Upload
daniela-malone
View
216
Download
1
Tags:
Embed Size (px)
Citation preview
The Risks of Portfolios of Hedge Funds
Drago IndjicFauchier Partners
PRMIA, 14 May 2003, London
El Pais, 24 Feb 2003
• Don’t believe everything you read
– Negative media bias
– Cliché: “LTCM”, “Soros”, “Courtisans” …
= Investor education, academic research
1 Speculators
2 Early 21c.
Risk
100
125
150
175
200
225
250
Apr-95 Oct-95 Apr-96 Oct-96 Apr-97 Oct-97 Apr-98 Oct-98 Apr-99 Oct-99 Apr-00 Oct-00 Apr-01 Oct-01 Apr-02 Oct-02
HFRI FOF Offshore MSCI
Source: HFR, Pertrac, Fauchier
• Hedge fund industry
• Investment strategies
• Investor’s perspective
• Data, Transparency and Estimation Risks
• Hedge fund risk
• Portfolios of Hedge funds
(Any HF investors or FoHF in the audience?)
3 Content
• Unregulated private placements
– (e.g.) A pooled investment vehicle that is privately organised,
administered by professional investment managers, and not
widely available to the public
• “Extralegality” (de Soto) => Frontier Creativity
– Less restrictive liquidity, borrowing, derivatives … (taxation)
– Creative investment strategies – efficient capital utilisation
– Perpetual innovation inefficiencies
• Consider only hedged (off-shore) funds
4 Hedged Funds
• The most dynamic sector of asset management today
– Decreasing sell side research coverage; Higher servicing profitability
• Regulators “lagging”
– SEC: May 14/15 – “raising bar”?
• Sustained growth
– Highly creative and talented manager’s end game: “personal”
styles
– Owner/Manager mentality
– Self-Regulation by adapting capacity, liquidity, fees
5 Industry
Estimated Assets Asset Flows
Estimated Hedge Fund Asset Growth and Flow 1990 - 2002
Estimated Number of Hedge Funds (ex FOF) 1990 - 2002
6
$38,910$58,370
$95,720
$167,790 $167,360$185,750
$256,720
$367,560 $374,770
$456,430$487,580
$536,060
$622,304
$27,861
$36,918 -$1,141 $14,698
$57,407
$91,431 $4,406
$54,847$20,353
$46,544
$99,436
$8,463
$0
$100,000
$200,000
$300,000
$400,000
$500,000
$600,000
$700,000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Ass
ets
(In
$
MM
)
530694
937
1,277
1,654
2,006
2,3922,564
2,848
3,1023,335
3,904
4,598
0
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
4,500
5,000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Numb
er of
Fund
sN
um
ber
of
Fun
ds
2003
• Tass Asset Flows Report™ 4Q2002 3493 total -1337 “dead”=
2156 “live” funds
• HFR 2002 Industry Report: 4598 funds (exc. FoF)
(AUM most probably underestimated)
7 Hedge Fund Environment
BillionsUSD
Tass HFR
2001 $261 $536
2002 $310 $622
• Contra:– HF are “alternative investment strategies”: too
heterogeneous, dynamic, evolving, with no brands• Pro:
– Absolute returns paradigm, Ineichen (2002) • Specific liquidity (“mark-to-market”) and
drawdown preferences– Very different sources of α, uncorrelated, –ve β,
better Ω … ran by non-consensus thinkers in small enterprises
Another Asset Class?
8
• Hedge Fund (HF) “Indexes”– Composites of actively managed portfolio returns– Over a dozen commercial indices– Investible? Transparent? Capacity? – No independent verification– Enforcing “relative” rather than “absolute” return
viewpoint• Evolving strategies
– E.g. Quantitative credit arb, macro equilibrium models– Many styles within strategy (inc. different fund of funds
styles)– “Strategy drift” detection
9 Investment Strategies
Equity Hedge38.52%
Emerging Markets (Total) 5.53%
Distressed Securities2.74%
Equity Market Neutral5.94%
Equity Non-Hedge7.84%
Event-Driven 7.28%
FI: Arbitrage 2.78%
FI: Convertible Bonds0.25%
FI: Diversified 2.56%
FI: High Yield 1.14%
FI: MBS 1.37%
Convertible Arbitrage3.70%
Short Selling0.76%Sector (Total) 6.71%
Macro 2.15%
Market Timing 3.65%
Merger Arbitrage 3.78%
Regulation D 0.52%
Relative Value Arbitrage 2.77%
Merger Arbitrage 0.60% Fixed Income (Total) 3.24%
Event Driven 3.84%
Relative Value Arbitrage 10.08%
Sector (Total) 0.24%
Macro71.07%
Equity Non-Hedge 0.60%
Equity Market Neutral 1.68%
Equity Hedge 5.28%Short Selling 0.12%
Convertible Arbitrage 0.48%
Distressed Securities 2.40% Emerging Markets (Total)
0.36%
10 Estimated Strategy Composition by AUM 1990
Estimated Strategy Composition by # of Funds (ex FOF) 2002
2003
Assets (in $MM)
Convertible Arbitrage
Distressed Securities
Emerging Markets (Total)
Equity Hedge
Equity Market Neutral
Event-Driven
Fixed Income: Arbitrage
Macro
Merger Arbitrage
Regulation D
Sector (Total)
Short Selling
Fund of Funds
RVA
FI: Convertible Bonds
FI: Mortgage-Backed
Fixed Income: High Yield
Market Timing
Equity Market Neutral: StatArb
Fixed Income: Diversified
Equity Non-Hedge
($60,000) ($40,000) ($20,000) $0 $20,000 $40,000 $60,000 $80,000 $100,000 $120,000
Convertible Arbitrage
Emerging Markets (Total)
Equity Non-Hedge
Event-Driven
Fixed Income: Arbitrage
Fixed Income: Diversified
Fixed Income: High Yield
Market Timing
Merger Arbitrage
Regulation D
Equity Hedge
Equity Market Neutral
Short Selling
Equity Market Neutral: Stat Arb
RVA
Fixed Income: MBS
FI: Convertible Bonds
Distressed Securities
Sector (Total)
Macro
($6,000) ($3,000) $0 $3,000 $6,000 $9,000 $12,000 $15,000
Assets (in $MM)
Fund of Funds
11 Estimated Net Asset Flow by Strategy 2002
Estimated Net Asset Flow by Strategy Q4
2002
2003
Convertible Arbitrage
Distressed Securities Emerging Markets (Total)
Equity Non-Hedge
FI: Diversified
FI: Mortgage-Backed
Fund Weighted Comp. Index
Reg. D
Relative Value Arb
Sector (Total)
Short Selling
Equity Hedge
Equity Market Neutral
Event Driven
FI: Arbitrage
FI: Convertible Bonds
FI: High Yield
Fund of Funds
Lehman Gov/Credit
Macro
Market Timing
Merger Arbitrage
S&P 500
Statistical Arbitrage
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
25%
0% 3% 6% 9% 12% 15% 18% 21%
STD (%)
Retur
n (%
)
Convertible Arbitrage
Distressed Securities
Emerging Markets (Total)
Equity Non-Hedge
Fund Weighted Comp. Index
Reg. D
Sector (Total)
Short Selling
Equity Hedge
Equity Market Neutral Event Driven
FI: Arbitrage
FI: Convertible Bonds
FI: Diversified
FI: High Yield
FI: Mortgage-Backed
Fund of Funds
Lehman Gov/Credit
Macro
Merger ArbitrageRelative Value Arb
Statistical Arbitrage
0%
3%
6%
9%
12%
0% 5% 10% 15% 20% 25% 30%
STD (%)
Retur
n (%)
12 2002 HFRI Index Risk Return Comparison 5 Year Annualised (1998 – 2002)
2002 HFRI Index Risk Return Comparison
2003
AIMA Strategy Definitions
• An index family for every commercial data source: too many indices but a lack of definitions
• Ad-hoc committee under the under the auspices of AIMA called for “Expressions of interest” in April 2003
• ‘Non-commercial’, coordinated long-term research effort leading to the development of a set of definition "guidelines"
• Survey planned during 3Q03
13
• How? – “DIY”, advisor, specialist?– “Fund of funds” (FoHF) route
• Passive: Indexed– Pools of managed accounts– Which “index” and “HF Tracking error”?
• Active: Portfolio of funds– “Off the shelf”– Tailor made and managed
• Structured – What type of security do you own? – Total costs?
14 Creating Exposure
• Two hedge funds• A Hedge fund Index, S&P 500-hedged• Selection of a dozen funds from “platform”,
wrapped• Five funds, 8 x levered portfolio• Single-strategy, multi-manager (levered)• Any including a fund that rebates 50% of fee
to anyone
15FoHF Examples
• Business rather than investment management:– Seeding, incubation, equity stakes
– Capacity marketing, fees splits – Selection vintage year
• Asset gatherers: – Collecting fees on gross assets?– Layered fees transparency (e.g. structured
products)– 2nd level Performance fee– Hurdle, Highwatermark
16Investment Biases
• Collection of HF accounts – a trivial solution?• Portfolio construction biases
– “Products” or portfolios? – Captive market?– Can “good” funds be included?– Where is manager self-invested?
• Should “on going” Due Diligence be outsourced?
17 Managed Account “Platforms”
• Data: not liquid market prices but performance estimates of “hyperactive” portfolios skilfully managed in different, very personal styles
• Problematic valuation: IAFE Hedge Fund Valuation Practice recommendations
• Hedge fund strategy modelling– Multifactor models: R2 from 0.1 to 0.9?– Option replication (Naik and Agrawal, 2001)– Calibration: NAV (RiskData) or model exposure
data
18Data and Modelling
• No unique answer– “Those people who need it will find managers who will
provide it”– “Those managers who won’t give it will be able to find
investors who don’t need it”– Greatest fear: hedge fund ruin (default)– Aggregated disclosure– Mutual trust: the “agent” in real-time dialogue
• Full Transparency Paradox– Un-actionable without active overlays– Diminishing need for managers if operating “active”
overlay?
19Transparency Debate
Fund ExposureHigh Low Average Close
Long Exposure %Short Exposure %Net Futures %Net options %Gross %Net %Cash %
Month end - Industry Sector/Asset type/Credit ExposureTop 5 Sectors Long Short Gross Net
12345
Sum
Month end - Country ExposureTop 5 Countries Long Short Gross Net
12345
Long/Short Equity Report Template
Concentration at month endLong portfolio top 5 Short Portfolio top 5Names % by Value % by Value
1 12 23 34 45 5
Sum Sum#Long #Short Total number of positions
Long Beta Short Beta
Perfomance Attribution - monthMonth Year to date
Long Gross (Y/N) ?Short Net (Y/N) ?Futures/OptionsCurrency
Intra month variation Fund Equity at month endHigh Low ($m) Net change ($m)
Daily NAV Equity
20Hedge Fund Exposures
Source: Fauchier
0%
25%
50%
75%
100%
Long / Short Sector/Asset Country Top 10 #positions Attribution stdev(NAV)
Reported Not Reported n/a
21 Transparency Compliance(2002)
Source: Fauchier
Estimation Risk
• Taboo topic: non-asymptotical statistics, very short and noisy data samples
• Volatility and VaR – Figlewski (2003)
• Portfolio - Kempf (2002)The equal weighting is theoretically optimal solution
when data and forecasts are not reliable
22
• Estimate correlation: n=12 data points: “ρ=0” ↔ ρ∊[-0.3, 0.3] (85%)
“secretary problem” - but fund may be already closed
-0.7 -0.2 0.3 0.8Estimated Correlation
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
Correlation Confidence Intervals
95% confidence level
Co
rrela
tion
Co
nfide
nce
Inte
rva
l
corr=-0.7 corr=-0.2 corr=0.3 corr=0.8
+/-0.0
+/-0.1
+/-0.2
+/-0.3
+/-0.4
+/-0.5
Confid
ence
In
terv
al
Correlation Confidence Intervals
number of samples
95% confidence level
n=25
n=50
# months
Es
tim
ate
d c
orre
lati
on
0 20 40 60 80 100 120
-1.0
-0.5
0.0
0.5
Estimated Correlations for Zero Correlation Data
23 Small sample bias
24Correlation Matrix1 April 2001 to 31 March 2003
Strategy ID M M M ELB ELB ELB EHH EHH EHH EHL EHL ESB SC END END MS MS MS MS FRI
Strategy ID Fund ID FPAM 1 869 342 1079 91 628 1481 912 1102 240 951 267 970 1019 1385 354 54 293 639 226 1011
M 869 -0.02 1.00 -0.14 0.45 0.21 0.01 -0.30 0.32 -0.16 -0.18 -0.39 0.35 0.70 -0.45 0.10 0.48 -0.51 -0.42 -0.31 -0.62 -0.34
M 342 0.19 -0.14 1.00 0.39 -0.20 -0.09 0.29 -0.07 -0.07 0.11 0.17 -0.12 -0.21 0.31 0.17 -0.06 0.38 0.16 0.16 0.27 0.14
M 1079 0.14 0.45 0.39 1.00 0.26 0.24 0.03 0.27 -0.20 -0.16 0.01 0.19 0.44 -0.18 0.20 0.32 -0.24 -0.28 -0.03 -0.21 0.13ELB 91 0.43 0.21 -0.20 0.26 1.00 0.42 -0.09 0.57 0.23 0.45 0.09 0.37 0.49 -0.12 0.08 0.54 -0.40 -0.13 -0.38 -0.13 -0.20ELB 628 0.55 0.01 -0.09 0.24 0.42 1.00 0.18 0.12 -0.01 0.40 0.60 0.47 0.02 0.30 0.67 0.14 0.05 0.20 0.14 0.27 0.14
ELB 1481 0.25 -0.30 0.29 0.03 -0.09 0.18 1.00 -0.44 -0.04 0.07 0.41 0.21 -0.39 0.14 0.20 -0.45 0.18 -0.08 0.25 0.17 0.33
EHH 912 0.25 0.32 -0.07 0.27 0.57 0.12 -0.44 1.00 -0.00 0.20 -0.21 0.08 0.74 -0.17 -0.05 0.77 -0.33 0.07 -0.51 -0.13 -0.47
EHH 1102 0.13 -0.16 -0.07 -0.20 0.23 -0.01 -0.04 -0.00 1.00 0.33 0.19 -0.12 -0.03 0.15 -0.04 0.19 0.05 0.26 -0.29 0.12 -0.29
EHH 240 0.60 -0.18 0.11 -0.16 0.45 0.40 0.07 0.20 0.33 1.00 0.50 0.29 0.05 0.29 0.36 0.20 0.15 0.33 0.07 0.36 -0.14
EHL 951 0.65 -0.39 0.17 0.01 0.09 0.60 0.41 -0.21 0.19 0.50 1.00 0.49 -0.37 0.52 0.59 -0.19 0.46 0.41 0.40 0.56 0.05
EHL 267 0.57 0.35 -0.12 0.19 0.37 0.47 0.21 0.08 -0.12 0.29 0.49 1.00 0.25 0.07 0.59 0.16 -0.07 -0.14 0.24 0.03 -0.07
ESB 970 0.00 0.70 -0.21 0.44 0.49 0.02 -0.39 0.74 -0.03 0.05 -0.37 0.25 1.00 -0.53 -0.07 0.78 -0.69 -0.25 -0.51 -0.58 -0.48
SC 1019 0.61 -0.45 0.31 -0.18 -0.12 0.30 0.14 -0.17 0.15 0.29 0.52 0.07 -0.53 1.00 0.45 -0.27 0.81 0.63 0.52 0.86 0.20
END 1385 0.67 0.10 0.17 0.20 0.08 0.67 0.20 -0.05 -0.04 0.36 0.59 0.59 -0.07 0.45 1.00 -0.16 0.38 0.34 0.46 0.40 0.21
END 354 0.11 0.48 -0.06 0.32 0.54 0.14 -0.45 0.77 0.19 0.20 -0.19 0.16 0.78 -0.27 -0.16 1.00 -0.53 -0.02 -0.58 -0.28 -0.50
MS 54 0.45 -0.51 0.38 -0.24 -0.40 0.05 0.18 -0.33 0.05 0.15 0.46 -0.07 -0.69 0.81 0.38 -0.53 1.00 0.54 0.62 0.85 0.31
MS 293 0.45 -0.42 0.16 -0.28 -0.13 0.20 -0.08 0.07 0.26 0.33 0.41 -0.14 -0.25 0.63 0.34 -0.02 0.54 1.00 0.21 0.64 -0.05
MS 639 0.24 -0.31 0.16 -0.03 -0.38 0.14 0.25 -0.51 -0.29 0.07 0.40 0.24 -0.51 0.52 0.46 -0.58 0.62 0.21 1.00 0.57 0.43
MS 226 0.58 -0.62 0.27 -0.21 -0.13 0.27 0.17 -0.13 0.12 0.36 0.56 0.03 -0.58 0.86 0.40 -0.28 0.85 0.64 0.57 1.00 0.36
FRI 1011 -0.03 -0.34 0.14 0.13 -0.20 0.14 0.33 -0.47 -0.29 -0.14 0.05 -0.07 -0.48 0.20 0.21 -0.50 0.31 -0.05 0.43 0.36 1.00
Correlation:Average correlation with all funds 0.30 -0.08 0.06 0.07 0.09 0.23 0.04 0.01 0.01 0.18 0.25 0.18 -0.07 0.22 0.27 -0.01 0.15 0.15 0.13 0.22 0.02MSCI The World Index - Gross -0.14 -0.65 0.06 -0.35 -0.52 -0.02 0.33 -0.82 -0.03 -0.13 0.33 -0.20 -0.92 0.38 0.11 -0.84 0.55 0.13 0.58 0.46 0.62SSB WGBI 5+ year sector in USD 0.03 0.56 -0.13 0.32 0.18 0.12 -0.63 0.39 -0.26 0.01 -0.15 0.14 0.52 -0.26 0.09 0.40 -0.21 -0.01 -0.09 -0.30 -0.39
Notes: CorrelationEstimation Error <10% >20% <20% <10%
2. The strategies formerly known as Restructuring (R) and Credit Arbitrage (CA) Positive 0 Negative
have been re-classified as Specialist Credit (SC) and Fixed Income (FI) respectively. Correlation band >0.66 <-0.42 <-0.66
4. A blank cell denotes that no meaningful correlation exists for the period of the report.
1. Assuming 90% confidence level and 10% error monthly data points, correlations greater than +/-0.42 (0.66) are eestimated with less than 20% (10%) error.
3. The correlation matrix is generated from 24 data points.
FPAM 1 CORRELATION MATRIX1 April 2001 to 31 March 2003
The table shows the correlation between;(i) FPAM1 and certain funds contained in FPAM1 (where sufficient data is available)(ii) the funds contained in FPAM1 with each other(iii) FPAM1 and various indices(iv) the funds and various indices.
Source: Fauchier
Weekly vs Monthly Data
ViewDifference between Weekly and Monthly Correlation Values
BoyerAllanPacificCaduceus CanyonCSFB DaedalusDoubleBlackDiamondEgertonFRI Gruss JGDYork LansdowneEuropeanPerryEuropeanPerryPartners Raptor Seminole SRGlobal StAlbans StandardPacific TTAsiaWPG BoyerAllanPacific0 -0.24 -0.21 -0.04 0.11 0.21 -0.34 -0.01 -0.25 -0.10 -0.18 -0.05 -0.26 -0.20 -0.14 -0.27 -0.10 -0.19 -0.34 -0.35Caduceus -0.24 0 -0.27 -0.15 0.26 0.33 -0.11 0.02 -0.35 -0.07 0.06 0.13 -0.06 -0.29 0.02 -0.09 0.05 0.16 -0.34 -0.34Canyon -0.21 -0.27 0 -0.44 -0.09 0.16 -0.24 0.17 -0.08 -0.27 -0.05 -0.13 -0.25 -0.12 -0.04 0.06 -0.14 0.11 -0.04 -0.04CSFB -0.04 -0.15 -0.44 0 -0.15 0.10 -0.31 0.17 -0.23 -0.08 0.16 0.11 -0.33 -0.04 -0.14 0.09 -0.42 0.12 0.05 -0.07Daedalus 0.11 0.26 -0.09 -0.15 0 -0.37 -0.16 -0.51 0.17 -0.16 -0.11 -0.74 -0.45 0.16 0.03 0.33 -0.35 -0.03 -0.21 0.03DoubleBlackDiamond0.21 0.33 0.16 0.10 -0.37 0 0.30 -0.14 0.56 -0.08 0.21 -0.13 -0.06 0.31 0.21 0.10 -0.12 0.14 0.22 0.24Egerton -0.34 -0.11 -0.24 -0.31 -0.16 0.30 0 -0.01 -0.16 -0.13 -0.08 -0.19 -0.29 0.10 -0.02 0.07 -0.10 -0.02 0.02 0.00FRI -0.01 0.02 0.17 0.17 -0.51 -0.14 -0.01 0 0.00 -0.02 -0.04 0.17 0.22 -0.20 0.02 -0.03 -0.25 -0.18 -0.09 -0.30Gruss -0.25 -0.35 -0.08 -0.23 0.17 0.56 -0.16 0.00 0 -0.23 -0.03 0.14 -0.02 -0.09 -0.35 0.04 -0.07 0.19 -0.16 -0.08JGDYork -0.10 -0.07 -0.27 -0.08 -0.16 -0.08 -0.13 -0.02 -0.23 0 -0.05 -0.01 -0.18 0.13 -0.33 0.00 -0.18 0.17 0.17 -0.41LansdowneEuropean-0.18 0.06 -0.05 0.16 -0.11 0.21 -0.08 -0.04 -0.03 -0.05 0 0.05 -0.08 -0.01 -0.17 -0.37 -0.18 -0.19 0.06 -0.51PerryEuropean-0.05 0.13 -0.13 0.11 -0.74 -0.13 -0.19 0.17 0.14 -0.01 0.05 0 -0.12 0.49 0.03 0.08 -0.56 0.18 -0.02 -0.14PerryPartners -0.26 -0.06 -0.25 -0.33 -0.45 -0.06 -0.29 0.22 -0.02 -0.18 -0.08 -0.12 0 0.27 -0.06 0.08 -0.28 0.05 0.05 -0.05Raptor -0.20 -0.29 -0.12 -0.04 0.16 0.31 0.10 -0.20 -0.09 0.13 -0.01 0.49 0.27 0 0.13 -0.20 0.12 -0.12 -0.18 -0.08Seminole -0.14 0.02 -0.04 -0.14 0.03 0.21 -0.02 0.02 -0.35 -0.33 -0.17 0.03 -0.06 0.13 0 -0.15 -0.14 0.19 -0.05 -0.30SRGlobal -0.27 -0.09 0.06 0.09 0.33 0.10 0.07 -0.03 0.04 0.00 -0.37 0.08 0.08 -0.20 -0.15 0 0.33 -0.30 -0.21 -0.34StAlbans -0.10 0.05 -0.14 -0.42 -0.35 -0.12 -0.10 -0.25 -0.07 -0.18 -0.18 -0.56 -0.28 0.12 -0.14 0.33 0 -0.11 0.30 0.14StandardPacific -0.19 0.16 0.11 0.12 -0.03 0.14 -0.02 -0.18 0.19 0.17 -0.19 0.18 0.05 -0.12 0.19 -0.30 -0.11 0 0.04 0.10TTAsia -0.34 -0.34 -0.04 0.05 -0.21 0.22 0.02 -0.09 -0.16 0.17 0.06 -0.02 0.05 -0.18 -0.05 -0.21 0.30 0.04 0 -0.15WPG -0.35 -0.34 -0.04 -0.07 0.03 0.24 0.00 -0.30 -0.08 -0.41 -0.51 -0.14 -0.05 -0.08 -0.30 -0.34 0.14 0.10 -0.15 0
From Feb 2001 to Jan 2003 (24 monthly or 108 weekly data values)
Surprising differences in certain fund correlations pairs
25
Source: Fauchier
Weekly HF “Indexes”
-0.04 -0.02 0.00 0.02 0.04
M
0
10
20
30
-0.03 -0.02 -0.01 0.00 0.01 0.02 0.03
EHL
0
10
20
30
40
50
60
Equally weighted index of weekly returns: non-normality
26
Source: Fauchier
Keating and Shadwick (2002)
27 Omega Ratio
• HF are SME (~7 people => no IT, client service …)– Can portfolio manager run (grow) a small business?– “Disgraceful aging”
• Total Hedge Fund Risk =– Market Risk + Operational Risk– Operational Risk >> Market Risk – Principal/Agency Problem
• Balance “Qualitative and Quantitative” Risks
28Hedge Fund Risk
29 The Real Risk
($1,000)
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
$9,000
$0.0
$10.0
$20.0
$30.0
$40.0
$50.0
$60.0
$70.0
$80.0
• Primary (individual hedge fund level): – Many market risks are (most often) hedged– Balance sheet dynamics: leverage and hedge skills– Kept in check by Prime Broker margin policy
• Secondary (portfolio of funds level):– Risk measurement + portfolio management – Operational risk management
30Risk Management
• Mandatory: Prime brokers– Are VaR and margin policy private information not to
be disclosed (timely) to (all) investors?• Optional: Third party “Risk aggregators”
– HF → TTP → Investor– New generation fund administrators?
• Voluntary: Customised risk reporting– IAFE IRC and AIMA: Strategy-specific templates
31Market Risks
• Age and stability– Immature business models– Incentives, succession planning
• Capacity– “Chicken & Egg” capacity games:
• Day 1 fund closures, secondary market– Big isn’t beautiful: median AUM $40m– “Know your client”: max. two dozen investors
• Liquidity– Lockups, penalties, gates, suspended and forced
redemption rights
32Operational Risks (1)
• Organisational Structure– Legal structure– Performance fee models
• Counterparties– Fund administration, Audit, Prime
Broker• Manager Utility: “Path-Dependant”
– Risk aversion = f ( ΔAUM, Losing streak, YTD, Wealth…)
33Operational Risks (2)
• FoHF A ≠ FoHF B– % own (or owned) funds, %funds of funds,
% multi-strategy funds …
– Liquidity, costs (fee sources)• Portfolio Analysis
– Performance Attribution: Manager selection vs Strategy allocation
– Turnover (usually low), ROCE– Style analysis
• Monitoring
– In-situ: business and operational risk
34Funds of Hedge Funds
• “One size doesn’t fit all”– Single-strategy, multi-manager: mitigate
decision making?– “All weather”
– Tailor-made
• Levered or not?
• “Optimised” or not?• Avoid behavioural biases
35Portfolio Construction
• Kempf (2002): Optimal portfolios for data length T, market inhomogeneity τ, identical prior mean.
Case τ =0 τ→∞
T=0 Minimum variance Equally weighted
T→∞ Minimum variance Two-step Markowitz
• Comment: funds of hedge funds are in T→0/τ→∞
36Portfolio Estimation Risk
0.02
0.03
0.04
0.05
207 515 117 215 309
Distribution of Portf olio Weights
37Portfolio Construction
• Constrained optimisation– Asymmetric calendar trading constraints
(illiquidity)– Inherent slippage
• Not mean-variance but scheduling and constraint programming
• Monitoring Costs: Communication density
– #meetings/funds/year/analyst(s)
38 Operational Risk Optimality
M/M+30/15
March 04Jan 03 March 03 May 03 Sept 03 Nov 03July 03 Jan 04
Jan 03 March 03 May 03 Sept 03 Nov 03July 03 Jan 04 March 04
Jan 03 March 03 May 03 Sept 03 Nov 03July 03 Jan 04 March 04
M/M+60/20
2/Q+60
39 Calendar Liquidity Constraints
Source: Fauchier
40 Manager Research andMonitoring
Total number of meetings
Number of meetings
0
10
20
30
40
50
60
70
80
90
100
Jan-
01
Apr-0
1
Jul-0
1
Oct-0
1
Jan-
02
Apr-0
2
Jul-0
2
Oct-0
2
Per
Mo
nth
0
200
400
600
800
1000
1200
Cu
mu
lati
ve
Managers' Office
Fauchier Partners
Seminars
Source: Fauchier
• Balance true risks and costs– Attention to vested business
interests and incentives (are we all “eating our own cooking”?)
– Quantitative, but also confident
• Product divergence– “Optimal” transparency– Commoditisation vs
customisation
41 Conclusion
• AIMA (2002) A Guide to Fund of Hedge Funds Management and Investment
• AIMA (2003) Hedge Fund Strategy Definition Standardisation
• Inechien, A. (2002) Absolute Returns, Wiley
• L’ Habitant, F.-S. (2002) Hedge Funds: Myths and Limits, Wiley
• Rahl, L. (2003) Hedge Fund Risk Transparency, Risk Books
42 Bibliography - Introduction
• Figlewski, S. (2003) Assessing the Risk in Risk Assessments, IAFE/ PRMIA Seminar, April 23rd, NYC
• Kempf, A., Memmel, C. (2002) On the Estimation of the Global Minimum Variance Portfolio, Discussion Paper 2002-2, Uni. Koeln
• Keating, C., Shadwick, W. (2002) “Omega: A Universal Performance Measure” Journal of Performance Measurement, Spring 2002
• Lo, A. (2002) Risk Management for Hedge Funds: Introduction and Overview, AIMR
• Naik, N., Agrawal, V. (2001) Performance Evaluation of Hedge Funds with Option-based and Buy-and-Hold Strategies, LBS
43 Bibliography - Research