💻
Kyle Law's Blog
  • 💻Kyle Law
  • 😀About Me
  • Blogs
    • AWS
      • Card Clash
      • CloudQuest
        • Solution Architect Role
          • CloudQuest - Deploying RESTful APIs
      • Mock Exam
      • DVA-C02
        • TJ demo t
        • Page 4
        • Practice Test 1 (SM)
        • Deployment
        • Deployment with AWS Services
        • Security
        • Troubleshooting and Optimization
        • Stephen Maarek Course study
      • SAP-C02
        • Daily Summary
        • 22 Mar 2024 noon study
        • 22 Mar 2024 night study
        • 23 Mar 2024 Morning study
        • 23 Mar 2024 noon study
        • 25 Mar 2024 morning study
        • 25 Mar 2024 noon study
        • 26 Mar 2024 morning study
        • 27 Mar 2024 noon study
        • 27 Mar 2024 evening study
        • 30 Mar 2024 Morning study
        • 19 Apr 2024 evening study
        • 20 Apr evening study
        • Design for new solutions (29%)
        • Design Solutions for Organizational Complexity (26%)
        • Continuous Improvement for Existing Solutions (25%)
        • Accelerate Workload Migration and Modernization (20%)
      • SAA C03
    • Practice test 1
    • CFA L3
      • Capital Market Expectations
        • Brian O'Reilly Case Scenario
        • Exeter Asset Management Case Scenario
        • Minglu Li Case Scenario
      • CME (Part 2): Forecasting Asset Class Returns
        • Intro
        • Overview of Tools and Approaches
        • Forecasting Fixed Income Ret
        • Risks in Emerging Market Bonds
        • Forecasting Equity Return
        • Forecasting Real Estate Returns
        • Forecasting Exchange Rates
        • Forecasting Volatility
        • Adjusting Global Portfolio
        • SUMMARY
        • Practice Questions
      • Overview of Asset Allocation
        • INTRODUCTION
        • INVESTMENT GOVERNANCE BACKGROUND
        • THE ECONOMIC BALANCE SHEET AND ASSET ALLOCATION
        • APPROACHES TO ASSET ALLOCATION
        • MODELING ASSET CLASS RISK
        • STRATEGIC ASSET ALLOCATION
        • STRATEGIC ASSET ALLOCATION: ASSET ONLY
        • STRATEGIC ASSET ALLOCATION: LIABILITY RELATIVE
        • STRATEGIC ASSET ALLOCATION: GOALS BASED
        • IMPLEMENTATION CHOICES
        • REBALANCING: STRATEGIC CONSIDERATIONS
        • SUMMARY
      • Questions (Asset Allocations)
      • PRINCIPLES OF ASSET ALLOCATION
      • INTRODUCTION
      • ASSET-ONLY ASSET ALLOCATIONS AND MEAN–VARIANCE OPTIMIZATION
      • MONTE CARLO SIMULATION
      • CRITICISMS OF MEAN–VARIANCE OPTIMIZATION
      • ADDRESSING THE CRITICISMS OF MEAN–VARIANCE OPTIMIZATION
      • ADDING CONSTRAINTS BEYOND BUDGET CONSTRAINTS, RESAMPLED MVO AND OTHER NON-NORMAL OPTIMIZATION APPROA
      • ALLOCATING TO LESS LIQUID ASSET CLASSES
      • RISK BUDGETING
      • FACTOR-BASED ASSET ALLOCATION
      • DEVELOPING LIABILITY-RELATIVE ASSET ALLOCATIONS AND CHARACTERIZING THE LIABILITIES
      • APPROACHES TO LIABILITY-RELATIVE ASSET ALLOCATION: SURPLUS OPTIMIZATION
      • Page 1
      • Page 2
      • Page 3
      • DEVELOPING GOALS-BASED ASSET ALLOCATIONS
      • CONSTRUCTING SUB-PORTFOLIOS AND THE OVERALL PORTFOLIO
      • REVISITING THE MODULE PROCESS IN DETAIL
      • ISSUES RELATED TO GOALS-BASED ASSET ALLOCATION
      • HEURISTICS AND OTHER APPROACHES TO ASSET ALLOCATION
      • SUMMARY
      • Questions
      • CFA Study 13 May Night
      • 15 May 2024 - Night Study
      • 16 May 12am study
      • 16 May noon study
      • 16 May midnight study
      • 17 May night study
      • 17 May midnight study
      • 18 May noon study
      • 18 May night study
      • 18 May midnight study (Options)
      • 19 May noon study - volatility
      • 19 May 6pm study - options practices
      • 20 May morning study (swaps, forwards, futures)
      • Practice: Swaps, Forwards, and Futures Strategies
      • Practice - Heights Case Scenario
      • Practice - Tribeca Case Scenario
      • CURRENCY MANAGEMENT: AN INTRODUCTION
      • 30 May evening study
      • 31 May morning study
      • 31 May Morning study - part 2 - Fixed Income Portflio MGT
      • 31 May Noon study -Currency Management Practice Question
      • 3 June - Fixed Income
      • Practice - Fixed Income
      • 5 June - LIABILITY-DRIVEN AND INDEX-BASED STRATEGIES
      • 8 June - skipped parts
      • 8 June - Practice Questions - Liability Driven and Index-based strategies
      • 10 June - Yield Curve Strategies
      • 11 June - YC Strategies skipped
      • 12 June - YC Strategies practices
      • 19 June - FI Active Mgt - Credit Strategies (skippe
      • 19 June - FI Active mgt summary
      • 19 June - FI Active Mgt: Credit Strategies
      • Equity Portfolio MGT (Gist)
      • Equity Portfolio Management (Skipped)
      • Practices
      • Passive Equity Investing (Brief)
      • Passive Equity Investing (Skipped)
      • Page 5
      • Practice (PEI)
      • ACTIVE EQUITY INVESTING: STRATEGIES
      • Actove Equity Investing (Skipped)
      • Active Equity Investing (Practice Questions)
      • ACTIVE EQUITY INVESTING: PORTFOLIO CONSTRUCTION
      • Active Equity Investing - Portfolio Construction (Skipped)
      • AEI - Portfolio Constructions (Practices)
      • Hedge Fund Strategies (brief)
      • HF Strategies
    • Chess
      • Game Analysis
      • Middlegame Plan
      • Endgame
    • Reading
    • Coursera
      • Google Cybersecurity
      • Untitled
    • DesignGurus
      • Grokking System Design Fundamentals
    • Page 6
  • Page
  • Others
    • Piano
      • My Piano Performance collection
      • unravel (Animenz arrangement)
      • ABRSM Grade 8 - Syllabus 2023 - 2024
        • A1 - Prelude and Fugue in B Flat
        • B2 - Étude in D flat
        • C3 - Over the Bars
        • C8 - Caballos Españoles
  • ColdPlay concert 26 Jan 2024
  • Grade 5 Theory
    • Instruments
    • G5 Terms
  • Rinjani
Powered by GitBook
On this page
  1. Blogs
  2. CFA L3

31 May Morning study - part 2 - Fixed Income Portflio MGT

OVERVIEW OF FIXED-INCOME PORTFOLIO MANAGEMENT

by Bernd Hanke, PhD, CFA and Brian J. Henderson, PhD, CFA

Bernd Hanke, PhD, CFA, is at Global Systematic Investors LLP (United Kingdom). Brian J. Henderson, PhD, CFA, is at the George Washington University (USA).

LEARNING OUTCOMES

The candidate should be able to:

  • discuss roles of fixed-income securities in portfolios and how fixed-income mandates may be classified

  • describe fixed-income portfolio measures of risk and return as well as correlation characteristics

  • describe bond market liquidity, including the differences among market sub-sectors, and discuss the effect of liquidity on fixed-income portfolio management

  • describe and interpret a model for fixed-income returns

  • discuss the use of leverage, alternative methods for leveraging, and risks that leverage creates in fixed-income portfolios

  • discuss differences in managing fixed-income portfolios for taxable and tax-exempt investors

ROLES OF FIXED-INCOME SECURITIES IN PORTFOLIOS

Learning Outcome

  • discuss roles of fixed-income securities in portfolios and how fixed-income mandates may be classified

Fixed-income securities serve important roles in investment portfolios, including diversification, regular cash flows, and possible inflation hedging. We will briefly review the roles in turn.

Diversification Benefits

Fixed-income investments can provide diversification benefits when combined with other asset classes in a portfolio. Recall that a major reason portfolios can effectively reduce risk is that combining securities whose returns are not perfectly correlated (i.e., a correlation coefficient of less than +1.0) provides risk diversification. Lower correlations are associated with higher diversification benefits and lower risk. The challenge in diversifying risk is to find assets with correlations much lower than +1.0.

Correlations of fixed-income and equity securities vary, but adding fixed-income exposure to portfolios that include equity securities is usually an effective way to obtain diversification benefits. Fixed-income investments may also provide risk reduction because of their low correlations with other asset classes, such as real estate and commodities. Exhibit 1 shows the correlation between the S&P 500 Index and various fixed-income categories based on total returns (monthly) over a 20-year period ending in December 2019.

Exhibit 1:

Total Return Correlations between US Fixed Income and Equities

Fixed-Income Indexes

US Aggregate

10Y US Treasury

US Corporate Bonds

Global Aggregate

US TIPS

US High Yield

Emerging Market (USD)

S&P 500

–0.09

–0.30

0.20

0.15

0.02

0.63

0.51

Note: Bloomberg Barclays Indices are shown.

Source: Bloomberg.

Exhibit 2 shows the divergent performance of US equities and bonds from the end of 2019 to the end of March 2020. For example, bonds outperformed equities amid the fears over the global COVID-19 pandemic in Q1 2020.

Exhibit 2:

Returns of S&P 500 vs. 10-Year Treasuries, 12 December 2019–31 March 2020

Note: Daily data; constant-maturity 10-year Treasuries used.

Within the fixed-income asset class, the correlation between fixed-income indexes will be driven largely by the interest rate component (i.e., duration) and by geography. Rate changes can explain a significant amount of movement in fixed-income securities prices. The credit component or credit spread will likely result in diversification given differences in sectors, credit quality, and geography. For example, investment-grade securities may exhibit less correlation with below-investment-grade securities and with emerging market securities and equities. The rate component of the return can be isolated by calculating correlations using excess returns (this is more meaningful when evaluating returns across fixed-income sectors). Exhibit 3 shows correlations on an excess return basis between various fixed-income indexes.

Exhibit 3:

Excess Return Correlations of Barclays Bloomberg Indexes over a 20-Year Period

US Aggregate
US Corporate
Global Aggregate
US High Yield
Emerging Market (USD)

US Aggregate

1.00

US Corporate

0.93

1.00

Global Aggregate

0.88

0.86

1.00

US High Yield

0.86

0.84

0.76

1.00

Emerging Market (USD)

0.79

0.76

0.74

0.80

1.00

Notes: Bloomberg Barclays Indices shown. Based on monthly data over 20 years ending December 2019.

Source: Bloomberg.

Importantly, correlations are not constant over time.

During a long historical period, the average correlation of returns between two asset classes may be low, but in any particular period, the correlation can differ from the average correlation.

During periods of market stress, investors may exhibit a “flight to quality” by buying safer assets, such as government bonds (increasing their prices), and selling riskier assets, such as equity securities and high-yield bonds (lowering their prices).

These actions may decrease the correlation between government bonds and equity securities, as well as between government bonds and high-yield bonds.

At the same time, the correlation between riskier assets, such as equity securities and high-yield bonds, may increase.

Note that similar to correlations, volatility (standard deviation) of asset class returns may also vary over time. If interest rate volatility increases, bonds, particularly those with long maturities, can exhibit higher near-term volatility relative to the average volatility over a long historical period.

The standard deviation of returns for lower-credit-quality (high-yield) bonds can rise significantly during times of financial stress, because as credit quality declines and the probability of default increases, investors often view these bonds as being more similar to equities.

Exhibit 4 shows the annual returns of the S&P 500 versus the Bloomberg Barclays US Corporate High Yield Index over a 20-year period ending in December 2019. It illustrates how the fixed-income sector and equities can behave in a similar way. Recall that both asset classes are strongly linked to the issuer’s business performance and fundamentals. Over the 20-year period, the average return was 7.96% and 6.26% for the high-yield index and the S&P 500, respectively, and the standard deviation was 15.54% and 17.02%, respectively. The correlation was 0.69.

Exhibit 4:

Relationship between S&P 500 and High-Yield Returns

Benefits of Regular Cash Flows

Fixed-income investments typically produce regular cash flows for a portfolio. Regular cash flows allow investors—both individual and institutional—to meet known future obligations, such as tuition payments, pension obligations, and payouts on life insurance policies. In these cases, future liabilities can be estimated with some reasonable certainty. Fixed-income securities are often acquired and “dedicated” to funding those future liabilities. In dedicated portfolios, fixed-income securities are selected with cash flows matching the timing and magnitude of projected future liabilities.

It is important to note that reliance on regular cash flows assumes that no credit event (such as an issuer missing a scheduled interest or principal payment) or other market event (such as a decrease in interest rates that causes an increase in prepayments of mortgages underlying mortgage-backed securities) will occur.

These events may cause actual cash flows of fixed-income securities to differ from expected cash flows. If any credit or market event occurs or is forecasted to occur, a portfolio manager may need to adjust the portfolio.

Inflation-Hedging Potential

Some fixed-income securities can provide a hedge for inflation.

Bonds with floating-rate coupons can protect interest income from inflation because the market reference rate should adjust for inflation over time.

The principal payment at maturity is unadjusted for inflation.

Inflation-linked bonds provide investors with valuable inflation-hedging benefits by paying a return that is directly linked to an index of consumer prices and adjusting the principal for inflation.

The return on inflation-linked bonds, therefore, includes a real return plus an additional component that is tied directly to the inflation rate.

All else equal, inflation-linked bonds typically exhibit lower return volatility than conventional bonds and equities do because the volatility of the returns on inflation-linked bonds depends on the volatility of real, rather than nominal, interest rates.

The volatility of real interest rates is typically lower than the volatility of nominal interest rates that drive the returns of conventional bonds and equities.

Many governments in developed countries and some in developing countries have issued inflation-linked bonds, as have financial and non-financial corporate issuers. For investors with long investment horizons, especially institutions facing long-term liabilities (for example, defined benefit pension plans and life insurance companies), inflation-linked bonds are particularly useful.

Adding inflation-indexed bonds to diversified portfolios of bonds and equities typically results in superior risk-adjusted real portfolio returns. This improvement occurs because inflation-linked bonds can effectively represent a separate asset class, since they offer returns that differ from those of other asset classes and add to market completeness. Introducing inflation-linked bonds to an asset allocation strategy can result in a superior mean–variance-efficient frontier.

EXAMPLE 1

Adding Fixed-Income Securities to a Portfolio

Mary is anxious about the level of risk in her portfolio because of a recent period of increased equity market volatility. Most of her wealth is invested in a diversified global equity portfolio.

She contacts two wealth management firms (Firm A and Firm B) for advice. In her conversations with each adviser, she expresses her desire to reduce her portfolio’s risk and to have a portfolio that generates a cash flow stream with consistent purchasing power over her 15-year investment horizon.

The correlation coefficient of Mary’s diversified global equity portfolio with a diversified fixed-coupon bond portfolio is –0.10 and with a diversified inflation-linked bond portfolio is 0.10. The correlation coefficient between a diversified fixed-coupon bond portfolio and a diversified inflation-linked bond portfolio is 0.65.

The adviser from Firm A suggests diversifying half of her investment assets into nominal fixed-coupon bonds. The adviser from Firm B also suggests diversification but recommends that Mary invest 25% of her investment assets in fixed-coupon bonds and 25% in inflation-linked bonds.

  1. Evaluate the advice given to Mary by each adviser on the basis of her stated desires regarding portfolio risk reduction and cash flow stream. Recommend which advice Mary should follow, making sure to discuss the following concepts in your answer:

    1. Diversification benefits

    2. Cash flow benefits

    3. Inflation-hedging benefits

    Solution:

    Advice from Firm A:

    Diversifying into fixed-coupon bonds would offer substantial diversification benefits in lowering overall portfolio volatility (risk) given the negative correlation of –0.10. The portfolio’s volatility, measured by standard deviation, would be lower than the weighted sum of standard deviations of the diversified global equity portfolio and the diversified fixed-coupon bond portfolio. The portfolio will generate regular cash flows because it includes fixed-coupon bonds. This advice, however, does not address Mary’s desire to have the cash flows maintain purchasing power over time and thus serve as an inflation hedge.

    Advice from Firm B:

    Diversifying into both fixed-coupon bonds and inflation-linked bonds offers additional diversification benefits beyond that offered by fixed-coupon bonds only. The correlation between diversified global equities and inflation-linked bonds is only 0.10. The correlation between nominal fixed-coupon bonds and inflation-linked bonds is 0.65, which is also less than 1.0. The portfolio will generate regular cash flows because of the inclusion of fixed-coupon and inflation-linked bonds. Adding the inflation-linked bonds helps at least partially address Mary’s desire for consistent purchasing power over her investment horizon.

    Which Advice to Choose:

    On the basis of her stated desires and the analysis given, Mary should follow the advice provided by Firm B.

CLASSIFYING FIXED-INCOME MANDATES

The previous section covered the roles of fixed-income securities in portfolios and the benefits these securities provide. When investment mandates include an allocation to fixed income, investors need to decide how to add fixed-income securities to portfolios. Fixed-income mandates can be broadly classified into liability-based mandates and total return mandates. Exhibit 5 provides a broad overview of the different types of mandates, splitting the universe into two broad categories—liability-based mandates and total return mandates.

Exhibit 5:

Fixed-Income Mandates

Liability-Based Mandates

Liability-based mandates are investments that take an investor’s future obligations into consideration. Liability-based mandates are managed to match or cover expected liability payments (future cash outflows) with future projected cash inflows. As such, they are also referred to as asset/liability management (ALM) or mandates that use liability-driven investments (LDIs). These types of mandates are structured in a way to ensure that a liability or a stream of liabilities (e.g., a company’s pension liabilities or those projected by insurance companies) can be covered and that any risk of shortfalls or deficient cash inflows is minimized. Cash flow matching is an immunization approach that attempts to ensure that all future liability payouts are matched precisely by cash flows from bonds or fixed-income derivatives. Duration matching is an immunization approach that is based on the duration of assets and liabilities. Ideally, the liabilities being matched (the liability portfolio) and the portfolio of assets (the bond portfolio) should be affected similarly by a change in interest rates. The mandates may use futures contracts (such as in a derivatives overlay) and, as in the case of contingent immunization—a hybrid approach that combines immunization with an active management approach when assets exceed the present value of liabilities—may allow for active bond portfolio management. Such liability-based mandates, which will be covered in detail later, are important because of their extensive use by such entities as pension plans and insurance companies.

Total Return Mandates

Total return mandates are generally managed to either track or outperform a market-weighted fixed-income benchmark, such as the Bloomberg Barclays Global Aggregate Bond Index. They are used by many types of investors, including individuals, foundations, endowments, sovereign wealth funds, and defined contribution retirement plans. Liability-based and total return mandates exhibit common features, such as the goal to achieve the highest risk-adjusted returns (or perhaps highest yields to maturity) given a set of constraints. The two types of mandates, however, have fundamentally different objectives. A common total return approach is pure indexing. It attempts to replicate a bond index as closely as possible and is sometimes referred to as “full replication.” Under this approach, the targeted active return (portfolio return minus benchmark return, also known as “tracking difference”) and active risk (annualized standard deviation of active returns, also known as the benchmark tracking risk or tracking error) are both zero. In practice, even if the active risk is zero, the realized portfolio return will almost always be lower than the corresponding index return because of trading costs and management fees. We will explain the limitations of this approach later, in our coverage of index-based strategies.

An enhanced indexing approach maintains a close link to the benchmark but seeks to generate some outperformance relative to the benchmark. As with the pure indexing approach, in practice, enhanced indexing allows small deviations in portfolio holdings from the benchmark index but tracks the benchmark’s primary risk factor exposures very closely (particularly duration). Unlike the pure indexing approach, however, minor risk factor mismatches (e.g., sector or quality bets) are used in enhanced indexing.

Active management allows larger risk factor mismatches relative to a benchmark index. These mismatches may cause significant return differences between the active portfolio and the underlying benchmark. Most notably, portfolio managers may take views on portfolio duration that differ markedly from the duration of the underlying benchmark. To take advantage of potential opportunities in changing market environments, active managers may incur significant portfolio turnover—often considerably higher than the underlying benchmark’s turnover. Active portfolio managers normally charge higher management fees than pure or enhanced indexing managers charge.

Exhibit 6 summarizes the key features of the total return approaches.

Exhibit 6:

Total Return Approaches: Key Features

Pure Indexing

Enhanced Indexing

Active Management

Objective

Match benchmark return and risk as closely as possible

Modest outperformance (generally 20–30 bps) of benchmark while active risk is kept low (typically around 50 bps or lower)

Higher outperformance (generally around 50 bps or more) of benchmark and higher active risk levels

Portfolio weights

Ideally the same as benchmark or only slight mismatches

Small deviations from underlying benchmark

Significant deviations from underlying benchmark

Target risk factor profile

Aims to match risk factors exactly

Most primary risk factors are closely matched (in particular, duration)

Large risk factor deviations from benchmark (in particular, duration; note that some active strategies do not take large risk factor deviations and focus on high idiosyncratic risk)

Turnover

Similar to underlying benchmark

Slightly higher than underlying benchmark

Considerably higher than underlying benchmark

Fixed-Income Mandates with ESG Considerations

Some fixed-income mandates include a requirement that environmental, social, and governance (ESG) factors be considered during the investment process. When considering these factors, an analyst or portfolio manager may look for evidence of whether the portfolio contains companies whose operations are favorable or unfavorable in the context of ESG and whether such companies’ actions and resource management practices reflect a sustainable business model. For example, the analyst or portfolio manager may consider whether a company’s activities involved significant environmental damage, instances of unfair labor practices, or lapses in corporate governance integrity. For companies that do not fare favorably in an ESG analysis, investors may assume that these companies are more likely to encounter future ESG-related incidents that could cause serious reputational and financial damage to the company. Such incidents could impair a company’s credit quality and result in a decline in both the price of the company’s bonds and the performance of a portfolio containing those bonds.

EXAMPLE 2

The Characteristics of Different Total Return Approaches

  1. A consultant for a large corporate pension plan is looking at three funds (Funds X, Y, and Z) as part of the pension plan’s global fixed-income allocation. All three funds use the Bloomberg Barclays Global Aggregate Bond Index as a benchmark. Exhibit 7 provides characteristics of each fund and the index. Identify the approach (pure indexing, enhanced indexing, or active management) that is most likely used by each fund and support your choices by referencing the information in Exhibit 7.

    Exhibit 7:

    Characteristics of Funds X, Y, and Z and the Bloomberg Barclays Global Aggregate Bond Index

    Risk and Return Characteristics

    Fund X

    Fund Y

    Fund Z

    Bloomberg Barclays Global Aggregate Bond Index

    Average maturity (years)

    8.61

    8.35

    9.45

    8.34

    Modified duration (years)

    6.37

    6.35

    7.37

    6.34

    Average yield to maturity (%)

    1.49

    1.42

    1.55

    1.43

    Convexity

    0.65

    0.60

    0.72

    0.60

    Quality

    AAA

    41.10

    41.20

    40.11

    41.24

    AA

    15.32

    15.13

    14.15

    15.05

    A

    28.01

    28.51

    29.32

    28.78

    BBB

    14.53

    14.51

    15.23

    14.55

    BB

    0.59

    0.55

    1.02

    0.35

    Not rated

    0.45

    0.10

    0.17

    0.05

    Maturity Exposure

    0–3 years

    21.43

    21.67

    19.20

    21.80

    3–5 years

    23.01

    24.17

    22.21

    24.23

    5–10 years

    32.23

    31.55

    35.21

    31.67

    10+ years

    23.33

    22.61

    23.38

    22.30

    Country Exposure

    United States

    42.55

    39.44

    35.11

    39.56

    Japan

    11.43

    18.33

    13.33

    18.36

    France

    7.10

    6.11

    6.01

    6.08

    United Kingdom

    3.44

    5.87

    4.33

    5.99

    Germany

    6.70

    5.23

    4.50

    5.30

    Italy

    4.80

    4.01

    4.43

    4.07

    Canada

    4.44

    3.12

    5.32

    3.15

    Other

    19.54

    17.89

    26.97

    17.49

    Notes: Quality, maturity exposure, and country exposure are shown as a percentage of the total for each fund and the index. Weights do not always sum to 100 because of rounding. Historical data used as of February 2016.

    Source: Barclays Research.

    Solution:

    Fund X most likely uses an enhanced indexing approach. Fund X’s modified duration and convexity are very close to those of the benchmark but still differ slightly. The average maturity of Fund X is slightly longer than that of the benchmark, whereas Fund X’s average yield to maturity is slightly higher than that of the benchmark. Fund X also has deviations in quality, maturity exposure, and country exposures from the benchmark, providing further evidence of an enhanced indexing approach. Some of these deviations are meaningful; for example, Fund X has a relatively strong underweighting in Japan.

    Fund Y most likely uses a pure indexing approach because it provides the closest match to the Bloomberg Barclays Global Aggregate Bond Index. The risk and return characteristics are almost identical for Fund Y and the benchmark. Furthermore, quality, maturity exposure, and country exposure deviations from the benchmark are very minor.

    Fund Z most likely uses an active management approach because risk and return characteristics, quality, maturity exposure, and country exposure differ markedly from the index. The difference can be seen most notably with the mismatch in modified duration (7.37 for Fund Z versus 6.34 for the benchmark). Other differences between Fund Z and the index exist, but a sizable duration mismatch provides the strongest evidence of an active management approach.

Previous31 May morning studyNext31 May Noon study -Currency Management Practice Question

Last updated 1 year ago