head of Quantitative Credits Research Robeco Asset Management Quantitative Strategies Forecasting Credit Risk 1
2 Contents Capturing Changing Volatility Building the Risk Model Testing the Risk Model Using the Risk Model Conclusions Forecasting Credit Risk 2
3 How to Capture Changing Volatility? 0.8% rolling 36m excess return volatilities US IG corporate bonds 0.6% volatility (%) 0.4% 0.2% 0.0% IG Volatility of excess returns is not constant Historical volatility is not suited to predict future volatility Using historical volatility to measure current risk means lagging the market Forecasting Credit Risk 3
4 Can Ratings Capture TimeVarying Volatility? 1.2% rolling 36m excess return volatilities US IG corporate bonds 1.0% volatility (%) 0.8% 0.6% 0.4% 0.2% 0.0% AAA AA A BBB Volatility of rating classes is not constant Using only ratings to measure current risk means lagging the market Forecasting Credit Risk 4
5 Can Spreads Capture TimeVarying Volatility? Volatility per rating class: Spread per rating class: 1.2% % % volatility (%) 0.6% spread (bps) % % 0.0% AAA AA A BBB AAA AA A BBB When spreads are high, credit markets are more volatile and vice versa So: volatility highly correlates with spread Forecasting Credit Risk 5
6 Why and How Should We Use Spreads? Excess returns ER over Treasuries consists of carry return (spread) spread change return (spreadduration D times spread change s): ER D s absolute spread change which is equivalent to ER Ds s s relative spread change Excess return volatility σ ER can thus be approximated by either absolute σ ER = Dσ spread absolute spread change volatility or relative σ ER = Dsσ spread relative spread change volatility Forecasting Credit Risk 6
7 Which Spread Volatility? 25 rolling 36m absolute spread change volatilities 12% rolling 36m relative spread change volatilities 20 10% 8% volatility (bps) volatility (%) 6% 4% 5 2% AAA AA A BBB 0% AAA AA A BBB Relative spread change volatility is much more constant than absolute spread change volatility Also: differences between ratings are much smaller Forecasting Credit Risk 7
8 Measure Excess Return Volatility per Unit DTS! Best way to measure excess return volatility is using relative spread change volatility relative σ ER = Dsσ spread duration times spread (DTS) Relative spread change volatility can be estimated accurately using historical data Duration Times Spread (DTS) can change on a daily basis, reflecting current market conditions Their product gives an estimate of current excess return volatility Alternative interpretation: Relative spread change volatility can thus be interpreted as the excess return volatility per unit of DTS It can be estimated as the volatility of excess returns divided by DTS Forecasting Credit Risk 8
9 From Market Level to Bond Level Each month assign bonds to duration times spread (DTS) quintiles and subdivide each quintile in 6 spread buckets For each bucket, calculate the average monthly return and its time series volatility and the average monthly DTS and its time series average 1.75% Duration Times Spread vs. excess return volatility volatility (%/month) 1.50% 1.25% 1.00% 0.75% 0.50% 0.25% 0.00% duration (y) x spread (bps) Spread Bucket 1  Low Spread Bucket 2 Spread Bucket 3 Spread Bucket 4 Spread Bucket 5 Spread Bucket 6  High Forecasting Credit Risk 9
10 Idiosyncratic Spread Change Volatility is Also Related to Spread Level Each month bonds are assigned to 3 sector (Financials, Industrials, Utilities) x 3 duration buckets x 6 spread buckets We calculate the idiosyncratic spread change as the spread change minus the average spread change of the bucket We estimate the standard deviation of idiosyncratic changes and the average spread per bucket 70 Volatility of idiosyncratic spread changes (bps/month) Spread (bps) Forecasting Credit Risk 10
11 Contents Capturing Changing Volatility Building the Risk Model Estimating Factor Returns Significance Tests Estimating Covariance Matrix of Factor Returns Measuring Risk Testing the Risk Model Using the Risk Model Conclusions Forecasting Credit Risk 11
12 Forecasting Credit Risk 12 Estimating Factor Returns We model a corporate bond s excess returns as S indicates whether bond i belongs to sector j in month t N is the number of sectors α s and β s are the factor returns Systematic returns are linear in DTS per sector Specific return volatility is proportional in DTS ( ) 1,, 2,,, 1 1,,, 1, 1 1,,,, 0, ~ = = = + + = t i t t i t i t i t i N j t j i t j t i N j t j i t j t i DTS N S DTS S ER γ σ σ ε ε β α
13 Significance tests Data: monthly, from June 1993 to January 2006 Universes: Lehman Brothers US IG and HY index constituents Estimation method: Generalized Least Squares regression Test 1: sector coefficients are jointly different from zero Test 2: sector coefficients differ from each other Percentage of months in which Wald tests indicate significance at 95% confidence level (USIG results:) Test 1 Test 2 α s β s α s β s β s only  78%  41% α s and β s 97% 90% 93% 80% ER N N i, t = α j, tsi, j, t 1 + DTSi, t 1 β j, tsi, j, t 1 + εi, t j = 1 j = 1 Forecasting Credit Risk 13
14 Estimating Covariance Matrix of Factor Returns We robustly estimate volatilities and correlations of factor returns to calculate the covariance matrix of our risk factors We shrink the covariance matrix by assuming: Equal volatility for all sector intercepts (α s) Equal volatility for all sector DTS slopes (β s) Equal correlation for all pairs of (un)loaded sectors sector intercepts sector slopes sector intercepts σ 2 cov sector slopes cov σ 2 Forecasting Credit Risk 14
15 Calculating Tracking Error and Beta Model Risk factors have covariance matrix Σ Portfolio has exposures P to the risk factors Benchmark has exposures B to the risk factors Bet = P B Systematic Tracking Error (TE) TE is defined as the volatility of the bet s returns TE 2 = variance (P B) = (P B) Σ(P B) CAPM beta Beta is defined as the covariance of the portfolio with the market (benchmark) divided by the variance of the market beta = covariance (P,B) = P ΣB variance (B) B ΣB Forecasting Credit Risk 15
16 Contents Capturing Changing Volatility Building the Risk Model Testing the Risk Model Using the Risk Model Conclusions Forecasting Credit Risk 16
17 Simulation Setup We test the risk model in a Monte Carlo simulation Data: June 1993 January 2006 Universe: Lehman Brothers US Investment Grade index Portfolios: random portfolios of 80 bonds each month Covariance matrix is estimated on 60month rolling window For each portfolio compare exante Tracking Error to expost 1month outperformance Criteria Level of risk Exceedings of tracking error multiples Discrimination of more risky and less risky portfolios Forecasting Credit Risk 17
18 ExAnte Tracking Errors Vary with Market Spread Exante tracking errors and market spread (IG) 90% TE bounds median TE market spread tracking error (%) spread (bps) Forecasting Credit Risk 18
19 ExAnte Tracking Errors Correspond Well to ExPost Returns Ratio of expost return to exante tracking error should be standard normally distributed standard deviation should be 1 < 1 means risk is overestimated, > 1 means underestimation Standard deviation of ratio is 1.04 on average, but overestimations and underestimation occur frequently % percentile standard deviation 95% percentile Forecasting Credit Risk 19
20 Risk Model Distinguishes High and Low Risk Portfolios Each month create buckets of 20% exante least risky portfolios and of 20% mostrisky portfolios Calculate expost standard deviation of both buckets Least risky bucket indeed has lowest standard deviation in 92% of months least risky quintile most risky quintile 0.5 expost volatility (%) Forecasting Credit Risk 20
21 Contents Capturing Changing Volatility Building the Risk Model Testing the Risk Model Using the Risk Model Conclusions Forecasting Credit Risk 21
22 Risk Attribution We measure the risk of Market Sectors Issuers Issues We report Total risk Risk contributions per bet Beta of the portfolio Forecasting Credit Risk 22
23 Tracking Error Report 1. Systematic risk 2. Specific risk 3. Beta Credit Risk Report Tracking error Systematic risk 1,14% Market 0,94% weight 0,15% weight x spread x duration 1,00% Sector 0,53% weight 0,14% weight x spread x duration 0,57% Specific risk 0,37% Issuer 0,20% Issue 0,31% Total 1,20% CAPMbeta 1, Forecasting Credit Risk 23
24 Attribution of Tracking Error to Sectors Sector Risk Report weight x spread x duration tracking error portfolio benchmark bet systematic specific total 1 Banking ,40% 0,30% 0,50% 2 Brokerage ,06% 0,03% 0,07% 3 Finance companies ,04% 0,06% 0,08% 4 Insurance ,07% 0,09% 0,12% 5 REITS ,01% 0,01% 0,01% 6 Financial other ,00% 0,01% 0,01% 7 Basic industry ,05% 0,03% 0,06% 8 Capital goods ,04% 0,03% 0,05% 9 Consumer cyclical ,07% 0,02% 0,07% 10 Consumer noncyclical ,05% 0,06% 0,08% 11 Energy ,00% 0,02% 0,02% 12 Technology ,02% 0,03% 0,04% 13 Transportation ,04% 0,02% 0,05% 14 Communications ,02% 0,07% 0,07% 15 Other industrial ,01% 0,01% 0,01% 16 Utilities ,06% 0,02% 0,06% 17 Supranat./Sovereigns/Agencies ,02% 0,01% 0,02% 18 ABS/Mortgages ,28% 0,12% 0,30% 99 Noncorporate ,13% 0,08% 0,15% Total TE contribution 1,00% 0,53% 0,37% 0,65% Forecasting Credit Risk 24
25 Attribution of Tracking Error to Issuers 1. Largest overweight, not highest risk 2. Not investing in an issue is a risk as well 3. Short position (with CDS) to exploit our strong view Issuer Risk Report weight weight x spread x duration tracking error Sector Subsector port BM bet port BM bet issuer TE issue TE total TE Banking Lower Tier II 1,11% 0,04% 1,07% ,082% 0,121% 0,146% Banking Tier 1 2,46% 0,76% 1,69% ,036% 0,075% 0,083% Banking Lower Tier II 1,97% 0,71% 1,26% ,051% 0,061% 0,080% Banking Upper Tier II 0,90% 0,23% 0,67% ,035% 0,063% 0,072% Banking Banking 3,07% 1,36% 1,71% ,038% 0,057% 0,069% ABS/Mortgages ABS 1,06% 0,00% 1,06% ,038% 0,058% 0,069% Banking Banking 2,58% 0,84% 1,74% ,044% 0,051% 0,067% Banking Lower Tier II 1,03% 0,24% 0,79% ,036% 0,050% 0,062% Banking Tier 1 0,78% 0,09% 0,70% ,032% 0,052% 0,061% ABS/Mortgages ABS 0,79% 0,00% 0,79% ,035% 0,049% 0,060% Banking Senior 1,02% 0,25% 0,76% ,037% 0,046% 0,059% Insurance Life 2,09% 0,34% 1,75% ,035% 0,046% 0,058% Banking Lower Tier II 2,53% 1,28% 1,26% ,032% 0,048% 0,058% Finance companies Noncaptive 0,17% 0,23% 0,40% ,009% 0,056% 0,057% Banking Banking 2,59% 0,86% 1,73% ,035% 0,042% 0,055% Banking Senior 0,98% 0,25% 0,72% ,024% 0,041% 0,048% Banking Banking 0,93% 1,04% 0,11% ,008% 0,046% 0,047% Banking Tier 1 1,34% 0,05% 1,29% ,024% 0,039% 0,046% Banking Upper Tier II 1,79% 0,07% 1,72% ,025% 0,039% 0,046% Banking Banking 3,46% 1,71% 1,76% ,030% 0,034% 0,045% Brokerage Brokerage 0,00% 1,05% 1,05% ,016% 0,009% 0,018% Forecasting Credit Risk
26 Contents Capturing changing volatility Building the risk model Testing the risk model Using the risk model Conclusions Forecasting Credit Risk 26
27 Conclusions Use duration times spread to capture changing volatility of Market Sectors Issuers Issues Don t use ratings! Risk model adequately captures timevarying volatility and distinguishes high and low risk portfolios Attribution to risk factors enhances insight in portfolio positioning Forecasting Credit Risk 27
28 Disclaimer All copyrights patents and other property in the information contained in this document is held by Robeco Institutional Asset Management and shall continue to belong to Robeco Institutional Asset Management. No rights whatsoever are licensed or assigned or shall otherwise pass to persons accessing this information. This document has been carefully prepared and is presented by Robeco Institutional Asset Management. It is intended to supply the reader with information and reference on Robeco Institutional Asset Management's specific capabilities and it is to be used as the basis for an investment decision with respect to this capability. The content of this document is based upon sources of information believed to be reliable, but no warranty or declaration, either explicit or implicit, is given as to their accuracy or completeness. This document is not intended for distribution to or use by any person or entity in any jurisdiction or country where such distribution or use would be contrary to local law or regulation. The information relating to the performance is for historical information only. Past performance is not indicative for future results Forecasting Credit Risk 28
Managing High Yield public small caps with Robeco s corporate bond selection model COALA For professional investors only
ROCK note December 2010 Managing High Yield public small caps with Robeco s corporate bond selection model COALA For professional investors only By Sander Bus, CFA, portfolio manager Daniël Haesen, CFA,
More informationSSgA CAPITAL INSIGHTS
SSgA CAPITAL INSIGHTS viewpoints Part of State Street s Vision thought leadership series A Stratified Sampling Approach to Generating Fixed Income Beta PHOTO by Mathias Marta Senior Investment Manager,
More informationCHAPTER 7: OPTIMAL RISKY PORTFOLIOS
CHAPTER 7: OPTIMAL RIKY PORTFOLIO PROLEM ET 1. (a) and (e).. (a) and (c). After real estate is added to the portfolio, there are four asset classes in the portfolio: stocks, bonds, cash and real estate.
More informationRegression Analysis. Pekka Tolonen
Regression Analysis Pekka Tolonen Outline of Topics Simple linear regression: the form and estimation Hypothesis testing and statistical significance Empirical application: the capital asset pricing model
More informationCFA Examination PORTFOLIO MANAGEMENT Page 1 of 6
PORTFOLIO MANAGEMENT A. INTRODUCTION RETURN AS A RANDOM VARIABLE E(R) = the return around which the probability distribution is centered: the expected value or mean of the probability distribution of possible
More informationApplied Fixed Income Risk Modeling
Applied Fixed Income Risk Modeling Successes and Learning Experiences Navin Sharma VP, Director of Fixed Income Risk Management and Analytics OppenheimerFunds, Inc. Northfield s 18 th Annual Research Conference
More informationPortfolio Performance Measures
Portfolio Performance Measures Objective: Evaluation of active portfolio management. A performance measure is useful, for example, in ranking the performance of mutual funds. Active portfolio managers
More informationCHAPTER 10 RISK AND RETURN: THE CAPITAL ASSET PRICING MODEL (CAPM)
CHAPTER 10 RISK AND RETURN: THE CAPITAL ASSET PRICING MODEL (CAPM) Answers to Concepts Review and Critical Thinking Questions 1. Some of the risk in holding any asset is unique to the asset in question.
More informationLiquidity of Corporate Bonds
Liquidity of Corporate Bonds Jack Bao, Jun Pan and Jiang Wang MIT October 21, 2008 The QGroup Autumn Meeting Liquidity and Corporate Bonds In comparison, low levels of trading in corporate bond market
More informationChapter 5. Conditional CAPM. 5.1 Conditional CAPM: Theory. 5.1.1 Risk According to the CAPM. The CAPM is not a perfect model of expected returns.
Chapter 5 Conditional CAPM 5.1 Conditional CAPM: Theory 5.1.1 Risk According to the CAPM The CAPM is not a perfect model of expected returns. In the 40+ years of its history, many systematic deviations
More informationDiversification and the RiskReturn TradeOff
Diversification and the RiskReturn TradeOff Dr. Patrick Toche References : Zvi Bodie, Alex Kane, Alan Marcus, Investments, McGrawHill/Irwin. The relevant chapters of this textbook will be followed closely
More informationLowvolatility investing: a longterm perspective
ROCK note January 2012 Lowvolatility investing: a longterm perspective For professional investors only Pim van Vliet Senior Portfolio Manager, LowVolatility Equities Introduction Over the longrun,
More informationChapter 10. Chapter 10 Topics. Recent Rates
Chapter 10 Introduction to Risk, Return, and the Opportunity Cost of Capital Chapter 10 Topics Rates of Return Risk Premiums Expected Return Portfolio Return and Risk Risk Diversification Unique & Market
More informationMultiple Choice: 2 points each
MID TERM MSF 503 Modeling 1 Name: Answers go here! NEATNESS COUNTS!!! Multiple Choice: 2 points each 1. In Excel, the VLOOKUP function does what? Searches the first row of a range of cells, and then returns
More informationKEY ELEMENTS TO DESIGN AN EXTERNAL ACTIVE MANAGEMENT PROGRAM. Alejandro C. Reveiz H. Director, Quantitative Solutions, SAA & Analytics (QSA)
KEY ELEMENTS TO DESIGN AN EXTERNAL ACTIVE MANAGEMENT PROGRAM Alejandro C. Reveiz H. Director, Quantitative Solutions, SAA & Analytics (QSA) October 1, 2015 Table of Contents Design guidelines in such a
More informationHow quantitative easing impacts government bond markets and the duration model
WHITE PAPER January 2014 For professional investors How quantitative easing impacts government bond markets and the duration model Johan Duyvesteyn Martin Martens Olaf Penninga How quantitative easing
More informationI.e., the return per dollar from investing in the shares from time 0 to time 1,
XVII. SECURITY PRICING AND SECURITY ANALYSIS IN AN EFFICIENT MARKET Consider the following somewhat simplified description of a typical analystinvestor's actions in making an investment decision. First,
More informationB.3. Robustness: alternative betas estimation
Appendix B. Additional empirical results and robustness tests This Appendix contains additional empirical results and robustness tests. B.1. Sharpe ratios of betasorted portfolios Fig. B1 plots the Sharpe
More informationS&P 500 Low Volatility Index
S&P 500 Low Volatility Index Craig J. Lazzara, CFA S&P Indices December 2011 For Financial Professional/Not for Public Distribution There s nothing passive about how you invest. PROPRIETARY. Permission
More informationPricing and Strategy for Muni BMA Swaps
J.P. Morgan Management Municipal Strategy Note BMA Basis Swaps: Can be used to trade the relative value of Libor against short maturity tax exempt bonds. Imply future tax rates and can be used to take
More informationMultiFactor Risk Attribution Concept and Uses. CREDIT SUISSE AG, Mary Cait McCarthy, CFA, FRM August 29, 2012
MultiFactor Risk Attribution Concept and Uses Introduction Why do we need risk attribution? What are we trying to achieve with it? What is the difference between expost and exante risk? What is the
More informationJava Modules for Time Series Analysis
Java Modules for Time Series Analysis Agenda Clustering Nonnormal distributions Multifactor modeling Implied ratings Time series prediction 1. Clustering + Cluster 1 Synthetic Clustering + Time series
More informationGOVERNMENT PENSION FUND GLOBAL HISTORICAL PERFORMANCE AND RISK REVIEW
GOVERNMENT PENSION FUND GLOBAL HISTORICAL PERFORMANCE AND RISK REVIEW 10 March 2014 Content Scope... 3 Executive summary... 3 1 Return and risk measures... 4 1.1 The GPFG and asset class returns... 4 1.2
More informationRisk management Making the difference in fixed income investing
Risk management Making the difference in fixed income investing Pictet Asset Management September 2014 For professional investors only Overview Tough times lie ahead for fixed income investors. The ballooning
More informationReducing Active Return Variance by Increasing Betting Frequency
Reducing Active Return Variance by Increasing Betting Frequency Newfound Research LLC February 2014 For more information about Newfound Research call us at +16175319773, visit us at www.thinknewfound.com
More informationUpdate on Mutual Company Dividend Interest Rates for 2013
Update on Mutual Company Dividend Interest Rates for 2013 1100 Kenilworth Ave., Suite 110 Charlotte, NC 28204 704.333.0508 704.333.0510 Fax www.bejs.com Prepared and Researched by June 2013 Near the end
More informationChapter 7 Risk, Return, and the Capital Asset Pricing Model
Chapter 7 Risk, Return, and the Capital Asset Pricing Model MULTIPLE CHOICE 1. Suppose Sarah can borrow and lend at the risk freerate of 3%. Which of the following four risky portfolios should she hold
More informationThe Capital Asset Pricing Model (CAPM)
The Capital Asset Pricing Model (CAPM) Tee Kilenthong UTCC c Kilenthong 2016 Tee Kilenthong UTCC The Capital Asset Pricing Model (CAPM) 1 / 36 Main Issues What is an equilibrium implication if all investors
More informationPITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU
PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard
More informationApplication of a Linear Regression Model to the Proactive Investment Strategy of a Pension Fund
Application of a Linear Regression Model to the Proactive Investment Strategy of a Pension Fund Kenneth G. Buffin PhD, FSA, FIA, FCA, FSS The consulting actuary is typically concerned with pension plan
More informationQuantitative Methods for Finance
Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain
More informationFixed Income Performance Attribution
Fixed Income Performance Attribution Mary Cait McCarthy August 2014 Content 1 2 3 4 5 6 What is Performance Attribution? Uses of Performance Attribution Drivers of Return in Fixed Income Returns Based
More informationIndividual contributions to portfolio risk: risk decomposition for the BETFI index
Individual contributions to portfolio risk: risk decomposition for the BETFI index Marius ACATRINEI Institute of Economic Forecasting Abstract The paper applies Euler formula for decomposing the standard
More informationFTS Real Time System Project: Portfolio Diversification Note: this project requires use of Excel s Solver
FTS Real Time System Project: Portfolio Diversification Note: this project requires use of Excel s Solver Question: How do you create a diversified stock portfolio? Advice given by most financial advisors
More informationExample 1: Calculate and compare RiskMetrics TM and Historical Standard Deviation Compare the weights of the volatility parameter using,, and.
3.6 Compare and contrast different parametric and nonparametric approaches for estimating conditional volatility. 3.7 Calculate conditional volatility using parametric and nonparametric approaches. Parametric
More informationETF Specific Data Point Methodologies
ETF Specific Data Point ethodologies orningstar ethodology Paper December 31 2010 2010 orningstar Inc. All rights reserved. The information in this document is the property of orningstar Inc. eproduction
More informationAn introduction to ValueatRisk Learning Curve September 2003
An introduction to ValueatRisk Learning Curve September 2003 ValueatRisk The introduction of ValueatRisk (VaR) as an accepted methodology for quantifying market risk is part of the evolution of risk
More informationUnderstanding Fixed Income
Understanding Fixed Income 2014 AMP Capital Investors Limited ABN 59 001 777 591 AFSL 232497 Understanding Fixed Income About fixed income at AMP Capital Our global presence helps us deliver outstanding
More informationECONOMIA DEGLI INTERMEDIARI FINANZIARI AVANZATA II MODULO ASSET MANAGEMENT LECTURE 3
ECONOMIA DEGLI INTERMEDIARI FINANZIARI AVANZATA II MODULO ASSET MANAGEMENT LECTURE 3 1 MANAGEMENT APPROACH OVERVIEW Portfolios need maintenance and periodic revision: Because the needs of the beneficiary
More information09/03/2015. The Changing Landscape of The Global High Yield Market. What makes the High Yield Market So Appealing
9/3/21 For professional use only Not for Public distribution The Changing Landscape of The Global High Yield Market March 21 Texas Association of Public Employee Retirement Systems (TEXPERS) Patrick Maldari,
More informationNavigating Rising Rates with Active, MultiSector Fixed Income Management
Navigating Rising Rates with Active, MultiSector Fixed Income Management With bond yields near 6year lows and expected to rise, U.S. core bond investors are increasingly questioning how to mitigate interest
More informationAre Unconstrained Bond Funds a Substitute for Core Bonds?
TOPICS OF INTEREST Are Unconstrained Bond Funds a Substitute for Core Bonds? By Peter Wilamoski, Ph.D. Director of Economic Research Philip Schmitt, CIMA Senior Research Associate AUGUST 2014 The problem
More informationPortfolio Management for institutional investors
Portfolio Management for institutional investors June, 2010 Bogdan Bilaus, CFA CFA Romania Summary Portfolio management  definitions; The process; Investment Policy Statement IPS; Strategic Asset Allocation
More informationQuantitative Asset Manager Analysis
Quantitative Asset Manager Analysis Performance Measurement Forum Dr. Stephan Skaanes, CFA, CAIA, FRM PPCmetrics AG Financial Consulting, Controlling & Research, Zurich, Switzerland www.ppcmetrics.ch Copenhagen,
More informationRisk Management for Fixed Income Portfolios
Risk Management for Fixed Income Portfolios Strategic Risk Management for Credit Suisse Private Banking & Wealth Management Products (SRM PB & WM) August 2014 1 SRM PB & WM Products Risk Management CRO
More informationPOINT Innovative MultiAsset Portfolio Analysis
Index, Portfolio and Risk Solutions POINT Innovative MultiAsset Portfolio Analysis The Difference Is Clear POINT: Dynamic Decision Support Flexible portfolio and index reporting Draw from our vast database
More informationTHE ELASTICITY OF THE PRICE OF A STOCK AND ITS BETA
THE ELASTICITY OF THE PRICE OF A STOCK AND ITS BETA Cyriac ANTONY MPh, Lecturer (Selection Grade) in Statistics, Sacred Heart College Thevara, Kochi, India Email: cyriacantony2003@yahoo.co.in E.S. JEEVANAND
More informationPeer Reviewed. Abstract
Peer Reviewed William J. Trainor, Jr.(trainor@etsu.edu) is an Associate Professor of Finance, Department of Economics and Finance, College of Business and Technology, East Tennessee State University. Abstract
More informationProfile: FRS Select High Yield Fund (B50)
Profile: FRS Select High Yield Fund (B50) Profile Issued 08/10 Asset Class and Fund Objective: Fund Type: Index or Active Strategy: Management Type: Specialty highyield bond fund Mutual fund Active fund
More informationInvestment Performance, Analytics, and Risk. Glossary of Terms
Investment Performance, Analytics, and Risk Glossary of Terms Investment Performance, Analytics, and Risk Glossary of Terms ABOUT J.P. MORGAN INVESTMENT ANALYTICS & CONSULTING J.P. Morgan Investment Analytics
More informationRisk and return (1) Class 9 Financial Management, 15.414
Risk and return (1) Class 9 Financial Management, 15.414 Today Risk and return Statistics review Introduction to stock price behavior Reading Brealey and Myers, Chapter 7, p. 153 165 Road map Part 1. Valuation
More informationGoldman Sachs ActiveBeta Equity Indexes Methodology
GOLDMAN SACHS ASSET MANAGEMENT Goldman Sachs ActiveBeta Equity Indexes Methodology Last updated 14 August 2015 Table of Contents I. Introduction... 1 A. Index Overview... 1 B. Index Details... 1 II. Index
More informationThis paper is not to be removed from the Examination Halls
~~FN3023 ZA d0 This paper is not to be removed from the Examination Halls UNIVERSITY OF LONDON FN3023 ZA BSc degrees and Diplomas for Graduates in Economics, Management, Finance and the Social Sciences,
More informationThese charts identify short term trends in benchmark relative performance and risk.
The Government Pension Global The Government Pension was established on 1 st January 6. The Government Pension comprises: The Government Pension Global (previously the Government Petroleum, established
More informationCredit Risk Stress Testing
1 Credit Risk Stress Testing Stress Testing Features of Risk Evaluator 1. 1. Introduction Risk Evaluator is a financial tool intended for evaluating market and credit risk of single positions or of large
More informationMadison Investment Advisors LLC
Madison Investment Advisors LLC Intermediate Fixed Income SELECT ROSTER Firm Information: Location: Year Founded: Total Employees: Assets ($mil): Accounts: Key Personnel: Matt Hayner, CFA Vice President
More informationRisk and Return in the Canadian Bond Market
Risk and Return in the Canadian Bond Market Beyond yield and duration. Ronald N. Kahn and Deepak Gulrajani (Reprinted with permission from The Journal of Portfolio Management ) RONALD N. KAHN is Director
More informationInvesting in Corporate Credit Using Quantitative Tools
14 SEPTEMBER 2010 MODELING METHODOLOGY FROM MOODY S KMV Investing in Corporate Credit Using Quantitative Tools Authors Zan Li Jing Zhang Editor Christopher Crossen Contact Us Americas +12125535160 clientservices@moodys.com
More informationBlackLitterman Return Forecasts in. Tom Idzorek and Jill Adrogue Zephyr Associates, Inc. September 9, 2003
BlackLitterman Return Forecasts in Tom Idzorek and Jill Adrogue Zephyr Associates, Inc. September 9, 2003 Using BlackLitterman Return Forecasts for Asset Allocation Results in Diversified Portfolios
More informationHIGH DIVIDEND STOCKS IN RISING INTEREST RATE ENVIRONMENTS. September 2015
HIGH DIVIDEND STOCKS IN RISING INTEREST RATE ENVIRONMENTS September 2015 Disclosure: This research is provided for educational purposes only and is not intended to provide investment or tax advice. All
More informationInvestment Statistics: Definitions & Formulas
Investment Statistics: Definitions & Formulas The following are brief descriptions and formulas for the various statistics and calculations available within the ease Analytics system. Unless stated otherwise,
More informationThe Tangent or Efficient Portfolio
The Tangent or Efficient Portfolio 1 2 Identifying the Tangent Portfolio Sharpe Ratio: Measures the ratio of rewardtovolatility provided by a portfolio Sharpe Ratio Portfolio Excess Return E[ RP ] r
More informationReview Jeopardy. Blue vs. Orange. Review Jeopardy
Review Jeopardy Blue vs. Orange Review Jeopardy Jeopardy Round Lectures 03 Jeopardy Round $200 How could I measure how far apart (i.e. how different) two observations, y 1 and y 2, are from each other?
More informationamleague PROFESSIONAL PERFORMANCE DATA
amleague PROFESSIONAL PERFORMANCE DATA APPENDIX 2 amleague Performance Ratios Definition Contents This document aims at describing the performance ratios calculated by amleague: 1. Standard Deviation 2.
More informationAlpha  the most abused term in Finance. Jason MacQueen Alpha Strategies & RSquared Ltd
Alpha  the most abused term in Finance Jason MacQueen Alpha Strategies & RSquared Ltd Delusional Active Management Almost all active managers claim to add Alpha with their investment process This Alpha
More informationFinancial Intermediaries and the CrossSection of Asset Returns
Financial Intermediaries and the CrossSection of Asset Returns Tobias Adrian  Federal Reserve Bank of New York 1 Erkko Etula  Goldman Sachs Tyler Muir  Kellogg School of Management May, 2012 1 The
More informationCAPM, Arbitrage, and Linear Factor Models
CAPM, Arbitrage, and Linear Factor Models CAPM, Arbitrage, Linear Factor Models 1/ 41 Introduction We now assume all investors actually choose meanvariance e cient portfolios. By equating these investors
More informationActive is as Active Does: Active Share vs Tracking Error
Active is as Active Does: Active Share vs Tracking Error Sebastian Ceria Chief Executive Officer Axioma, Inc. Joint work with Vishv Jeet and Dieter Vandenbussche March 2015 FACTSET 2015 SYMPOSIUM NOT FOR
More informationSIPCO Corporate Bond Strategy
. SIPCO Corporate Bond Strategy The principles of SIPCO believe that bond investors are best served by primarily investing in corporate bonds. We are of the opinion that corporate bonds offer the best
More informationVolatility modeling in financial markets
Volatility modeling in financial markets Master Thesis Sergiy Ladokhin Supervisors: Dr. Sandjai Bhulai, VU University Amsterdam Brian Doelkahar, Fortis Bank Nederland VU University Amsterdam Faculty of
More informationDoes the Number of Stocks in a Portfolio Influence Performance?
Investment Insights January 2015 Does the Number of Stocks in a Portfolio Influence Performance? Executive summary Many investors believe actively managed equity portfolios that hold a low number of stocks
More informationSmart ESG Integration: Factoring in Sustainability
Smart ESG Integration: Factoring in Sustainability Abstract Smart ESG integration is an advanced ESG integration method developed by RobecoSAM s Quantitative Research team. In a first step, an improved
More informationApplication of Quantitative Credit Risk Models in Fixed Income Portfolio Management
Application of Quantitative Credit Risk Models in Fixed Income Portfolio Management Ron D Vari, Ph.D., Kishore Yalamanchili, Ph.D., and David Bai, Ph.D. State Street Research and Management September 263,
More informationFIN 683 Financial Institutions Management Market Risk
FIN 683 Financial Institutions Management Market Risk Professor Robert B.H. Hauswald Kogod School of Business, AU Overview Market risk: essentially a price risk a FI might need to liquidate a position
More informationAFM 472. Midterm Examination. Monday Oct. 24, 2011. A. Huang
AFM 472 Midterm Examination Monday Oct. 24, 2011 A. Huang Name: Answer Key Student Number: Section (circle one): 10:00am 1:00pm 2:30pm Instructions: 1. Answer all questions in the space provided. If space
More informationPart 2: Analysis of Relationship Between Two Variables
Part 2: Analysis of Relationship Between Two Variables Linear Regression Linear correlation Significance Tests Multiple regression Linear Regression Y = a X + b Dependent Variable Independent Variable
More information1. Portfolio Returns and Portfolio Risk
Chapter 8 Risk and Return: Capital Market Theory Chapter 8 Contents Learning Objectives 1. Portfolio Returns and Portfolio Risk 1. Calculate the expected rate of return and volatility for a portfolio of
More informationTarget Strategy: a practical application to ETFs and ETCs
Target Strategy: a practical application to ETFs and ETCs Abstract During the last 20 years, many asset/fund managers proposed different absolute return strategies to gain a positive return in any financial
More informationI. Estimating Discount Rates
I. Estimating Discount Rates DCF Valuation Aswath Damodaran 1 Estimating Inputs: Discount Rates Critical ingredient in discounted cashflow valuation. Errors in estimating the discount rate or mismatching
More informationINSTITUTIONAL INVESTMENT & FIDUCIARY SERVICES: Building a Better Portfolio: The Case for High Yield Bonds
14\GBS\22\25062C.docx INSTITUTIONAL INVESTMENT & FIDUCIARY SERVICES: Building a Better Portfolio: The Case for High Yield Bonds By Adam Marks, Area Vice President and Jamia Canlas, Senior Analyst By looking
More informationSensex Realized Volatility Index
Sensex Realized Volatility Index Introduction: Volatility modelling has traditionally relied on complex econometric procedures in order to accommodate the inherent latent character of volatility. Realized
More informationA Review of Cross Sectional Regression for Financial Data You should already know this material from previous study
A Review of Cross Sectional Regression for Financial Data You should already know this material from previous study But I will offer a review, with a focus on issues which arise in finance 1 TYPES OF FINANCIAL
More informationCHAPTER 13: EMPIRICAL EVIDENCE ON SECURITY RETURNS
CHAPTER 13: EMPIRICAL EVIDENCE ON SECURITY RETURNS Note: For endofchapterproblems in Chapter 13, the focus is on the estimation procedure. To keep the exercise feasible the sample was limited to returns
More informationNon Linear Dependence Structures: a Copula Opinion Approach in Portfolio Optimization
Non Linear Dependence Structures: a Copula Opinion Approach in Portfolio Optimization Jean Damien Villiers ESSEC Business School Master of Sciences in Management Grande Ecole September 2013 1 Non Linear
More information2007 Presentation created by: Michael E. Kitces, MSFS, MTAX, CFP, CLU, ChFC, RHU, REBC, CASL, CWPP michael@kitces.com
The Impact of Market Valuation on By: Michael E. Kitces, MSFS, MTAX, CFP, CLU, ChFC, RHU, REBC, CASL Partner, Director of Research, Pinnacle Advisory Group Publisher, The Kitces Report, www.kitces.com
More informationCorporate Finance, Fall 03 Exam #2 review questions (full solutions at end of document)
Corporate Finance, Fall 03 Exam #2 review questions (full solutions at end of document) 1. Portfolio risk & return. Idaho Slopes (IS) and Dakota Steppes (DS) are both seasonal businesses. IS is a downhill
More informationCost of Capital and Corporate Refinancing Strategy: Optimization of Costs and Risks *
Cost of Capital and Corporate Refinancing Strategy: Optimization of Costs and Risks * Garritt Conover Abstract This paper investigates the effects of a firm s refinancing policies on its cost of capital.
More informationFiduciary Insights OPTIMIZING YOUR BOND PORTFOLIO THROUGH RISK FACTOR MODELING
OPTIMIZING YOUR BOND PORTFOLIO THROUGH RISK FACTOR MODELING FIXED INCOME INVESTMENTS SPAN A BROAD RANGE OF SENSITIVITIES TO CHANGES IN YIELDS AND CREDIT SPREADS. Understanding how different types of fixed
More informationBetting Against Beta
Betting Against Beta Andrea Frazzini AQR Capital Management LLC Lasse H. Pedersen NYU, CEPR, and NBER Preliminary Copyright 2010 by Andrea Frazzini and Lasse H. Pedersen Motivation Background: Security
More informationFixed Income Training Seminar Asset Management Experience
Asset Management Fixed Income Training Seminar Asset Management Experience Philipp Büchler, Chris Koslowski, Markus Kramer, Manuel Walker Credit Suisse Asset Management Core Fixed Income Group Zurich August
More informationEnhanced Indexing equity strategy benefits from RobecoSAM scores
ARTICLE June 2014 For professional investors Enhanced Indexing equity strategy benefits from RobecoSAM scores ESG factors integrated in Robeco Enhanced Indexing RobecoSAM scores in the stock selection
More informationCase study: Making the move into investment grade corporates
National AssetLiability Management Europe Case study: Making the move into investment grade corporates Tomas Garbaravičius 3 March 2016 London Outline The reasoning behind the decision to corporate bonds
More informationWeek TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500 6 8480
1) The S & P/TSX Composite Index is based on common stock prices of a group of Canadian stocks. The weekly close level of the TSX for 6 weeks are shown: Week TSX Index 1 8480 2 8470 3 8475 4 8510 5 8500
More informationCalculating VaR. Capital Market Risk Advisors CMRA
Calculating VaR Capital Market Risk Advisors How is VAR Calculated? Sensitivity Estimate Models  use sensitivity factors such as duration to estimate the change in value of the portfolio to changes in
More informationActive Management in Swedish National Pension Funds
STOCKHOLM SCHOOL OF ECONOMICS Active Management in Swedish National Pension Funds An Analysis of Performance in the APfunds Sara Blomstergren (20336) Jennifer Lindgren (20146) December 2008 Master Thesis
More informationDevelopments in Risk Attribution
Singapore 2004 Developments in Risk Attribution Investment Managers Association of Singapore September 23, 2004 Alex Carmichael Director Risk Solutions & Operations RiskMetrics Group Asia Pacific Alex.carmichael@riskmetrics.com
More informationA New Way to Set Issuer Limits in Corporate Credit Portfolios
A New Way to Set Issuer Limits in Corporate Credit Portfolios Miikka Taurén and Thomas Philips October 14, 2014 AT A GLANCE Credit ratings are not always a reliable indicator of credit risk Credit spreads
More informationRisk Management Series
AAA AAA AAA AAA AAA AAA AAA AAA AAA AAA January 1996 Managing Market Exposure Robert Litterman Kurt Winkelmann Acknowledgements The authors are grateful to Mike Asay, Alex Bergier, Jean Paul Calamaro,
More informationModerator Timothy Wilson
Investment Symposium March 2012 P2: Portfolio Construction: Asset Allocation versus Risk/Strategy Buckets Marc Carhart Radu Gabudean Moderator Timothy Wilson Beyond Modern Portfolio Theory Radu C. Gabudean
More informationM.I.T. Spring 1999 Sloan School of Management 15.415. First Half Summary
M.I.T. Spring 1999 Sloan School of Management 15.415 First Half Summary Present Values Basic Idea: We should discount future cash flows. The appropriate discount rate is the opportunity cost of capital.
More information