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11 May 2011
Cross Asset Research
The Smart ETF Investor
How to earn the credit risk premium
across the business cycle with ETFs
The performance of corporate high-yield bonds is strongly influenced by the
business cycle. Based on this simple observation we present a quantitative
allocation scheme that involves a rebalancing between German government
bonds and high-yield bonds. The rebalancing is triggered by the following
simple signals:
Contents
Introduction ________________________________ 2
High yield benchmark indices___________________ 3
Investment strategy __________________________ 6
Implementing the strategy with ETFs _____________ 9
Conclusion ________________________________ 11
Literature and references _____________________ 12
■
Buy signal: Shift the capital into high-yield bonds if the business
expectations component of the Ifo Business Climate Index has increased
for at least three consecutive months. The transaction is executed on the
day the Ifo Business Climate Index is published.
■
Sell signal: The drawdown of the high-yield investment is monitored on
a daily basis. The capital is shifted to low-risk German government bonds
as soon as:
Oliver Kilian
[email protected]
– the drawdown of the high-yield investment exceeds 6%.
Sushil Krishan
[email protected]
– or the business expectations component of the Ifo Business Climate
Index has decreased for at least three consecutive months.
Oliver Weidenmüller
oliver.weidenmü[email protected]
Florian Lenhart
[email protected]
Franco Rossetti
+39 02 8862-0660
[email protected]
PERFORMANCE OF THE INVESTMENT STRATEGY
Andrea Manciocco
[email protected]
250
Paolo Giulianini
Head of ETF Trading
+44 207 826-6921
[email protected]
200
portfolio value in EUR
ETF Sales & Advisory
Chris Hofmann
Head of ETF Sales & Advisory
+49 89 378-12934
[email protected]
ETF Trading Lines
Milan
+39 02 8862-0660
Munich
+49 89 378-17585
London +44 20 782-66789
150
100
exposure to high-yield bonds
exposure to German government bonds
50
Markit iBoxx Euro Liquid High Yield (strategy)
Markit iBoxx Euro Liquid High Yield 30 (strategy)
0
2006
2007
2008
2009
2010
Source: UniCredit Research
Quantitative Cross Asset Strategy
Thorsten Weinelt, CFA (UniCredit Bank)
+49 89 378-15110
[email protected]
The proposed investment strategy can be fully implemented with ETFs.
Focusing on the two liquid euro-denominated high-yield bond indices, the
Dr. Stefan Schulz (UniCredit Bank)
+49 89 378-12765
[email protected]
■
Markit iBoxx EUR Liquid High Yield Index and the
Bloomberg
UCGR
■
Markit iBoxx EUR Liquid High Yield 30 Index,
Internet
www.research.unicreditgroup.eu
we discuss the pros and cons of using ETFs, tracking them as the basis for
our strategy.
UniCredit Research
page 1
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11 May 2011
Cross Asset Research
The Smart ETF Investor
Introduction
Historical origins
Speculative or non-investment grade bonds became ubiquitous in the 1970s and 1980s as a
then largely new financing mechanism in mergers and acquisitions and leveraged buyouts.
The money-raising abilities of such controversial financiers like Michael Milken [1], nicknamed
the "Junk Bond King", largely helped to ignite the 1980s leveraged buyout boom. Since then,
high-yield bonds have become a well-established asset class for risk-loving investors. In the
current low-yield environment, many investors are considering building up exposure to highyield bonds to enhance their returns. Not long ago, building up exposure to the high-yield
bond segment was only possible either by buying individual bonds or through an investment
in actively managed funds. With the launch of two ETFs tracking euro-denominated liquid
high-yield indices last year, investors now have two more exchange-traded products for
building up exposure to this asset class. In this work, we present a relatively simple, rulebased, and transparent investment strategy based on these high-yield ETFs.
Volatile outperformance
The performance of high-yield bonds displayed in the left chart of Figure 1 may suggest that a
buy-and-hold strategy delivers an attractive outperformance over Treasuries. However, such
a strategy would probably exceed the risk capital of most institutional investors. Furthermore,
a passive high-yield investment is so volatile that the cumulated outperformance generated
over many years may vanish in a matter of weeks.
Business cycle and spreads
For obvious reasons, the performance of corporate high-yield bonds is strongly influenced by
the business cycle. The right chart of Figure 1 impressively underscores the impact a
recession has on the average high-yield credit spread. In light of the current discussion about
historically low credit spread levels, it is interesting to note that while spread tightening has
lead to low corporate yields in absolute terms, the credit spread to the average Treasury yield
is still not at the all-time lows observed in October 1997 or June 2007. However, that is no
reason to ignore the substantial spread tightening since the Lehman bankruptcy in September
2008. The unprecedented 26-month bull market that started in March 2009 is likely to end in a
backlash with a sudden increase in credit spreads if the economic outlook dims. Therefore,
any significant exposure to high-yield bonds should be accompanied by risk management in
the form of a stop-loss rule. Because of the strong link between business cycle and credit
spreads, it is worth striving to find an economic indicator to identify the turning points of the
economy, thus connecting the stop-loss rule with a consistent start-gain policy.
TOTAL RETURN AND OUTPERFORMANCE
YIELDS, SPREADS AND RECESSIONS
25%
800%
700%
U.S. High Yield
600%
Spread
20%
U.S. High Yield
U.S. Treasuries
U.S. Treasuries
500%
15%
400%
yield
cumulative return
Recession
cumulative outperformance
300%
10%
200%
100%
5%
0%
0%
1986
-100%
86
88
90
92
94
96
98
00
02
04
06
08
10
1990
1994
1998
2002
2006
2010
Figure 1: Performance comparison between the Bank of America Merrill Lynch Treasury Master Index and the Bank of America Merrill Lynch U.S. High Yield Master
II Index (left chart). Historical spreads between the redemption yield of the Bank of America Merrill Lynch Treasury Master Index and the Bank of America Merrill
Lynch U.S. High Yield Master II Index (right chart). The gray-shaded areas indicate U.S. recessions as defined by the National Bureau of Economic Research.
Sources: Thomson Reuters Datastream, National Bureau of Economic Research
UniCredit Research
page 2
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11 May 2011
Cross Asset Research
The Smart ETF Investor
GLOBAL CORPORATE DEFAULTS
GLOBAL CORPORATE DEFAULT RATES
250
12%
Speculative grade
Investment grade
9%
150
default rate
number of defaults
Speculative grade
Investment grade
200
100
6%
3%
50
0
0%
82
84
86
88
90
92
94
96
98
00
02
04
06
08
10
82
84
86
88
90
92
94
96
98
00
02
04
06
08
10
Figure 2: Global corporate defaults both in terms of absolute numbers (left chart) and in terms of rates (right chart). The default rates that appear in the left chart are
calculated based on the number of issuers rather than the nominal amounts affected by defaults.
Source: Standard & Poor's
Corporate default rates
The risk that issuers of high-yield bonds might not be able to meet their interest and principal
obligations to bondholders is substantial, particularly when measured relative to investmentgrade bonds. As displayed in Figure 2 (left chart), the count of defaulting companies hit an alltime high in 2009 [2]. When the default rate is calculated based on the number of issuers
rather than on the nominal amounts affected by the defaults, a value of 9% is easily achieved
during an economic downturn. Because of their lower credit ratings and higher risk of default,
high-yield bonds must compensate investors adequately for the risk they take. Ideally, for a
well-diversified high-yield portfolio, the earned risk premium is high enough to compensate for
potential defaults through the downturns of many economic cycles.
High yield benchmark indices
Liquid indices
In this section we want to focus on two euro-denominated high-yield bond indices that are
tracked by recently issued ETFs, namely the
■
Markit iBoxx EUR Liquid High Yield index, and the
■
Markit iBoxx EUR Liquid High Yield 30 index.
Both of the above indices are members of the iBoxx EUR High Yield index family launched on
1 January 2007 by Markit Indices Limited [3]. They comprise the most liquid bonds from the
Markit iBoxx EUR High Yield core cum crossover index (Bloomberg: EHYTCQRQ). The cum
crossover indices, in contrast to the ex-crossover indices include split-rated bonds. While we
believe that both of the above liquid high-yield indices provide a balanced exposure to the
high-yield bond segment in terms of risk-return profile, Table 1 reveals a number of
differences regarding their construction and diversification level.
Idiosyncratic risks
UniCredit Research
Perhaps the most striking difference between both indices is the degree of diversification.
Investor exposure to idiosyncratic (firm-specific) risk is thus roughly (194/30)1/2 ≈ 2.5 times
higher for the more concentrated Markit iBoxx EUR Liquid High Yield 30 index. However,
investors should not jump the gun and shy away from this idiosyncratic risk. Note that the
index rules restrict the weight of an issuer to 5% of the market value of the index, thus
ensuring a level of issuer diversification comparable or even superior to many blue-chip equity
indices such as the German DAX or Swiss SMI.
page 3
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11 May 2011
Cross Asset Research
The Smart ETF Investor
INDEX FACTS
Markit iBoxx EUR Liquid High Yield Index
Markit iBoxx EUR Liquid High Yield 30 Index
Number of index constituents
194
30
ISIN
GB00B57G6H43
GB00B6728P13
Market Value in EUR
113,818,273,737
28,037,005,957
6.64%
6.13%
Yield (annual)
Modified Duration (annual)
Convexity (annual)
3.30
3.24
17.48
16.10
Bloomberg Code
IBOXXMJA
IBOXLH3T
RIC
.IBOXXMJA
.IBOXLH3T
Issuer type
Corporate non-financial and financial debt
Only corporate non-financial debt
Issuer domicile
EUR-denominated debt issued by both eurozone and noneurozone issuers is eligible for inclusion.
EUR-denominated debt issued by both eurozone and noneurozone issuers is eligible for inclusion. Issuers selected for the
index need to be based in a country with an investment-grade
debt rating.
Bond Type
The following bond types are eligible for the index
The following bond types are eligible for the index
Fixed-coupon bonds
Fixed-coupon bonds
Floating-rate notes
Floating-rate notes
Callable bonds
Callable bonds
Callable fixed-to-floaters
Callable fixed-to-floaters
Rating-sensitive bonds
Rating-sensitive bonds
Bonds with poison put options
Bonds with poison put options
Bonds with make-whole call or tax changes call provisions
Bonds with make-whole call or tax changes call provisions
Event-driven bonds
Registration-sensitive bonds
Step-up bonds with known schedules
Step-up bonds with known schedules
The following bond types are not eligible for the index
The following bond types are not eligible for the index
Perpetuals
Perpetuals
Zero-coupon bonds
Zero-coupon bonds
Payment-In-Kinds
Payment-In-Kinds
Putables (other than poison puts)
Putables (other than poison puts)
Sinking funds
Sinking funds
Convertibles
Convertibles
Preferred shares
Preferred shares
Private placements
Private placements
Index-linked notes
Index-linked notes
Minimum amount outstanding
EUR 250 million per bond
EUR 500 million per bond
Weight restrictions
The weight of an issuer in the index is capped at 5% of the
market value of the index at the rebalancing date. The size of
individual bonds from an issuer is capped in relation to their
market value.
The weight of an issuer in the Index is capped at 5% of the
market value of the Index at the rebalancing date.
Minimum time to maturity
2 years for new bonds. No restriction for bonds already in the
index
2 years for new bonds. 1.25 years at each quarterly rebalancing
for bonds already in the index
Maximum original time to
maturity
10.5 years to maturity as of the first settlement date of the bond
to the maturity date
10.5 years to maturity as of the first settlement date of the bond
to the maturity date
Rating
To be eligible a bond must be rated sub-investment grade. The
average of the ratings from Fitch, Moody’s and S&P is used to
determine if a bond is investment grade or high yield. The
highest rating from the three agencies is used for bonds where
all three ratings from the agencies are below investment grade.
If any of the agencies rates a bond as CC or lower, such bond is
removed from the index at the next rebalancing.
To be eligible a bond must be rated sub-investment grade. The
average of the ratings from Fitch, Moody’s and S&P is used to
determine if a bond is investment grade or high yield. The
highest rating from the three agencies is used for bonds where
all three ratings from the agencies are below investment grade.
If any of the agencies rates a bond as CC or lower, such bond is
removed from the index at the next rebalancing.
Rebalancing
Monthly in accordance with rules available on
indices.markit.com. The membership list and weightings remain
constant during the month until the next rebalancing.
Quarterly on the last calendar day of February, May, August and
November.
Pricing
New bonds enter the index at their ask price. For all other
bonds, the bid price is used. For the calculation of the index
level, the bid price is used.
New bonds enter the index at their ask price. For all other
bonds, the bid price is used. For the calculation of the index
level, the bid price is used.
History
Available daily back to 2 January 2006
Available daily back to 30 November 2006
Table 1: Index facts as of 6 May 2011
UniCredit Research
Source: Markit iBoxx
page 4
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11 May 2011
Cross Asset Research
The Smart ETF Investor
PERFORMANCE COMPARISON
RELATIVE PERFORMANCE
15%
45%
15%
Markit iBoxx EUR Liquid High Yield
10%
relative outperformance
cumulative performance
30%
eb.rexx Gov. Ger. 1.5 - 2.5
Markit iBoxx EUR High Yield core cum crossover LC
Markit iBoxx EUR Liquid High Yield
Markit iBoxx EUR Liquid High Yield 30
0%
-15%
0%
-5%
-10%
-30%
-45%
2006
Markit iBoxx EUR Liquid High Yield 30
5%
2007
2008
2009
-15%
2006
2011
2007
2008
2009
2011
Figure 3: Performance comparison between high-yield indices and a short-term German government bond index (left chart). Performance relative to the Markit iBoxx
EUR Liquid High Yield core cum crossover index (right chart). Data from 30 November 2006 to 3 May 2011.
Sources: Markit, Bloomberg, UniCredit Research
The left chart of Figure 3 compares the performance of the following high-yield bond indices
to short-term German government debt:
■
Markit iBoxx EUR High Yield core cum crossover index
■
Markit iBoxx EUR Liquid High Yield 30 index
■
Markit iBoxx EUR Liquid High Yield index.
The right chart of Figure 3 displays the cumulative performance of the Markit iBoxx EUR
Liquid High Yield 30 index and the Markit iBoxx EUR Liquid High Yield index relative to the
Markit iBoxx EUR High Yield core cum crossover benchmark index.
Tracking error analysis
Until the end of April 2009, the returns of the liquid indices relative to the core cum crossover
index appear to fluctuate rather randomly, fully in line with their ex-post tracking errors. From
November 2006 to April 2009, the values of the annualized tracking error of the Markit iBoxx
EUR Liquid High Yield index and Markit iBoxx EUR Liquid High Yield 30 index were 1.36%
and 3.44%, respectively. However, since rebalancing on the last calendar day of April 2009,
the Markit iBoxx EUR Liquid High Yield index started to significantly deviate from the Markit
Euro High Yield core cum crossover index. This is even more surprising as the ex-post
tracking error of the Markit iBoxx EUR Liquid High Yield index increased only slightly for the
period between May 2009 and April 2011 from 1.36% to 1.44%. During the same period the
tracking error of the more concentrated Liquid High Yield 30 index narrowed by more than 80
basis points from 3.44% to 2.61%. Despite these countervailing tracking-error shifts, the
broader Markit iBoxx EUR Liquid High Yield index still has a smaller tracking error to the core
cum crossover index.
The above analysis of the tracking error hints at the coincidental nature of the outperformance
achieved by the Markit iBoxx EUR Liquid High Yield 30 index since April 2009. Of course, the
outperformance can be fully explained in terms of a performance attribution analysis at the
level of the individual index constituents. However, this ex-post analysis reveals that the
observed outperformance is mainly due to a combination of rather singular events, such as an
increase in the share of subordinated financials due to weakening ratings of banks and other
financials during the peak of the recent financial crisis [4]. Rather than inferring a sustained
superiority of one of the liquid indices over the other, investors should not overstate the above
outperformance, carefully analyze the current index constituents and choose the index that
best conforms with their sector or even name-specific views on future spreads.
UniCredit Research
page 5
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11 May 2011
Cross Asset Research
The Smart ETF Investor
SECTOR ALLOCATION
Telecommunications
iBoxx EUR High Yield core cum crossover LC
Markit iBoxx EUR Liquid High Yield
Technology
Markit iBoxx EUR Liquid High Yield 30
Media
Industrials
Health Care
Construction & Materials
Basic Materials
Oil & Gas
Consumer Services
Consumer Goods
Financials
0%
5%
10%
15%
20%
25%
30%
Figure 4: Sector allocations as of 13 April 2011. Because of liquidity selection criteria, the Markit iBoxx EUR Liquid High
Yield 30 index may not provide exposure to all sectors at all times.
Source: Markit
Investment strategy
The prolonged drawdowns observed during past credit bear markets have often prevented
buy-and-hold investors from earning a long-term risk premium adequately compensating them
for the risk taken. As evidenced from Figure 3, a passive high-yield investment is often so
volatile that the cumulated outperformance generated over many years may vanish in a
matter of weeks. Therefore, our goal is to devise a quantitative, transparent, and economically
intuitive investment strategy that seeks to
■
profit from the long-term risk premium,
■
while limiting the probability of suffering extended losses.
Since the performance of high-yield bonds is strongly influenced by the business cycle, our
investment strategy will be based on identifying the turning points of the economy by using a
leading indicator. Thus, we first need to identify an appropriate economic indicator for deriving
investment timing decisions that can be applied to the liquid high-yield indices introduced in
the previous section. To confine the economic indicator to a geographic region, we analyze
the allocation of the two liquid indices by issuer domicile. Table 2 displays the corresponding
index weights of all European-domiciled issuers. Even if some of the domiciles chosen by the
issuers may not coincide with the countries where the operational units actually run their
business activities, the allocation profile in terms of issuer domicile is still a very good
approximation to the exposure to a country's economy.
Since the issuers eligible for the Markit iBoxx EUR Liquid High Yield 30 index need to be
based in a country with an investment-grade debt rating, a potential downgrade of any issuer
country to a speculative rating would strongly affect the composition of the index. In light of
the European debt crisis, such a rating action cannot be completely ruled out.
UniCredit Research
page 6
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11 May 2011
Cross Asset Research
The Smart ETF Investor
ALLOCATION BY ISSUER COUNTRY
Issuer country
Country rating / outlook
Markit iBoxx EUR Liquid High Yield Index
Markit iBoxx EUR Liquid High Yield Index 30
Austria
AAA /stable
0.61%
Belgium
AA+ / negative
0.89%
Denmark
AAA / stable
0.57%
Finland
AAA / stable
2.20%
France
AAA / stable
16.61%
12.36%
Germany
AAA / stable
16.31%
19.05%
Ireland
BBB+ / stable
4.29%
7.70%
A+ / stable
0.98%
Luxembourg
AAA / stable
15.82%
12.10%
Netherlands
AAA / stable
15.28%
20.24%
Norway
AAA / stable
0.41%
Spain
AA / negative
2.57%
Sweden
AAA / stable
0.26%
United Kingdom
AAA / stable
Italy
Jersey
0.41%
Sum
4.37%
7.88%
8.05%
85.09%
83.87%
Table 2: Index weights of European-domiciled issuers. Data as of 13 April 2011.
Sources: Markit, Standard & Poor's, UniCredit Research
Ifo Business Climate Index
Since over 80% of all issuers in the index are domiciled in Europe, a natural choice for a
leading indicator is the expectations component of the Ifo Business Climate Index. The Ifo
Business Climate Index is the most widely observed indicator for the German economy. The
index is based on around 7,000 monthly survey responses of firms in manufacturing,
construction, wholesaling and retailing. As displayed in the left chart of Figure 5, Germany's
heavy reliance on exports [5], in particular to western European countries, should provide a
somewhat secure basis to use the expectations component of the Ifo Business Climate Index
as a leading indicator for the European economy.
Ifo business expectations: A
reliable leading indicator
The analysis of historical data confirms good leading properties of the forward-looking
business expectations component for turning points in the German economy. Although the
predictive power in respect to the magnitude of business cycles varied substantially, the Ifo
expectations component has been able to track almost all important peaks and troughs of
economic activity. These leading properties suggest using changes in the Ifo business
expectations index as allocation signals for a quantitative investment scheme.
GERMANY'S SHARE OF EUROPEAN TRADE
IFO BUSINESS EXPECTATIONS
120
Spain
3.4%
110
Belgium
4.8%
index level
Switzerland
4.3%
31.1%
Austria
5.0%
100
90
business expectations
United Kingdom
5.8%
Italy
6.0%
US
6.4%
China
6.4%
80
France
9.2%
Netherlands
7.4%
business situation
December 2008
70
2005
2006
2007
2008
June 2009
2009
2010
2011
Figure 5: Germany's external trade partners by trading volume (exports + imports) in 2009 (left chart). Comparison between the Ifo business expectations and the Ifo
Business Climate Index (right chart).
Sources: German Federal Statistical Office (www.destatis.de), Bloomberg
UniCredit Research
page 7
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11 May 2011
Cross Asset Research
The Smart ETF Investor
Strategy definition
In this section we will substantiate the above ideas by presenting a quantitative allocation
scheme that is digital in the sense that at any time all of the capital is either fully allocated to
an ETF tracking a high-yield bond index or to an ETF tracking a German government bond
index. The timing strategy between German government bonds and high-yield bonds involves
a rebalancing between these two asset classes, triggered by the following simple signals:
■
Buy signal: Shift the capital into high-yield bonds if the business expectations component
of the Ifo Business Climate Index has increased for at least three consecutive months. The
transaction is executed on the day the Ifo Business Climate Index is published.
■
Sell signal: The drawdown of the high-yield investment is monitored on a daily basis. The
capital is shifted to low-risk German government bonds as soon as:
– the drawdown of the high-yield investment exceeds 6%.
– or the business expectations component of the Ifo Business Climate Index has
decreased for at least three consecutive months.
The particular choice of the drawdown limit is somewhat arbitrary. However, choosing a
drawdown limit somewhere between 5% and 7% should provide enough leeway to account
for the natural price volatility of high-yield bonds. Another possible economic rationale for a
trailing stop-loss in this range could be the desire of the investor to limit potential losses to a
threshold not exceeding the average coupon of the index. In general, the above rules connect
two key success factors for high-yield investments:
■
The trailing stop-loss limit provides consistent and forceful risk management.
■
The business expectations component of the Ifo Business Climate Index delivers an
economic rationale for timing the high-yield investment.
PERFORMANCE OF THE INVESTMENT STRATEGY
250
exposure to high-yield bonds
exposure to German government bonds
Markit iBoxx EUR Liquid High Yield (strategy)
200
Markit iBoxx EUR Liquid High Yield 30 (strategy)
portfolio value in EUR
Markit iBoxx EUR Liquid High Yield (buy-and-hold)
Markit iBoxx EUR Liquid High Yield 30 (buy-and-hold)
150
100
50
2006
2007
2008
2009
2010
Figure 6: Performance and allocation profile. Data from 30 November 2006 to 8 April 2011. Transaction costs are
included according to the model specified in the next section.
Sources: Bloomberg, UniCredit Research
UniCredit Research
page 8
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11 May 2011
Cross Asset Research
The Smart ETF Investor
In Figure 6 we backtest our strategy using both the Markit iBoxx EUR Liquid High Yield Index
and the Markit iBoxx EUR Liquid High Yield 30 index. The backtest of the strategy starts on
30 November 2006, the first date for which there is historical data available for the Markit
iBoxx EUR Liquid High Yield 30 index. The strategy starts with the capital fully invested in
short-term German government bonds. The exposure to German government bonds is
modeled using the eb.rexx Government Germany 1.5 – 2.5 index.
LIST OF TRANSACTIONS
Date
Transaction
Comment
30 November 2006
Start the strategy with buying the eb.rexx Government Germany 1.5 - 2.5
The strategy starts with exposure to government bonds because
on 26 September 2006 the Ifo business expectations had
decreased for 3 consecutive months, signaling a deteriorating
economic environment.
19 December 2006
Shift the capital into the high-yield bond index
On 19 December 2006 the Ifo business expectations had
increased for 3 consecutive months.
28 August 2007
Shift the capital into the eb.rexx Government Germany 1.5 - 2.5 index
On 28 August 2007 the Ifo business expectations had decreased
for 3 consecutive months, signaling a deteriorating economic
environment. The drawdown limit of 6% had not yet been
reached on 28 August 2007
25 March 2009
Shift the capital into the high-yield bond index
On 25 March 2009 the Ifo business expectations had increased
for 3 consecutive months, signaling economic recovery.
Table 3: Rule-based allocation decisions of the strategy during the backtesting period.
Source: UniCredit Research
The transactions triggered by our investment rules are summarized in detail in Table 3. During
the backtesting period, the stop-loss rule limiting the drawdown to a maximum loss of 6%
never took effect, since the Ifo business expectations component deteriorated before this
drawdown limit was reached. Particularly the last allocation signal for shifting the capital back
to high-yield bonds on 25 March 2009 underscores the good leading properties of the ifo
business expectations to identify turning points in the economy. Since the allocation signals
are linked to the business cycle, the strategy will most likely only trigger a small number of
transactions in the long term. However, due to the digital allocation rule, each transaction
incurs a turnover of 200%. In the next section we will focus on the practical implementation of
the investment strategy covering topics such as index tracking quality and transaction costs.
Implementing the strategy with ETFs
The implementation of the investment strategy outlined above is straightforward. Some facts
about two ETFs tracking the indices discussed in this paper are compiled in Table 4. Apart
from the fact that the high-yield indices tracked by the two ETFs differ slightly in their
constituent selection process, the two ETFs also differ in technical features such as the use of
income and replication method [6].
ETF FACTS
ETF
iShares Markit iBoxx Euro High Yield Bond
ISIN
DE000A1C8QT0
FR0010975771
iShares (www.ishares.com)
Lyxor (www.lxyoretf.com)
Markit iBoxx EUR Liquid High Yield Index
Markit iBoxx EUR Liquid High Yield 30 Index
Issuer
Benchmark
AuM in EUR
735,859,000
60,260,000
Fund inception
3 September 2010
9 December 2010
Listing on Xetra
26 November 2010
8 March 2011
Fund domicile
Ireland
France
Use of Income
Distributing (semi annually)
Accumulating
TER
Replication method
Table 4: Data as of 11 May 2011.
UniCredit Research
Lyxor ETF iBoxx EUR Liquid High Yield 30
0.50%
0.45%
physical (sampling)
swap-based
Sources: iShares, Lyxor
page 9
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11 May 2011
Cross Asset Research
The Smart ETF Investor
Index tracking quality
In order to assess the index-tracking quality of the two high-yield ETFs from iShares and
Lyxor we compare the cumulative performance of the total return benchmark index with the
actual performance of the fund. Note that in the case of the iShares ETF, displayed in the left
chart of Figure 7, we have assumed a full reinvestment of the distributed income. Judging
from the historical data available so far, both ETFs track their respective benchmarks
convincingly. The different replication methods used by the ETF issuers and the liquidity of
their underlying indices translate into different tracking errors. While the optimized physical
sampling used by iShares leads to a larger tracking error than the swap-based replication, so
far this increased tracking error has not been detrimental to the performance. On the contrary,
the tracking error of the iShares ETF is accompanied by a small outperformence relative to
the tracked index, whereas the Lyxor ETF slightly underperforms its benchmark with a very
small tracking error.
ISHARES MARKIT IBOXX EURO HIGH YIELD BOND
LYXOR ETF IBOXX EUR LIQUID HIGH YIELD 30
8%
6%
Lyxor ETF iBoxx EUR Liquid High Yield 30
iShares Markit iBoxx Euro High Yield Bond ETF
Markit iBoxx EUR Liquid High Yield 30 Index
Markit iBoxx Euro Liquid High Yield Index
6%
4%
4%
2%
2%
0%
0%
Sep 10
Oct 10
Nov 10 Dec 10
Jan 11
Feb 11 Mar 11
-2%
Jan 24, 2011
Apr 11 May 11
Feb 18, 2011
Mar 15, 2011
Apr 09, 2011
May 04, 2011
Figure 7: Index-tracking quality of the iShares Markit iBoxx Euro High Yield Bond (left chart) and the Lyxor ETF iBoxx EUR Liquid High Yield 30 (right chart).
Sources: iShares, Lyxor
Transaction costs
Since the allocation signals are linked to the business cycle, the strategy will most likely only
trigger a small number of transactions in the long term. In terms of assessing transaction
costs, it is important to remember that the calculation of the benchmark index level is based
on bid prices. Therefore, shifting the capital from high-yield bonds to short-term German
government bonds is generally a cheaper transaction than vice versa. Under normal market
conditions we assume the following fees in terms of transaction volume V :
■
for selling high-yield bond ETFs:
HY
α sell
= 0.80%
■
for buying high-yield bond ETFs:
HY
α buy
= 0.20%
■
for selling German government bond ETFs:
Gov
α sell
= 0.05%
■
for buying German government bond ETFs:
Gov
α buy
= 0.05%
While there are a number of sophisticated transaction models, for the sake of simplicity we
will assume that the investor pays a transaction-volume independent fixed fraction ΔV / V of
the traded volume as transaction costs:
UniCredit Research
(1)
HY
Gov
ΔV / V = 1 − (1 − α sell
) ⋅ (1 − α buy
) ≈ 0.8496%
(2)
Gov
HY
ΔV / V = 1 − (1 − α sell
) ⋅ (1 − α buy
) ≈ 0.2499%
page 10
See last pages for disclaimer.
11 May 2011
Cross Asset Research
The Smart ETF Investor
Conclusion
We have presented a simple, transparent, and rule-based investment strategy that aims at
maximizing the credit risk premium that can be earned with a diversified euro-denominated
high-yield bond portfolio. Our strategy is based on timing the business cycle, which should
improve the performance in terms of total return, particularly when compared to a simple buyand-hold strategy. The proposed investment strategy can be fully implemented with ETFs.
Focusing on the two liquid euro-denominated high-yield bond indices, the
■
Markit iBoxx EUR Liquid High Yield index and the
■
Markit iBoxx EUR Liquid High Yield 30 index,
we discussed the pros and cons of using the ETFs, tracking them as the basis for our
strategy. In summary, even if the more concentrated Markit iBoxx EUR Liquid High Yield 30
index has outperformed the broader Markit iBoxx EUR Liquid High Yield index by over 15%
since April 2009, we have found no evidence that this outperformance is due to a
systematically superior index concept. With the Markit iBoxx EUR Liquid High Yield 30 index
outperforming the Markit iBoxx EUR Liquid High Yield index by only 0.37% this year, the
spread tightening potential of the more concentrated Markit iBoxx EUR Liquid High Yield 30
index seems to be exhausted.
ETF VS INDEX-PERFORMANCE
ETF / Index
absolute performance
performance relative to benchmark
iShares Markit iBoxx Euro High Yield Bond ETF
2.19%
+0.18%
Lyxor ETF iBoxx EUR Liquid High Yield 30
1.98%
-0.13%
Markit iBoxx Euro Liquid High Yield
2.02%
Markit iBoxx Euro Liquid High Yield 30 Index
2.11%
Table 5: Performance figures for the period from January 24, 2011 to May 4, 2011.
Sources: Bloomberg, Lyxor, iShares, UniCredit Research
So far, the iShares Markit iBoxx Euro High Yield Bond ETF has attracted much more assets
than the Lyxor iBoxx EUR Liquid High Yield 30 ETF. A performance comparison between
these ETFs is only possible for a relatively short period starting on 20 January 2011, the
launch date of the Lyxor iBoxx EUR Liquid High Yield 30 ETF. As of 4 May 2011, the iShares
Markit iBoxx Euro High Yield Bond ETF has outperformed the Lyxor iBoxx EUR Liquid High
Yield 30 ETF by +21 basis points, whereas the relative performance of their underlying
indices for the same period amounts to -9 basis points. However, keep in mind that the
performance figures in Table 5 should not be over-interpreted as they only represent a
snapshot.
Stefan Schulz
+49 89 378-12765
[email protected]
UniCredit Research
page 11
See last pages for disclaimer.
11 May 2011
Cross Asset Research
The Smart ETF Investor
Literature and references
[1] http://en.wikipedia.org/wiki/Michael_Milken
[2] Default, Transition, and Recovery: 2010 Annual Global Corporate Default Study and
Rating Transitions, Standard & Poor's, 30 March 2011
[3] www.markit.com
[4] High Yield Pacenotes, UniCredit Research, 1 April 2011
[5] Aussenhandel. Rangfolge der Handelspartner im Aussenhandel der Bundesrepublik
Deutschland [Ranking of trade partners in foreign trade of the Federal Republic of
Germany] 2009, German Federal Statistical Office, 16 December 2010
[6] Delta One Navigator – ETF/ETC Manual, UniCredit Corporate & Investment Banking,
1st Quarter 2011
UniCredit Research
page 12
See last pages for disclaimer.
11 May 2011
Cross Asset Research
The Smart ETF Investor
Disclaimer
Our recommendations are based on information obtained from, or are based upon public information sources that we consider to be reliable but for the completeness and
accuracy of which we assume no liability. All estimates and opinions included in the report represent the independent judgment of the analysts as of the date of the issue. We
reserve the right to modify the views expressed herein at any time without notice. Moreover, we reserve the right not to update this information or to discontinue it altogether
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This analysis is for information purposes only and (i) does not constitute or form part of any offer for sale or subscription of or solicitation of any offer to buy or subscribe for any
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ORGANIZATIONAL AND ADMINISTRATIVE ARRANGEMENTS TO AVOID AND PREVENT CONFLICTS OF INTEREST
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UniCredit Research
page 13
11 May 2011
Cross Asset Research
The Smart ETF Investor
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UniCredit Research
page 14
11 May 2011
Cross Asset Research
The Smart ETF Investor
UniCredit Research*
Thorsten Weinelt, CFA
Global Head of Research & Chief Strategist
+49 89 378-15110
[email protected]
Dr. Ingo Heimig
Head of Research Operations
+49 89 378-13952
[email protected]
Cross Asset Research
Economics & FI/FX Research
Quantitative Cross Asset Research
Economics & Commodity Research
Dr. Stefan Schulz
+49 89 378-12765
[email protected]
Andreas Rees, Chief German Economist
+49 89 378-12576
[email protected]
Equity Strategy
Marco Valli, Chief Italian Economist
+39 02 8862-8688
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UniCredit Research
page 15