are hedge funds simply too risky? an investor’s perspective€¦ · are hedge funds simply too...

Are Hedge Funds Simply too Risky? An Investor’s Perspective

Nils S. Tuchschmid Tages Capital LLP Global Association of Risk Professionals May 2015

2

The views expressed in the following material are the

author’s and do not necessarily represent the views of

the Global Association of Risk Professionals (GARP),

its Membership or its Management.

Agenda

•  Asset management or risk management?

•  Why hedge funds? •  Hedge Fund strategies and risks •  Exogenous or endogenous risk ? •  Concluding remarks « Hedge-‐fund investors and managers o2en dismiss risk management as

secondary with ”alpha” or performance as the main objec=ve » Lo A., Risk Management for Hedge Funds : IntroducFon and Overview, 2001, hJp://papers.ssrn.com/

sol3/papers.cfm?abstract_id=283308

Asset Management or Risk Management?

•  Asset management is somehow hard to disFnguish from risk management

•  …. indeed when allocaFng to risk assets –and even more so when allocaFng to investment styles or investment strategies, one needs to know something –or hopes to know something, about return generaFng processes

–  What are the underlying “risk factors” that are driven returns?

4

Asset Management or Risk Management?

Input data Corporate Bond Treasury Bond Risk-free asset

Expected Return pa 7.38% 5.75% 5.36% Volatility pm 1.58% 1.90% Correlation 0.9654

Output data $19.66 -$15.66 -$3.06 Optimal Portfolio Return and Risk

Initial Equity $1 Expected Return (monthly) 3.1% Volatility (monthly) 8.1% Ratio of equity to SD 12.31 Source: Risk Management Lessons from LTCM, Jorion P., EFM, 2000

5

Why invesFng into Hedge Funds ? … before the crisis

Source: Edhec-‐Risk

0.00%$

10.00%$

20.00%$

30.00%$

40.00%$

50.00%$

60.00%$

70.00%$

For$their$diversifica9on$benefits$with$

bonds$

For$their$diversifica9on$benefits$with$

equi9es$

Hedge$funds$offer$absolute$

returns$

BeEer$performance$on$average$than$that$of$tradi9onal$funds$

The$vola9lity$of$hedge$fund$

performance$is$lower$than$that$of$

tradi9onal$assets$

The$poten9al$for$maximal$loss$is$lower$than$for$tradi9onal$assets$

Other$

Why Hedge Funds ?

Why invesFng into Hedge Funds ? … a]er the crisis

Source: JPMorgan Cap. Intro, 2014

Why Hedge Funds ?

8 Source: Prequin Investor Interview, July 2013

… but what are the issues?

Why Hedge Funds ?

… and what are the trends ?

E&Y, Global Hedge Fund and Investor Survey 2012 9

Why Hedge Funds ?

Classically, investors tend to separate hedge funds’ risks into two broad categories, that is:

–  Market risk… or everything that could be related to markets and hedge fund strategies

–  OperaFonal risk… or any other risks that would stem from the operaFonal side of the business and not related to market-‐wide risk

To note that business risk should be part of what people defined as operaFonal risk. Yet, it certainly has a special “flavor” when it comes to hedge funds (see e.g. Comac)

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Hedge Fund Strategies and Risks

Market Risk –  SensiFvity of the fund to market risk factors, both tradiFonal and

alternaFve (yield curve, credit spread, …) –  Captured by risk factor models

Residual Risk –  Not captured by risk factor models –  Driven by the hedge fund’s parFcular poriolio holdings or investment

style ? Concentrated poriolio (area, sector, asset class), high poriolio turnover, illiquid assets, exoFc instruments,…

Tail Risk –  Stemming from exogenous extreme events and quite o]en associated

with leverage, concentraFon and liquidity (e.g. SNB January announcement)

–  PotenFal to significantly affect monthly returns in parFcular if its impact has not been observed in the past (e.g. LTCM) 11


What if it were to be “alpha” only… ?

12


Source: Brad Jones, Asset Bubbles: Re-‐thinking Policy for the Age of Asset Management, IMF Paper, 2015

DirecFonal Strategies (EH) –  Stock markets risk –  Other risks

Sector Size (Small vs. Large Caps) Style (Value vs. Growth companies) …

Event Driven Strategies –  a priori idiosyncraFc risks that risks linked to specific events

e.g. deal risk –  Some market direcFonality, for example,

Corporate M&A acFvity tends to be higher during bull markets Default rates are lower during bull markets: recovery capitalizaFon

13


RV Strategies –  Liquidity risk

Issues of converFble bonds issues for example, are on average small, which limits the depth of the market

–  Credit risk and event risk Corporate and even “sovereign” bonds have a credit risk component embedded into their prices

–  NegaFve convexity ConverFble bonds and other hybrid instruments are o]en callable

–  Model risk Complex pricing models

–  …

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TacFcal Trading Strategies –  Leverage risk –  Nonlinear market exposures to IR, FX, EquiFes, Credit or Commodity

that is stemming from the opportunistic nature of trading strategies –  Model risk and estimation risk –  … and more importantly the ability of the managers (programs) to

implement well their trading ideas

Each broad family of strategies has been empirically tested either “boJom-‐up” or “top-‐down” (see e.g. Mitchell and Pulvino (2001), Durate, Longstaff, and Yu (2007), Fung and Hsieh (2001)… Let’s think for example about the famous poriolio of lookback straddles when it comes to explain trend-‐followers’ risk-‐return profile 15


Yet, the heterogeneity of risks among strategies and styles seems to cancel out when hedge funds are bundled together into a classic mulF-‐strategy poriolio. Indeed, factor models are doing quite a good job at explaining returns. Stated otherwise, the famous “alpha” component appears o]en to be both small and insignificant.

“The empirical literature sounds irrevocable. Only a minority of hedge fund managers deliver significant and posi=ve alpha and it even seems that their number diminishes over =me. The picture is even more depressing for funds of hedge funds. They appear unable to produce alpha and barely relay the alpha, if any, generated by the underlying hedge fund managers.” Pirotte et al. 2014

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•  Data –  Monthly net-‐of-‐fees HFs’ performance provided by TASS –  Period : 1/1994 to 8/2009 –  From 4564 FoHFs to 1315 by deleFng :

•  ‘duplicated funds’ and non-‐USD funds •  funds with no informaFon on date added to database •  funds with obvious outliers

–  For the FDR methodology we require at least 60 months of returns à 280 funds

–  Returns are “unsmoothed” using the Getmansky, Lo & Makarov methodology

•  PotenFal bias –  Survivorship bias : not a problem as we have living and dead funds –  Backfilling (instant history) bias : not a problem as we delete return

entries from incepFon to the date added to database –  SelecFon bias : less of a concern for FoHFs


Source : Dewaele et al. 2013


Find the addi<onal return above the expected return (alpha) of a :

PorHolio made of HF strategies (DJCS model) PorHolio of HFs underlying factors (FH model)

Separate the cross sec<on of alphas into: Skilled funds

Unskilled funds Zero-‐alpha funds

by taking luck into account…

α extracFon

α significant ? FDR

False Discoveries Rate – an overview


20

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If we consider that funds’ alpha are divided in 3 categories (unskilled, zero alpha and skilled), we get the following cross-‐secFonal distribuFon :

StarFng from the cross-‐secFon, the FDR method separates alphas into these 3 categories by taking luck into account (by luck we mean zero-‐alpha funds that will in a classical test be considered as skilled or unskilled)

Source : Barras, Scaillet, Wermers (JoF – 2010)

Steps 1-4 Step 5 Step 5


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•  Alpha –  Regress excess returns on two sets of factors :

•  The 7+1 factors of Fung & Hsieh (2004) •  A 13 factors model, where factors are constructed as the excess return of

13 index of HF strategies provided by DJCS (“unsmoothed” using GLM methodology)

–  Using a classical linear regression framework :

•  Alpha t-‐staFsFcs : –  Instead of alpha, we rely on t-‐staFsFcs as it is shown that alpha t-‐

staFsFcs have beJer staFsFcal properFes than alphas –  As regression residuals present autocorrelaFon and heteroskedasFcity,

we use a heteroskedasFcity and autocorrelaFon-‐consistent esFmator.

ri,t = αi + βij

j=1

k

∑ Ftj +εi,t

Find alpha and alpha t-‐stats (steps 1 and 2) Hedge Fund Strategies and Risks

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•  The 7 + 1 factos of Fung and Hsieh are: –  the excess return on the S&P 500 Index –  the "small-‐minus-‐big" factor computed as the difference

between the Russell 2000 index monthly total return and the S&P 500 monthly total return

–  the monthly change in the difference between the 10-‐year Treasury constant maturity yield and the 1-‐month LIBOR

–  the change in the credit spread of Moody's BAA bond over the 10-‐year Treasury bond

–  the excess returns on a poriolio of lookback opFons on bonds, currencies and commodiFes

–  the excess return on the MSCI Emerging Markets Index

Find alpha and alpha t-‐stats (steps 1 and 2) Hedge Fund Strategies and Risks

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•  t-‐stat’s p-‐values –  FoHFs' returns are not normally-‐distributed è instead of relying on a

student-‐t distribuFon, we build the t-‐stat distribuFon under the null hypothesis using a bootstrap procedure (Kosowski et al. (2006) methodology) : •  Adding back randomly-‐sampled residuals from the former regression (step

1) to the same regression equaFon omi}ng the alpha constant (1’000 Fmes for each FoHF).

•  Bootstrapped returns are regressed against the factors, resulFng in an empirical distribuFon under the “zero-‐alpha” hypothesis.

•  The alpha p-‐value for each fund is obtained by comparing the original t-‐staFsFc to the distribuFon obtained here above.

ri,tb = βi

j

j=1

k

∑ Ftj +εi,t

b

Determine t-‐stats’ p-‐value (steps 3) Hedge Fund Strategies and Risks

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•  Percentage of zero-‐alpha funds –  If all funds were zero-‐alpha, p-‐values would be uniformly distributed

over the interval [0,1] –  Using this property, the objecFve is to choose a threshold level (λ)

above which funds are considered as being zero-‐alpha funds

–  The opFmal level (λ*) is found using a bootstrap methodology (Storey (2002))

Determine the % of zero-‐alpha funds (steps 4) Hedge Fund Strategies and Risks

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•  Determine the percentage of (un)skilled funds : –  Individual bootstrapped t-‐stats are aggregated to get the non-‐

parametric cross-‐secFonal distribuFon under H0. –  Fix thresholds on each side of the distribuFon based on various levels

of significance to be tested (10%,…,50%) for unskilled/skilled funds.

–  Count the number of funds' t-‐staFsFcs superior/inferior to these thresholds and correct these proporFons for false discoveries (computed using zero-‐alpha proporFon) to get proporFons of skilled and unskilled managers.

Determine the % of (un)skilled funds (steps 5) Hedge Fund Strategies and Risks

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•  FH regression –  A limited set of “risk factors” seems to capture well the return

generaFng process of FoFs

•  DJCS regression –  A majority of FoFs does not add “unexplained returns” or omiJed risk

factors that would not have been captured at the HFs’ level –  A]er management and incenFve fees, only 3.57% of FoHFs managed to

provide a]er-‐fees alpha, and 46.43% delivered negaFve alpha.


If factor models can explain a significant porFon of returns of poriolios of hedge funds, then one should be able to use the same models to replicate what hedge funds are doing. Let’s indeed take a simplisFc view and create a trading strategy based on the following model: … using a limited set of factors 28


rt =β1ft

1 ++β5ft5 +εt

s.t. βi

i=1

5

∑ =1

The factors are : CBOE S&P 500 BuyWrite Index Russell 2000 Index MSCI EAFE Index Barclays Capital U.S. Aggregate Corporate AA Bond Index S&P-‐GS Commodity Index

Using a 24 month rolling-‐window to esFmate weights with one-‐month lag Out-‐of-‐Sample period: Feb-‐1992 to Dec-‐2008 Sample is taken from HFRs database of FoF 29


30


Mean SD Sharpe

All fund of funds Funds 0.072 0.051 1.42 Clones 0.062 0.073 0.84

ConservaFve Funds 0.059 0.033 1.82 Clones 0.043 0.047 0.93

Diversified Funds 0.072 0.052 1.40 Clones 0.065 0.074 0.88

Market Defensive Funds 0.087 0.048 1.81 Clones 0.068 0.081 0.84

Strategic Funds 0.088 0.081 1.09 Clones 0.084 0.118 0.71

Performance of eq.-weighted portfolios. Feb-1992 to Dec-2008.

Source: Wallerstein, Tuchschmid, and Zaker (2009a)

Performance of all (eq.-‐weigthed) clones (solid thick), FoF (solid), HFRI FoF (dashed), and Composite –US Bond /S&P500 (doted). Sample period: Feb 1992 to Dec 2008.

Source: Wallerstein, Tuchschmid, and Zaker (2009a)


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Source: Tuchschmid et al. (2012) ; sample period : January 2007 – October 2010

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When investors think about other “market risk” and hedge funds, they tend to refer to:

–  Lack of transparency –  Leverage –  Liquidity risk and liquidity shocks –  … and contagion effect (?)

that make these investment vehicles prone to “blow-‐ups”. Famous examples could be LTCM, Amaranth, Peloton, Endeavour, Everest, …. “The received wisdom is that risk increases in recessions and falls in booms. In contrast, it may be more helpful to think of risk as increasing during upswings, as financial imbalances build up, and materialising in recessions ” CrockeJ A., Marrying the micro-‐ and macro-‐prudenFal dimensions of financial stability, BIS 2000

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Exogenous or endogenous risk

Quite o]en, one sees external or exogenous shocks as the only underlying drivers behind these blow-‐ups. They are unexpected events that suddenly force managers to deleverage and to realize their losses. In some cases, events can be clearly idenFfied:

–  LTCM (1998) : Russian default –  Everest or Comac (2015) : SNB’s announcement

… but the laJer is not always true (e.g. Amaranth or Endeavour and the widening of spreads) 34


“For the 10-‐year old firm, founded by Colm O'Shea, the currency move crystallized problems that were already moun=ng, another source who knows the fund said. Impa=ent with years of poor returns, investors had asked for their money back for some =me, the person said, no=ng that the fund had managed roughly $4.5 billion in late 2012 and that redemp=on requests had mounted recently. O'Shea, who had once worked for George Soros the famed global-‐macro investor, gained aRen=on with a 31 percent return in 2008, when most funds lost money. More recent returns weren't as good. In 2012, the fund lost 9.0 percent and returns for 2013 and 2014 were essen=ally flat, the person said”.

35


Source: uk.reuters.com/arFcle/2015/01/20/hedgefunds-‐comac-‐idUKL6N0UZ4SW20150120

“Comac Capital, the $1.2 billion hedge fund firm run by Colm O’Shea, is returning money to clients a2er losses incurred last week when the Swiss Na=onal Bank abandoned the franc’s cap against the euro, according to a person with knowledge of the situa=on. Comac, based in London, lost 8 percent as the franc surged as much as 41 percent versus the euro on Jan. 15. The declines bring its loss this month to 10 percent, said the person, who asked not to be iden=fied because the informa=on is private. Comac will con=nue to trade with internal money, the person said”.

36


Source: www.bloomberg.com/news/arFcles/2015-‐01-‐20/o-‐shea-‐s-‐comac-‐capital-‐to-‐return-‐investor-‐money-‐from-‐fund


Source: Bloomberg


Input data Corporate Bond Treasury Bond Risk-free asset

Expected Return pa 7.38% 5.75% 5.36% Volatility pm 1.58% 1.90% Correlation 0.9654

Output data $19.66 -$15.66 -$3.06 Optimal Portfolio Return and Risk

Initial Equity $1 Expected Return (monthly) 3.1% Volatility (monthly) 8.1% Ratio of equity to SD 12.31

seems very safe !

a « 12 times volatitly move » is needed for equity to be wiped out! However …

Source: Risk Management Lessons from LTCM, Jorion P., EFM, 2000

38


Monthly Probability of Ruin Rho SD Safety Factor Normal Student-6 Student-4

0.9999 1.56% 64.10 0.00000% 0.00000% 0.00002% 0.990 4.55% 21.98 0.00000% 0.00003% 0.00127% 0.970 7.58% 13.19 0.00000% 0.00059% 0.00954%

... ... ... ... ... ... 0.850 16.68% 6.00 0.00000% 0.04843% 0.19470% 0.800 19.24% 5.20 0.00001% 0.10099% 0.32637%

... ... ... ... ... ... 0.600 27.17% 3.68 0.01164% 0.51621% 1.05970%

Source: Risk Management Lessons from LTCM, Jorion P., EFM, 2000

39

Liquidity risk has been spoJed as a main source of hedge fund performance (e.g. Sadka 2011 or Gibson and Wang 2010) … and interesFngly enough, correlaFon of “liquidity risk factor(s)” appears to be low with the commonly used market factors.

This suggests that … hedge-‐fund returns can be characterized as selling out-‐of-‐the money put op=on on market liquidity events, collec=ng fees during normal, non-‐crisis periods and paying out during crisis periods. Sadka, Hedge Fund Performance and Liquidity Risk, Journal of Investment Management 2011

Examples of “liquidity shocks” are numerous. Recently we could think about the “Treasury flash crash” of October 15, a move of 40 bps, that is, seven standard deviaFons away from its intraday norm

40



Source: Bloomberg


Source : Sadka, Hedge Fund Performance and Liquidity Risk, Journal of Investment Management 2011


The majority of hedge fund strategies can be also analyzed in terms of risk limits or risk constraints

–  Equity L/S : sizing, net exposure, gross exposure, … –  CTAs : volaFlity target, margin to equity raFo, … –  Global Macro : VaR, … –  FI arbitrageurs : leverage (10y equivalent), VaR, … –  Credit L/S : gross exposure, spread widening, beta, … –  Event Driven, spread widening, …

… associated quite o]en with stop-‐loss policies All risk limits or risk constraints set a predetermined trading behavior or degree of risk appeFte.

43


Let’s take the simple case of a M-‐V investor with two risky securiFes (with respecFve holding a1 and a2) and cash (c). By definiFon, one should have* :

a1 + a2 + c = e where “e” stands for capital or equity. We thus simply have:

* Source: Risk and Liquidity, Shin, Oxford University Press, 2010

U = E r − ! 12τVar r != cr! + a!!! + a!!! !− !

12τVar cr! + a!r! + a!r! !

= er! + a! !! − r! + a! !! − r! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!− ! 12τ a!!σ!! + a!!σ!! + 2a!a!σ!" !

44


… and classically, we obtain: “τ”, the investor’s risk tolerance, is obviously here a key parameter…

Let’s for instance assume that e = 1 and τ = 0.25. With µ1 = 0.1, µ2 = 0.05, r0 = 0.02, σ1 = σ2 = 0.2 and ρ = 0.925, one gets:


a!a! = !τ σ!! σ!"

σ!" σ!!!! !! − r!

!! − r! !

a!a! = ! 2.2619

−1.9047 !!⟹ ! = !!""#$"!"#$%& =2.26+ 0.64

1 = 2.9!

45


Let’s now take the case of a (risk neutral) hedge fund manager*. Of course, we sFll have that :

a1 + a2 + c = e Here, the manager aims at maximizing returns for a given VaR constraint, such that If we set VaR = ασr, we thus have:

and we end up with the following problem to solve


Max!E r !subject!to!VaR! ≤ e!

ασ!! ≤ e!or!equivatenly!σ!! ≤eα

!!

ℒ = !E r − !λ !!! −!!

!!

46


Hence we get: since We obtain finally:


a!a! = ! 12!

σ!! σ!"σ!" σ!!

!! !!!! = 1

2! Σ!! !!

!! !

σ!! = a!Σa = ! 14λ! !!Σ!!! ⇒ ! 14λ! !

!Σ!!! = eα

!!

a!a! = ! e!×

1!′Σ!!!

σ!! σ!"σ!" σ!!

!! !!!! !

47


We can note that the investor’s risk tolerance “τ” is here replaced by: which somehow can be seen as the manager’s degree of “risk appeFte” –e.g. favorable market outcome leads to greater holdings of risk assets and vice versa.


e!×

1!′Σ!!!

!

48


Let’s assume that σ2 = 1, µ1= 0.1, µ2= 0.05 and α= 2.33 For posiFve correlaFon and greater than 0.50, leverage is then equal to (see Shin, op. cit.)


L = 1− !a!e = 1+ 1ασ×

ρ!! − !!!1− ρ! !!! − 2ρ!!!! + !!!

!

0"

20"

40"

60"

80"

100"

120"

140"

160"

0.5" 0.55" 0.6" 0.65" 0.7" 0.75" 0.8" 0.85" 0.9" 0.95"

Leverage'and'VaR'constraint''

ρ

49


Endogenous risk is intrinsically linked to responses originated by market parFcipants, responses that in turn amplify price moves through a feedback loop (see the previous example). If increased demand for the risky security puts large upward pressure on the price of the risky security, then the feedback effect will be strong… the amplifica=on of ini=al shocks to prices … is a key channel through which risk becomes endogenous Danielsson et al. 2010

Think about the downgrading of Ford and GM in May 2005 or more recently market reacFon to Bernanke's tapering comments in May 2013

50


Market parFcipants’ responses to liquidity shock and more precisely funding liquidity shock creates what Boyson et al. (2010) describe as “liquidity spirals”… that, in turn “affect all assets held by speculators that face funding liquidity constraints, leading to commonality in the performance of these assets”. Boyson et al. 2010

Variables used are –  measure of stock market liquidity, –  measure of credit spreads –  TED spread –  returns to banks and prime brokers –  changes in repo volume –  flows to hedge funds

51


According to Boyson et al (JF 2010), their results show that

–  “liquidity shocks to a number of contagion channel variables help explain … hedge fund contagion”.

–  There is yet liJle evidence that “liquidity itself could be a risk factor … that could explain the existence of hedge fund contagion”.

–  Stated otherwise, “while small changes to liquidity are not associated with hedge fund contagion, large shocks to liquidity are associated with it. Further, hedge funds appear to share a common exposure to large liquidity shocks, and exis=ng models used to explain hedge fund returns do not capture this exposure”.

52

Concluding remarks

ü  Factor models can do quite a good job at explaining the risk characterisFcs of hedge funds poriolios … under normal market condiFons, leading to : –  replicaFon soluFons –  decomposiFon between tradiFonal risk premia and alternaFve

risk premia

ü  If liquidity risk factors seem to embedded into hedge fund returns…

ü  they are not sufficient to understand hedge fund contagion

53

Source : DJCS index

1996 1997 1998 1999GM(30,7% EM(34,5% GM(37,1% MF(20,6% ELS(47,2% CA(25,6%

2000 2001 2002 2003 20041995

DST(26,1% GM(25,6% EM(26,6% ELS(17,2% EM(44,8% DS(15,8% GM(18,4%DST(20,0% MF(18,3% EM(28,8% DST(15,6%

ELS(23,0% DST(25,5% HF(25,9% EMN(13,3% HF(23,4% EMN(15,0% CA(14,6% GM(14,7%DS(18,1% DST(25,1% ED(14,5%

ED(20,0% EM(12,5%HF(21,7% ED(23,1% ELS(21,5% MS(7,7% ED(22,3%

HF(9,6%ED(18,3% HF(22,2% DST(20,7% RA(5,6% DST(22,2% GM(11,7%RA(14,7% ED(11,5% EMN(7,4% GM(18,0% ELS(11,6%

HF(15,4% GM(8,5%CA(16,6% CA(17,9% ED(20,0% HF(;0,4% CA(16,0% MS(11,2% FIA(8,0%EMN(9,3% EM(7,4% ELS(17,3%

MS(15,0% MS(7,5%FIA(12,5% ELS(17,1% MS(18,3% DST(;1,7% EMN(15,3% ED(7,3% EM(5,8% FIA(5,8%MS(6,3%

EMN(16,6%EMN(14,8% GM(;3,6% RA(13,2%MS(11,9% FIA(15,9% CA(14,5% CA(;4,4% FIA(12,1% HF(4,8%

FIA(6,3% RA(5,7% CA(4,0% MF(14,1% FIA(6,9%RA(11,9%

EMN(11,0% MS(14,1% RA(9,8% ED(;4,9% MS(9,4% MF(4,2% HF(4,4%MS(5,5% HF(3,0% CA(12,9% EMN(6,5%

MF(;7,1% RA(13,8% FIA(9,3% DS(;6,0% GM(5,8% ELS(2,1% MF(1,9% DST(;0,7%ED(0,2% RA(9,0% MF(6,0%

FIA(8,0% RA(5,5%DS(;7,4% MF(12,0% MF(3,1% FIA(;8,2% MF(;4,7%EM(;16,9% DS(;5,5% DS(0,4% EM(;37,7% DS(;14,2% EM(;5,5% ELS(;3,7%

DST(1,9% DS(;3,6% ELS(;1,6% EMN(7,1% CA(2,0%RA(;3,5% DS(;32,6% DS(;7,7%2012 2013 20142006 2007 2008 2009 2010 20112005

ELS.17,7% MF.18,4%EM.20,3% MF.18,3% CA.47,3% GM.13,5% GM.6,4% DST.11,8%EM.17,4% EM.20,5%MS.6,1%DS.14,9% EM.30,0% ED.12,6% FIA.4,7% MS.11,2% DST.16,0%DS.17,0% ED.15,7% GM.17,4%

EMN.4,5% FIA.11,0% ED.15,5% ELS.5,5%DST.11,7% DST.15,6% ELS.13,7% RA.;3,3%ELS.9,7%

FIA.27,4% FIA.12,5%

GM.9,2% ELS.14,4%ED.10,6% MS.11,2% FIA.4,4%MS.14,5% ED.13,2% GM.;4,6% MS.24,6% MF.12,2% DS.3,8%

ED.9,0% CA.14,3% MS.10,1%HF.9,7% HF.4,1%HF.12,6% ED.;17,7% DST.20,9% EM.11,3% MS.1,8% EM.10,3%

DST.2,5%HF.7,6% HF.13,9% EMN.9,3% ELS.;19,8%GM.3,1%HF.;19,1% ED.20,4% CA.11,0% CA.1,1% ELS.8,2% EMN.9,3%

ELS.19,5% HF.10,9% RA.0,8% CA.7,8% EM.8,8%HF.7,7% CA.6,0% ED.1,6%GM.13,5% RA.8,8% DST.;20,5% HF.18,6% DST.10,3% HF.;2,5%MS.7,5%

RA.4,9% EM.1,5%DST.8,4% MS.;23,6% RA.12,0% MS.9,3% MF.;4,2% GM.4,6%EMN.6,1% EMN.11,2%EMN.;1,2%FIA.;28,8% GM.11,6% ELS.9,3% DST.;4,2% RA.2,8% GM.4,3%RA.3,1% FIA.8,7% DS.6,0%

EMN.0,9% FIA.3,8% RA.;1,3%FIA.0,6% RA.8,1% MF.6,0% EM.;30,4% EMN.4,1% RA.3,2% EM.;6,7%MF.;2,9% MF.;2,6% CA.;1,7%MF.8,1% CA.5,2% CA.;31,6% MF.;6,6% EMN.;0,8% ELS.;7,3%MF.;0,1%

DS.;5,6%EMN.;40,3%DS.;25,0% DS.;22,5% ED.;9,1% DS.;20,4% DS.;24,9%CA.;2,5% DS.;6,6% FIA.3,8%

HF is the Global HF index. DST stands for « distressed » and DS is for « Dedicated Short » 54

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