An EDHEC-Risk Institute Publication
An Analysis of the Convergence
between Mainstream and
Alternative Asset Management
February 2013
with the support of
Institute
Table of Contents
1. Executive Summary.............................................................................................. 5
2. Review of Hedge Fund Techniques and how they can be
transported to the Mutual Fund/UCITS Space ............................................... 11
3.Literature Review and our Contribution .......................................................27
4. UCITS Restrictions and Testable Hypotheses................................................31
5.Data and Summary Statistics...........................................................................35
6.Average HF and UHF Performance .................................................................43
7. Conclusion ..........................................................................................................55
References................................................................................................................57
About Newedge.......................................................................................................63
About EDHEC-Risk Institute.................................................................................66
EDHEC-Risk Institute Publications and Position Papers (2010-2013)......... 71
2
Printed in France, February 2013. Copyright© EDHEC 2013
The opinions expressed in this study are those of the author and do not necessarily reflect those of EDHEC Business School.
The author can be contacted at research@edhec-risk.com.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
Foreword
The present document, which represents
the latest publication from the “Advanced
Modelling for Alternative Investments”
research chair at EDHEC Risk Institute,
supported by the Prime Brokerage Group
at Newedge, is part of the chair research
on “Analysing the Convergence between
Mainstream and Alternative Money
Management.”
Over the last few years institutional
investors’ traditional portfolios have
failed to meet their objectives in terms
of risk and performance. Investors have
thus demonstrated a growing interest
in new forms of diversification and
especially in investment vehicles offering
better protection during extreme market
conditions. This has naturally led them
to increasingly consider hedge funds as
part of their investment universe, but
also to search for access to sophisticated
risk management techniques within the
regulated and transparent world of mutual
fund products.
While this convergence of mainstream
long-only asset management and
alternative hedge funds has been much
talked about for some years, it has not
been realised on any significant scale until
recently. Two main forces are currently
accelerating the trend. The first of these
forces, from the supply side, was the
amendment of the UCITS framework, which
allowed mainstream fund managers to
supply regulated forms of hedge fund-type
products to their traditional customer base,
while also permitting hedge funds to reach
out to the same customers.
The second of these forces, from the demand
side, is the increased interest of private but
also, and more importantly, institutional
investors in novel, more sophisticated forms
of mutual fund investing strategies, and
which has led to the rapid development of
regulated products implementing absolute
return strategies and 130/30 strategies.
Such products are often perceived as an
entry door to the alternative industry,
or a middle ground between complex
and opaque hedge fund strategies and
unsophisticated mutual fund strategies
without any substantial added value in
terms of risk management.
It is important to note that the convergence
goes in both directions, as an increasing
number of hedge fund managers are now
offering regulated products as well.
We would particularly like to thank the
authors of this publication, Robert Kosowski
and Juha Joenväärä, for the quality of their
research.
We would also like to thank the Prime
Brokerage Group at Newedge for their
generous support of our research, with
particular thanks to James Skeggs, Duncan
Crawford and Gérard de Lambilly.
Wishing you an agreeable and informative
read,
Noël Amenc
Professor of Finance
Director of EDHEC-Risk Institute
An EDHEC-Risk Institute Publication
3
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
About the Authors
Juha Joenväärä is assistant professor at the University of Oulu. He conducts
research on delegated portfolio management especially hedge funds, empirical
asset pricing, and financial econometrics. In particular, his research focuses
on hedge fund performance predictability. Juha received his master’s degree
in finance with highest honours from University of Oulu in 2005. He earned
his PhD in finance from the University of Oulu and the Graduate School of
Finance in 2010. His PhD dissertation “Essays on Hedge Fund Performance
and Risk” has received an award from the OP-Pohjola Research Foundation.
Since October 2010 Juha is a visiting researcher at Imperial College’s Centre
for Hedge Fund Research. Joenväärä has consulted for several Finnish asset
management companies and pension funds in their asset allocation and
fund selection decisions.
Robert Kosowski is affiliate professor at EDHEC-Risk Institute, associate
professor in the Finance Group of Imperial College Business School, Imperial
College London and Director of the Centre for Hedge Fund Research. Robert’s
research has been published in top peer-reviewed finance journals and
received several awards. Robert holds a BA (First Class Honours) and MA
in Economics from Trinity College, Cambridge University, and a MSc in
Economics and PhD from the London School of Economics. Robert is an
associate member of the Oxford-Man Institute of Quantitative Finance
at Oxford University and a member of AIMA's research committee.
Robert has consulted for private and public sector organisations and has
worked for Goldman Sachs, the Boston Consulting Group and Deutsche Bank.
His policy related advisory work includes: specialist adviser to UK House of
Lords (2009-2010), expert technical consultant (International Monetary
Fund, USA, 2008).
4
An EDHEC-Risk Institute Publication
Executive Summary
An EDHEC-Risk Institute Publication
5
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
Executive Summary
1 - The Undertakings for
Collective Investment in
Transferable Securities,
Directives 2001/107/EC and
2001/108/EC (or "UCITS")
are a set of European Union
Directives that are designed
to allow collective investment
schemes to operate freely
throughout the EU on the
basis of a single authorization
from one member state.
2 - See, for example, http://
www.hedgefundintelligence.
com/UCITS.
3 - UCITS III compliant hedge
fund strategies are sometimes
referred to as or Newcits or
‘absolute’ UCITS.
4 - ‘The Coming of Age of
Alternative UCITS Funds
January 2012’, January 2012,
Pertrac Research Study.
This paper has two objectives. First, this
paper provides an academic analysis of the
main techniques that are currently used
by hedge fund managers and that could
be transported to the mutual fund and
alternative UCITS space in a straightforward
manner so as to provide better forms of risk
management in a regulated environment.
We categorize techniques according to
three groups: (1) risk management, (2)
alpha generation and (3) leverage. Within
the group of risk management techniques
we review (1.1) techniques based on
derivatives, (1.2) techniques based on
dynamic trading strategies, (1.3) volatility
scaling of positions, (1.4) risk measurement
techniques, (1.5) currency overlay and (1.6)
performance-enhancing compensation
and incentive structures. In the context
of alpha creation techniques, we discuss
(2.1) advanced econometric techniques
for asset allocation, (2.2) asset-specific
betas and stock-picking, (2.3) shareholder
activism, (2.4) order execution alpha and
(2.5) currency alpha. Finally, we show how
leverage can act as a performance driver
and distinguish (3.1) financial leverage,
(3.2) construction leverage, (3.3) instrument
leverage, (3.4) risk-parity techniques and
(3.5) VaR techniques and leverage.
Alternative investment fund managers
are increasingly deciding to implement
alternative strategies through traditional
investment vehicles such as mutual funds
in order to access assets from retail and
institutional investors that, for various
reasons (such as investment mandates,
for example), cannot invest through less
regulated structures. Packaging hedge
fund strategies in a traditional format
is not straightforward and it raises a
lot of challenges for the managers as
6
An EDHEC-Risk Institute Publication
well as for the brand of the regulatory
format. An important question is to
know whether structuring hedge fund
strategies through mutual funds will
compromise these strategies and provide
the same level of returns, considering the
constraints under mutual fund regulations
such as investment restrictions, liquidity
requirements, operational requirements
and risk management. We therefore
carefully examine how dynamic trading and
derivatives strategies can be transported to
the mutual fund space.
Our second objective is to examine the
convergence between the mainstream and
the alternative asset management industry
by studying UCITS and non-UCITS hedge
funds.1 The latest amendment of the UCITS
framework, referred to as UCITS III and
IV, allows mainstream fund managers to
supply regulated forms of hedge fund-type
products to their traditional customer
base, while also permitting hedge funds
to reach out to the same customers.2 We
refer to the latter as UCITS hedge funds
(UHFs) to distinguish them from traditional
non-UCITS hedge funds (HFs).3 A recent
Pertrac study found that alternative UCITS
Assets under Management (AuM) peaked at
€178.82 billion in May 2011.4 Alternative
UCITS or UCITS hedge funds are funds
that follow a hedge fund type strategy
aiming to generate absolute return or
absolute performance. They are, in other
words, simply UCITS that take advantage of
certain investment techniques allowed by
the UCITS regulations which enable them
to pursue strategies that were previously
more common in the alternative investment
sector – in particular, the hedge fund sector.
Based on regulatory requirements that
apply to UCITS, we economically motivate
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
Executive Summary
5 - See, for example, ESMA’s
guidelines on ETFs and other
UCITS issues, available at
http://www.esma.europa.eu/
system/files/2012-44_0.pdf.
a range of hypotheses regarding differences
in AuM growth, performance and risk
between UHFs and HFs and empirically test
them using one of the most comprehensive
hedge fund databases constructed to
date. We examine differences between
UHFs and HFs based on a range of crosssectional fund features such as investment
objectives and other fund characteristics
including compensation and redemption
structures. Regulatory requirements that
apply to UCITS imply that UHFs impose less
binding share restrictions than HFs. Hence,
UHF investors can exploit performance
persistence, if any, more easily than HF
investors. Our paper sheds light on the
convergence of mainstream and alternative
investment management as well as drivers
of performance and risk for different types
of UCITS funds. This study is timely since
UCITS funds, and in particular the so-called
retailization of complex products and the
use of total return swaps, recently attracted
the attention of regulators in 2012.5
UCITS funds differ from hedge funds
in several ways which leads to testable
hypotheses about differences in their
performance and risk. First, the requirement
of a (i) separate risk management function
in UCITS funds as well as (ii) leverage limits
and (iii) VaR (Value-at-Risk) limits leads
to our first hypothesis that the risk of
UHFs is lower than that of HFs. Measuring
risk is a complex issue and therefore we
apply a range of different risk metrics to
capture tail-risk in addition to volatility
(Patton (2009)). Second, UCITS funds face
restrictions regarding the use of derivatives.
This leads to two further hypotheses. Our
second hypothesis is that restrictions in
the use of derivatives reduce option-like
payoff profiles and non-normal returns
in UHF return distributions. Our third
hypothesis is that reduced flexibility in
the use of derivatives makes UHF returns
less counter-cyclical than those of HFs. A
fourth hypothesis is that the investment
objective is crucial and that the extent to
which UCITS restrictions affect risk and
performance depends on the investment
objective of the fund. We therefore carry
out our hypotheses tests for all funds
as well as by investment objective to
distinguish, for example, Long/Short Equity
funds, Global Macro funds as well as Event
Driven funds. Previous studies of UHFs
have typically focused on a smaller set of
investment objectives (Darolles (2011)). Our
fifth hypothesis is related to the fact that
different countries have implemented the
UCITS directive in different ways, which
implies that geography and in particular
domicile matters for UHFs.
One of the main strengths of our paper is
that we examine the relationship between
performance and risk of HFs and UHFs and
a range of economically important fund
characteristics related to fund manager
incentives and fund liquidity which have not
been previously examined in the context of
UHFs. Liquidity is linked to fund performance
in at least two important ways. First,
liquidity, in terms of less binding redemption
restrictions for UHFs investors, may allow
them exploit performance persistence.
On the one hand, Joenväärä, Kosowski
and Tolonen (2012, hereafter JKT (2012))
show that HFs performance may
hypothetically persist but investors’
ability to exploit it is limited by strict
share restrictions. Hence, the UHF universe
provides an interesting setting to test
whether performance persists and
whether it can be exploited in practice.
An EDHEC-Risk Institute Publication
7
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
Executive Summary
On the other hand, Teo (2011) provides
evidence that capital outflows can be costly
if HFs are exposed to liquidity risk. This
suggests that liquidity may be harmful in
certain circumstances. Thus, it is interesting
to study the role of share restrictions and
liquidity risk for UHFs.
6 - See, Güner, Rachev,
Edelman, and Fabozzi (2010).
The AIS database includes
funds that UBS allocated
capital to, but that do
not report to commercial
databases.
Moreover, in terms of the time-series and
number of funds in our data, this paper
is to our knowledge one of the most
comprehensive analyzes of the performance
and risks of HFs and UHFs. Previous studies
have typically analyzed UHFs in isolation
without comparing them to the HF universe
or they have examined at most 460 UHFs
and less than 2800 HFs. First, we carry out
a comprehensive aggregation process to
construct an aggregate HF dataset that
consists of more than 24,000 unique
hedge funds that report at least 12 return
observations. This database consists of
active and inactive or defunct funds. This
group of funds represents our HF control
group. The number of hedge funds in our
database is close to that reported by the
UBS’ proprietary AIS database consisting
of about 20,000 hedge funds and 45,000
share classes6, while the PerTrac 2010 hedge
fund database study finds that the hedge
fund industry contains about 23,600 funds.
Therefore, we believe that our aggregate
database containing the union of five major
databases is close to the true unobservable
population of hedge funds.
Second, by merging data on UCITS funds
from the EurekaHedge, BarclayHedge, and
HFR databases on UCITS hedge funds we
also construct an aggregate database on
UHFs. In our study we thus carry out a
comprehensive analysis and comparison of
both UHFs and HFs. Hedge fund databases
8
An EDHEC-Risk Institute Publication
are also non-overlapping - we find that
almost 70% of funds in our consolidated
database report only to one of the used
major databases. JKT (2012) recently
documented that data biases are different
between hedge fund databases, thus
affecting stylized facts about performance
and risk of HFs. This suggests that merging
several databases will provide us with a
more accurate view of the aggregate size
of the UCITS hedge fund universe.
We document stylized facts about hedge
fund performance, data biases, and
fund-specific characteristics explaining
cross-sectional differences in UHF and
HF performance. To understand why the
performance results differ between UHFs
and HFs, we start by highlighting how the
total return and AuM time-series differ on
a value and equal-weight basis between
UHFs and HFs. We then move to differences
in fees and risk-adjusted performance and
study how Sharpe ratios, the Fung and Hsieh
(2004) seven-factor model alphas and risk
exposures differ between HFs and UHFs.
Finally, we examine the cross-sectional
relationship between fund characteristics
and hedge fund performance. The existing
literature on HFs has documented that
managerial incentives, share restrictions
and capacity constraints are associated
with cross-sectional differences in hedge
fund performance. Using portfolio sorts and
the Fama and MacBeth (1973) regressions,
JKT (2012) demonstrate that smaller and
younger funds, and funds with greater
capital flows deliver better future returns
than their peers. This conclusion is in line
with the previous literature (e.g., Teo, (2010)
and Aggarwal and Jorion (2010)). In contrast
to the existing literature, JKT (2012) find,
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
Executive Summary
however, that fund characteristics related
to managerial discretion or illiquidity do
not consistently explain hedge fund crosssectional returns. In fact, they find very little
evidence that share restrictions in the form
of lockup, notice and redemption periods
are related to higher risk-adjusted returns
when they control for the role of other
characteristics in multivariate regression.
Given regulatory requirements on the
liquidity of UHFs it is therefore of particular
interest to study the relationship between
UHF performance and fund liquidity.
hedge fund performance and cross-sectional
characteristics. Section 7 concludes.
In our empirical results, we find that
UHFs underperform HFs on a total and
risk-adjusted basis. However, UHFs have
more favourable liquidity terms and when
we compare liquidity matched groups of
UHFs and HFs, we find that UHFs generate
similar performance. Thus we uncovered an
important liquidity-performance trade-off
in the sample of UHFs. Our results also show
that HFs have generally lower volatility
and tail risk than UHFs, which is consistent
with hurdles to the transportation of risk
management techniques discussed. Finally
we find important domicile effects related
to firm and fund performance.
The paper is structured as follows. Section
2 reviews techniques used by hedge funds
and how they can be transported to the
mutual fund/UCITS space. Section 3 reviews
the related literature and puts our study in
context. Section 4 reviews the regulatory
restrictions imposed on UCITS funds and
motivates the resulting testable hypotheses.
Section 5 describes the HF and UHF data
and methodology. Section 6 summarizes
the empirical results on differences in
performance and risk between HFs and
UHFs. It also describes stylized facts about
An EDHEC-Risk Institute Publication
9
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
Executive Summary
10
An EDHEC-Risk Institute Publication
2. Review of Hedge Fund
Techniques and how they can
be transported to the Mutual
Fund/UCITS Space
An EDHEC-Risk Institute Publication
11
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
7 - Equation 1 in Section
5.3 shows one of the risk
decomposition that is most
common in the academic
hedge fund literature.
8 - See, for example,
Buraschi, Kosowski and
Sritrakul (2012).
9 - See for example Kraus and
Litzenberger (1976).
Hedge funds use a diverse set of techniques
to improve performance and reduce
volatility. Hedge funds have investment
mandates that differ from those of
traditional long-only managers. Hedge
funds have an absolute return objective,
i.e. achieving returns uncorrelated with
the market (Ineichen (2002)). Absolute
return managers therefore have different
incentives regarding risk management. The
portfolios of most long-only mutual fund
managers closely track the benchmark
(with significant risk or beta exposure)
and, due to their investment mandate,
they are mainly concerned about tracking
error or active risk relative to a benchmark.
When absolute return managers speak
of risk, they normally mean total risk
and its mandate is generating positive
returns in excess of a hurdle such as
LIBOR, for example. The absolute return
objective implies that risk reduction
techniques such as long-short strategies
and derivatives positions are used to
reduce benchmark exposures. Despite the
fact that a hedge fund’s objective is to
generate absolute returns, for purposes
of performance and risk attribution it is
instructive to decompose hedge fund
returns into risk exposures (beta) and
risk-adjusted exposure (alpha).7 It is well
known8 that leverage can be used to scale
and increase an existing alpha in standard
performance regressions and therefore it
is helpful to examine leverage separately
as one of several potential performance
drivers.
Given the large number of techniques
it is useful to categorize the techniques
into three main groups and several
subgroups (see Box 1 below). The three
main groups that we use are (1) risk
12
An EDHEC-Risk Institute Publication
management techniques, (2) alpha
creation techniques and (3) techniques
related to leverage. Of course, some
techniques may fall into more than one
category. Risk management techniques
which reduce beta exposures may not
only change a fund’s return profile, for
example, but may also increase the alpha
of a fund relative to a given benchmark.
Nevertheless, we believe that our choice
of classification scheme facilitates the
explanation of the mechanism underlying
different techniques and their impact on
fund performance and risk.
2.1 Risk Management Techniques
Risk management techniques encompass
methods to reduce risk exposures and
change the return distribution in such
a way as to reduce the probability of
(extreme) losses. Risk can be viewed from
two perspectives.
On the one hand, the distribution of
returns can be described statistically
using moments such as the mean, the
volatility (standard deviation), skewness
and kurtosis (lower probability of tail
outcomes in general). Theoretically it
can be shown that many common utility
functions imply a preference for higher
mean and skewness and lower volatility
and kurtosis.9 In practice, many investors
prefer funds with a higher mean return,
lower volatility and lower drawdown.
Drawdown is defined as the money lost
since reaching the most recent high
water mark. One of the limitations of
using the four statistical moments to
select funds is that drawdown is often
poorly approximated by skewness and
kurtosis (Burghardt and Liu (2012)). The
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
Box 1. This box categorizes techniques that hedge funds use. The third column provides an assessment of whether this particular
technique is transferable to UCITS funds.
Technique
1. Risk Management techniques
(reduction of risk exposures and
change in return distribution)
2. Alpha Creation Techniques
Sub-group
Transferable to UCITS
1.1.Techniques to change the fund return distribution by means
of derivatives (futures, forwards, options, swaps and other
derivatives)
Partly
1.2. Techniques to change the fund return distribution by means
of dynamic (option-like) trading strategies (portfolio insurance)
Partly
1.3. Volatility scaling of positions
Yes
1.4. Risk measurement techniques (VaR, EVT, advanced risk
measures)
Yes
1.5. Currency overlay
Yes
1.6. Performance-enhancing compensation and incentive
structures (high water marks, performance fees, clawback)
Partly
2.1. Advanced econometric and forecasting techniques for
Global Tactical Asset Allocation
Yes
2.2 Asset-specific bets and stock-picking
Yes
2.3 Shareholder activism
Yes
2.4 Order Execution Alpha
Partly
2.5 Currency Alpha
3. Leverage as performance
driver
Yes
3.1 Financial Leverage (borrowing leverage and /or notional
leverage; prime broker funding)
Partly
3.2 Construction leverage (Shorting as a source of Leverage)
Partly
3.3 Instrument leverage
Partly
3.4 Risk-parity techniques
Yes
3.5 VaR techniques and leverage
Yes
reason is that drawdown is affected
by autocorrelation and that it is a
multiperiod concept in contrast to the
statistical distribution. On the other hand,
the distribution of returns can be traced
back to underlying risk exposures and risk
factors such as equity, interest rate, credit,
currency and volatility risk.
Risk management techniques also include
risk measurement tools which are used to
monitor risk and inform risk management
decisions. We further distinguish
the following five sub-groups of risk
management techniques: (i) techniques to
change the fund return payoff distribution
by means of derivatives and tail risk
hedges, (ii) techniques to change the fund
return payoff distribution by means of
dynamic (option-like) trading strategies,
(iii) risk measurement techniques, (iv)
currency overlay and (v) performance
enhancing compensation structures and
incentives (performance fees, clawbacks,
staggered position reduction).
(i) Techniques to change the fund
return distribution by means of
derivatives (options, swaps, futures
and other derivatives)
Futures and forwards are the simplest
derivatives that hedge funds can use
to manage risk. A futures contract is
a standardized contract between two
parties to buy or sell a specified asset
of standardized quantity and quality
for a price agreed today. The difference
between forward contracts and futures
An EDHEC-Risk Institute Publication
13
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
contracts is that forward contracts are
not standardized and not traded on the
exchange (which leads to different margin
and capital requirements). Futures and
forward contracts exist globally for several
hundred assets such as equities, interest
rates, currencies and commodities.
10 - Interest rate swaps
pay periodic interest, while
variance swaps, for example,
do not.
11 - CFDs are currently not
permitted in the United
States, due to restrictions
by the U.S. SEC on OTC
derivatives. Until 2007
CFDs were OTC contracts,
but in November 2007
the Australian Securities
Exchange listed exchangetraded CFDs on several
Australian stocks, FX pairs as
well as other global indices
and commodities.
Another highly popular type of derivative
is the swap. A swap is a derivative in which
counterparties exchange cash flows of
one party's financial instrument for those
of the other party's financial instrument.
In an interest rate swap, for example, two
parties agree to exchange interest rate
cash flows, based on a specified notional
amount from a fixed rate to a floating rate
(or vice versa) or from one floating rate
to another. Swaps are commonly used for
both hedging and speculating. Swaps exist
on interest rates, currencies, commodities,
variance, correlation, longevity indices,
equities as well as credit default events.10
Some swaps, such as interest rate swaps,
are relatively liquid while others such as
correlation swaps are not. Total return
swaps (TRS) have been used by UCITS fund
structures to implement hedge fund like
strategies. In a total return swap, party
A pays the total return of an asset and
party B makes periodic interest payments.
Globally there is a movement to move
certain swap trading from OTC markets to
central clearing and exchanges.
In addition to forwards and futures,
hedge funds can use contracts for
differences (CFDs) in some countries such
as the United Kingdom, Hong Kong, the
Netherlands, Poland, Portugal, Germany,
Switzerland, Italy, Singapore, South
Africa, Australia, Canada, New Zealand,
Sweden, Norway, France, Ireland, Japan
14
An EDHEC-Risk Institute Publication
and Spain.11 A CFD is a contract between
two parties that agree that the seller will
pay the buyer the difference between the
current value of an asset and its value at
contract time. CFDs emerged in the 1990s
in the UK where they were used by hedge
funds and institutional investors as a cost
effective way to hedge equity exposure
since they involved only small margin
payments and avoided UK stamp duty tax.
Similar to futures, CFDs also allow trading
on margin which we discuss in one of the
upcoming subsections in the context of
leverage as a performance driver.
Similar to futures contracts, options
exist on a wide range of underlyings
including equity indices, individual stocks,
interest rates, futures, swaps (“swaption”),
commodities, currencies and volatility
indices. Options have asymmetric return
profiles. A long put position on an equity
index, for example, can be used by a fund
to buy protection against large losses. The
put option premium will reduce the mean
return of a fund but is likely to reduce its
volatility and increase its skewness (and
thus reduce its tail risk). This is an example
of a risk return trade-off. The trade-off
between mean return and volatility in
financial markets is well known. In the last
10 years more evidence has accumulated
of a trade-off between the mean return as
well as skewness and kurtosis (Harvey and
Siddique (2000)).
Following the financial crisis, practitioners
including investors and hedge fund
managers have become increasingly
interested in what are loosely defined as
‘tail-risk hedges’ or ‘tail hedge funds’.12
These vehicles are used to reduce tail risk
by means of different derivatives or option
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
like techniques. As the simple option
example above illustrated, a tail risk hedge,
like any type of insurance, comes at a cost.
One way to reduce the cost of the hedge
is to implement it conditionally and vary
the type of derivative used to implement
it depending on where insurance is
cheapest. Therefore, the challenge for ‘tail
risk funds’ is to provide a given tail risk
hedge (against a particular risk factor)
at the cheapest possible costs. Different
derivatives can be used to reduce extreme
losses for a premium. These include put
options, but also credit default swaps and
variance or volatility swaps. Although the
area of higher order moments and tail risk
hedges has been studied in the academic
literature for decades, the concept of tail
risk was popularized significantly by Taleb
(2007, 2008) who introduced such catchy
labels as the ‘The Black Swan’ to explain
the underpinnings of tail events and their
impact on performance. Many hedge
funds occasionally or regularly implement
tail-risk hedges in their portfolios. Some
tail hedge funds however claim to
specialize in providing tail risk insurance.
Tail hedge funds rose to prominence in
2008 when some of them delivered tripledigit returns for investors. Their success
led to copycat funds that similarly aim
to profit from rare extreme events. The
first tail-protected ETF was launched in
May 2012 and is traded on the TSX via
Horizons ETFs.
potentially high cost in terms of average
absolute returns as well as risk-adjusted
performance. There is evidence that some
hedge funds buy tail risk insurance, while
other hedge funds implicitly or explicitly
sell tail risk insurance. In a recent paper,
Kelly and Jiang (2012) document large
persistent exposures of hedge funds to
downside tail risk. Funds that lose value
during high tail risk episodes earn average
annual returns more than 6% higher than
funds that are tail risk-hedged, controlling
for commonly used hedge fund factors.
Buraschi, Kosowski and Trojani (2012)
further shed light on the drivers of tail
risk by highlighting the role of correlation
risk in driving drawdowns for longshort hedge funds, merger arbitrage
funds, option trader funds and other
funds that are susceptible to large losses
when correlations unexpectedly change.
These results are consistent with the
notion that a significant component
of hedge fund returns can be viewed
as compensation for selling disaster
insurance. Therefore, a fund that is buying
(selling) tail risk insurance is likely to have
lower (higher) returns, lower (higher)
volatility and higher (lower) skewness.
Several derivatives can be used to change
the payoff distribution by changing
tail risk exposure and Box 2 provides an
overview of different derivatives types
and their effect on tail risk.
Although tail hedge funds are gaining
in popularity, the economic literature
provides a sobering perspective on the
potential returns that can be expected
from them due to evidence of a return /
tail-risk trade-off in hedge funds. Investors
may find that reducing tail risk comes at a
An EDHEC-Risk Institute Publication
15
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
Box 2. Derivatives and tail risk. This table shows a range of derivatives position examples and the impact on tail risk.
Type of Derivative Position
1. (Equity) Put Option
2. Credit Default Swap
3. Variance Swap (on equity index, but potentially
also bond index or currency)
4. Correlation Swap (on equity index)
13 - A CDS contract is binary
in the sense that it pays
out in case of default. The
question of when a default
event is declared is governed
by the ISDA protocol and a
sovereign CDS may not be
an effective tail hedge if a
government takes steps to
avoid a default event being
declared.
14 - See, for example, EU
Regulation No 236/2012 of
the European Parliament
and of the of 14 March 2012
on short selling and certain
aspects of credit default
swaps.
15 - Strictly speaking the
theoretically possible perfect
dynamic replicating strategy
does not exist in practice.
Attempts to dynamically
hedge derivatives can we
viewed as approximations
(Derman and Taleb (2005)).
16
An EDHEC-Risk Institute Publication
Position
Effect on Tail Risk
Long (agree to sell asset at strike price K)
Reduction
Short (agree to sell asset at strike price K)
Increase
Long (buy insurance against credit event)
Reduction
Short (sell insurance against credit event)
Increase
Long (pay implied, receive realized variance)
Reduction
Short (receive implied, pay realized variance)
Increase
Long (pay implied, receive realized correlation)
Reduction
Short (pay implied, receive realized correlation)
Increase
Variance and correlation swaps are traded
over-the-counter (OTC) and are less liquid
than most put options or CDS index
contracts.13 The relative price of tail risk
insurance across these contracts can vary
over time. Moreover, regulation can affect
which of the derivative contracts offers
the best tail risk hedge at a given point
in time. Bans on naked short-selling or
naked CDS positions14, for example, can
make CDS unattractive and instead make
it preferable to search for approximate
tail risk hedges using options or other
derivatives for regulatory reasons.
Buraschi, Kosowski and Trojani (2012)
obtain data on OTC correlation swaps
and show that option trader funds, for
example, had a statistically significant
loading on a correlation swap based
factor that explained part of their superior
performance as well as their higher tail
risk. Aragon, Kang and Martin (2012)
collect a unique dataset of long equity
option positions for hedge funds using
SEC filings. They find that index option
positions for hedge funds are associated
with greater reductions in portfolio risk of
poorly performing hedge funds.
(ii) Techniques to change the fund
return distribution by means of
dynamic (option-like) trading
strategies (portfolio insurance,
volatility scaling)
Apart from using derivatives to reduce
risk as discussed above, hedge funds can
also employ dynamic trading strategies
to reduce risk. These strategies can be
divided into two further subgroups: (a)
dynamic replication strategies and (b)
volatility scaling strategies.
Dynamic trading strategies are rooted in
dynamic replication and option pricing
theory. A portfolio is said to replicate
another portfolio if it generates at
maturity identical cash flows (positive
or negative). A put option on an equity
index, for example, can be replicated
using positions in equity index futures
and borrowing/lending. Mathematically,
concepts of replication involve the
existence of a equivalent martingale
measure.15 Dynamic trading strategies can
serve the purpose of risk management,
similar to derivatives. They can, however,
also be used for arbitrage strategies.
Replication is costly in practice and
imperfect replication can be desirable
if the objective is to take directional
views on some risks while hedging out
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
other risks. Such arbitrage strategies can
be the source of alpha or risk-adjusted
performance.
Portfolio insurance is one example of a
dynamic trading strategy. It is a method
of hedging a portfolio of stocks against
market risk by short selling stock index
futures. The method was invented by
Leland and Rubinstein in 1976 and
involves the replication of a put option
by shorting the underlying in amounts
determined by the constantly changing
option delta. In principle portfolio
insurance can be applied to other market
risks such as interest rate risk, currency
risk, volatility risk or commodity price risk.
More
advanced
dynamic
trading
strategies are linked to volatility and
correlation trading. In Section 2.1 we
discussed variance and correlation
swaps. A variance swap may be hedged
and hence replicated using a portfolio
of European call and put options with
weights inversely proportional to the
square of strike (Bossu, Strasser and
Guichard, 2005). A correlation swap
can also be synthetically replicated. This
would require index and individual stock
variance swaps. Another approach to
create a correlation exposure is through
a dispersion trade. This involves creating
an exposure to correlation by shorting
index options and going long individual
options accompanied by positions in the
underlying index components and the
riskless rate in time-varying proportions.
The discussion of dynamic replication
strategies shows that hedge funds can
dynamically trade in instruments such
as futures and options to dynamically
replicate option and swap payoffs and
thus hedge risk without having to outright
purchase the instrument that the payoff
replicates. One attraction of such dynamic
trading strategies is that they may be
cheaper than buying the corresponding
derivative in the market which often
involves paying a profit margin to the
seller of the derivative.
(iii) Volatility scaling of positions and
stop-loss rules
Another set of techniques that is not
based on replication, but that also helps
reduce portfolio volatility is related to
volatility scaling and stop-loss rules.
Commodity trading advisors and trendfollowing funds are one group of investors
that extensively employ volatility scaling
of positions. Simply speaking, volatility
scaling means that assets whose volatility
goes up receive a lower weight in the
portfolio, that is portfolio weights are
inversely related to asset specific volatility.
Volatility scaling is not predicated on an
asset price model such as a Brownian
motion and therefore, on a conceptual
level, it significantly differs from dynamic
replication strategies. Volatility scaling
is employed by hedge funds both in
the context of time-series momentum
strategies (Baltas and Kosowski (2012b))
as well as cross-sectional momentum
strategies. See Barroso and Santa-Clara
(2012) for how the risk-adjusted return
of equity momentum strategies can be
improved by means of volatility scaling.
Although conceptually different from
portfolio insurance and volatility scaling
strategies, the use of stop-loss rules by
hedge funds is another related technique.
Stop-loss rules involve the reduction or
closing of positions once losses above
An EDHEC-Risk Institute Publication
17
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
16 - For example, if a
portfolio has a 5% one month
value at risk of £100 million,
then this means that there
is a 5% chance that the firm
could lose more than £100
million in any given month.
Therefore, a £100 million loss
should be expected to occur
once every 20 months.
17 - See, for example,
Embrechts, Kluppelberg and
Mikosch (1997)
a certain level have been incurred. For
example, a position may be cut by 50%
(100%) if it experienced a loss of 2%
(5%). Theoretically stop-loss rules should
not improve performance for asset prices
that exhibit mean reversion as positions
are closed before asset prices recover.
However, stop-loss rules can be very
beneficial since they introduce discipline
into the portfolio construction process
and prevent behavioural biases from
creeping in. One important bias is the
disposition effect (Kahneman and Tversky
(1979)) which states that investors have
a tendency to sell winning investments
more frequently than losing investments.
Stop-loss rules can therefore prevent
portfolio underperformance that is driven
by a manager’s disposition effect. The
empirical evidence on the performance
of mechanical stop-loss rules applied in
equity markets shows that the benefits of
the technique are not constant and vary
over time (Lei and Li (2009)).
(iv) Risk measurement techniques
(VaR, EVT, advanced risk measures)
Risk measurement techniques are different
from risk management techniques
discussed above, but they may help risk
management through the monitoring
or risks and provide risk management
signals. Hedge funds use techniques such
as scenario analysis and Value-at-Risk
(VaR) to monitor portfolio risk. Scenario
analysis is a process of examining possible
future events by considering alternative
possible outcomes. It involves simulating
portfolio returns under different extreme
historical or hypothetical conditions. VaR
is a simple but common risk measure of
the risk of loss of a specific portfolio. For
a given portfolio, probability and time
18
An EDHEC-Risk Institute Publication
horizon, VaR is defined as the threshold
portfolio value such that the probability
that the mark-to-market loss on the
portfolio over the given time horizon
exceeds this value is the given probability
level.16
In principle, VaR can be calculated using
forward looking or backward looking
measures of volatility. Many hedge funds
draw on the latest econometric research
that documents the benefits of using
high frequency data to calculate volatility
estimates. Baltas and Kosowski (2012a)
discuss the role of different volatility
estimators in the implementation of trendfollowing strategies. Blair, Poon and Taylor
(2001) discuss the incremental information
content of implied volatilities and highfrequency index returns, for example. A
number of recent academic papers have
studied volatility estimators that can
improve on realized volatility estimators.
The choice of volatility estimator is crucial
for the successful implementation of
VaR models. Extreme Value Theory17 and
other recent innovations have been used
to address the challenge of forecasting
rare extreme events with a small number
of observations. Hedge funds carry out
research and innovate in the area of
volatility estimation.
(v) Currency overlay
Currency overlay is a technique used
to manage the currency exposure in a
given portfolio (Record (2003)). Currency
overlay can have two main objectives:
(a) hedging exchange rate risk or (b)
speculation and generation of alpha from
tactical exchange rate bets. Therefore, it
can be viewed as falling into subsections
2.1 (i) and (ii) above or Section 2.2 below.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
We discuss hedging applications of
currency overlay in this section and cover
alpha generation aspects in Section 2.2.
Often one distinguishes passive and active
currency overlay. Passive currency overlay
involves the use of forward contracts to
match the portfolio’s currency exposures
in such a way as to insure against exchange
rate fluctuations. The implementation of
the overlay depends on parameters such
as the maturity of the forward contracts,
the frequency of the cash flows to be
hedged, their currency as well as the
rebalancing frequency.
18 - Clawbacks can exist at
the fund level or within the
fund (individual portfolio
manager compensation).
Clawbacks at the fund level
are relatively rare. At the
fund level a clawback can
be viewed as a put option
written by the fund to
investors.
(vi)Performance-enhancing
compensation and incentive
structures (high water marks,
performance fees, clawbacks)
An important but often overlooked set of
techniques used by hedge funds relates
to their compensation structures. There
is evidence that hedge funds that have
higher performance fees achieve higher
total and risk-adjusted performance (JKT
(2012)). The use of high water marks
by hedge funds introduces option like
features into the compensation contract
and affects risk taking and performance
(Buraschi, Kosowski and Sritrakul (2012)).
Another compensation feature used
by many hedge funds are clawbacks.
Clawbacks imply that portfolio managers
do not receive their performance
bonuses in full each year, but that
some proportion is withheld until later
years and their payout is conditional on
future performance.18 See Light (2001)
for an example of a clawback feature in
the compensation scheme of Harvard
Management Company.
2.2 Alpha Creation Techniques
The intercept in performance regressions
such as Equation 1 below (in Section 5)
is referred to as the alpha. The sign and
magnitude of alpha is therefore affected
by the choice of factors. The distinction
between alpha and beta is often not clear
and in practice alpha is often defined
with respect to a simple value-weighted
equity index such as the MSCI World
index instead of a seven-factor model
such as the one in Equation 1. It is wellknown that alpha in an unconditional
performance regression can be generated
by either stock-picking skill or market
timing. Some of the techniques reviewed
below implicitly fall into the category of
market timing.
(i) Advanced econometric and
forecasting techniques for Global
Tactical Asset Allocation
One example of a technique that is
fundamentally based on a form of
successful market timing is Global Tactical
Asset Allocation (or GTAA). GTAA generally
refers to a top-down investment strategy
that attempts to exploit short-term mispricings among a global set of assets.
The implementation of GTAA in practice
differs widely. Litterman (2003, Chapter
25) provides a detailed example of a
GTAA application. GTAA can be further
decomposed into a strategic rebalancing
component and an overlay component.
Strategic rebalancing is implemented
to make sure that the portfolio stays
close to the strategic benchmarks that
the investment mandate prescribes. The
overlay component aims at capturing
excess return through tactical long and
short positions in different asset classes
and countries. Global macro funds, for
An EDHEC-Risk Institute Publication
19
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
19 - ‘Buyside IT department
of the year – Marshall
Wace tops the league of
innovation’, by Dan Barnes,
Financial News, 10 October
2011.
example, invest across a wide range of
securities such as global equities, bonds,
commodities and currencies. To reduce
transaction costs they often use futures
and forwards to take positions. They
use different signals including, but not
limited to value and momentum. Asness,
Moskowitz and Pedersen (2012) provide
a good overview of the profitability of
value and momentum strategies across
different asset classes. Volatility and
correlation can be viewed as separate
asset classes. Implied volatility indices
now exist not just for equity, bond and
currency markets, but also for commodity
markets such as oil. Therefore, tactical
allocations to benefit from changes in
implied or realized volatility or correlation
can be viewed as part of the investment
opportunity set for GTAA.
(ii) Asset-specific bets
and stock-picking
In contract to GTAA which focuses on
general movements in the market, some
techniques focus on the performance
of individual securities. The signals used
in practice for stock-picking are too
numerous to list here and we can only
provide a small number of unrepresentative
examples. McLean and Pontiff (2012)
provide one of the most comprehensive
studies of 82 market anomalies identified
in the academic literature. The techniques
used by hedge funds to implement assetspecific or discretionary bets can be
classified into discretionary or systematic
and quantitative approaches. Value and
momentum strategies within asset classes
are one example of systematic stockpicking strategies. Asness, Moskowitz
and Pedersen (2012) not only provide
evidence of value and momentum
20
An EDHEC-Risk Institute Publication
strategies across asset classes but also
within asset classes. The academic
literature has, for example, documented
the information content of some analyst
forecasts (Womack (1996)). In practice,
some funds have developed sophisticated
systems to capture the information in
analyst recommendations. These systems
are also referred to as alpha capture
systems, and they allow investment banks
and other organisations to submit ‘trading
ideas’ or analyst recommendations in a
written electronic format. Marshall Wace
implemented one of the first alpha capture
systems called TOPS (Trade Optimised
Portfolio System) in 2001.19
(iii) Order-execution alpha
Some hedge fund strategies have grown
out of techniques used by market makers
and the returns earned as compensation
for liquidity provision services. Orderexecution alpha is sometimes used to
describe alpha generated through the
superior execution of orders and the
provision of liquidity in the market
(Easley, Lopez de Prado and O’Hara
(2012)). The quantitative techniques
used to implement it in practice are
sometimes based on algorithmic trading.
Algorithmic trading is the use of
electronic platforms for entering trading
orders with an algorithm determining the
timing, price and quantity of an order. If
algorithmic trading occurs at very high
frequencies, it is often referred to as ‘high
frequency-trading’. Algorithmic trading
can also take the form of scalping which
is a method of arbitraging small price
gaps created by the bid-ask spread.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
20 - See Overview of
Leverage, AIMA Canada,
Strategy Paper Series
Companion Document,
October 2006, Number 4,
http://aima-canada.org/
doc_bin/AIMA_Canada_
StrategyPaper_06_Leverage.
pdf.
(iv) Shareholder activism
Another technique used for stock specific
bets that is however much more longterm than the strategies reviewed in
the previous subsections, is shareholder
activism. Generally speaking, activist
shareholders use their ownership stakes in
companies to monitor and influence the
management of the company. Brav, Jiang
and Kim (2009) provide a comprehensive
review of shareholder activism by hedge
funds. They note that hedge fund activism
differs from that of other institutional
investors in several ways. Hedge funds’
incentive fees and the prevalence of
personal investments by their managers
imply that they have strong incentives to
generate performance. Moreover, hedge
funds can use derivatives or leverage
their stakes. Derivatives also allow them
to potentially separate their cash flow
and voting rights associated with certain
investments (Black and Hu (2006)).
(v) Currency alpha
Although trading in foreign exchange
markets could be viewed as only one type
of asset specific bet or market timing with
the objective of generating alpha, we
review it separately since many managers
specialize in currency markets. Currency
alpha is related to currency overlay and
the generation of alpha from currency
markets is one type of active currency
overlay strategy (see Section 2.1. (v)). There
are many different currency strategies,
but they can be broadly classified as (a)
carry, (b) trend-following, (c) value or
(d) volatility based. Levich and Pojarliev
(2008) provide a comprehensive overview
and also use tradable proxies for (a)-(d).
A carry trade strategy implies borrowing
in a low interest rate currency and
investing in a high interest rate currency.
Trend-following currency strategies
are based on time-series momentum.
Value-based currency investing often
depends on Purchasing Power Parity
deviations. Volatility can also form the
basis for currency managers who may use
derivatives to benefit from volatility in
currency markets.
2.3 Leverage as a Performance
Driver
Hedge funds can use several techniques
related to leverage to affect their
performance and risk. Leverage can scale
an existing alpha and can therefore be a
performance or alpha driver (Buraschi,
Kosowski and Sritrakul (2012)). We can
distinguish three types of leverage:
(a) financial leverage, (b) construction
leverage and (c) instrument leverage.20
(i) Financial Leverage (borrowing
leverage and/or notional leverage;
prime broker funding)
Financial leverage can be created through
borrowing leverage and/or notional
leverage. Hedge funds can trade on
margin or borrow from a prime broker
and use the funding to leverage existing
positions.
(ii) Construction leverage
Construction leverage can be the result of
how assets are combined in a portfolio and
in particular whether long positions are
hedged with short positions. Short selling
involves the sale of a security not owned
by the seller. A short seller must generally
pledge to the lender other securities or
cash as collateral for the shorted security
in an amount at least equal to the market
An EDHEC-Risk Institute Publication
21
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
price of the borrowed securities. As an
example, if a hedge fund has invested
£100,000 in stock A, by shorting £100,000
worth of stock B, the fund uses the
proceeds to increase its position in stock A
(ignoring margin requirements and other
transaction costs for simplicity).
(iii) Instrument Leverage
Instrument leverage measures the intrinsic
risk of a certain security selected since
different instruments have different levels
of internal leverage. Futures positions
are highly leveraged because the initial
margins that are set by the exchanges are
relatively small compared to the notional
value of the contracts in question. Options
and leveraged exchange traded funds are
designed with embedded leverage. Recent
research by Frazzini and Pedersen (2011)
shows that embedded leverage lowers
required returns.
(iv) Risk-parity
Risk parity is one alternative investment
approach followed by hedge funds. The
premise of risk parity is that if asset
allocations are adjusted (leveraged or
deleveraged) to the same risk level,
then the risk-adjusted or Sharpe Ratio
performance of the portfolio can be
increased. Note the analogy between
volatility scaling in CTA strategies
discussed above and risk parity: equating
the volatility of different asset allocation
components by levering the investments
up and down is similar to volatility scaling
futures positions. One famous example of
a hedge fund that employs risk parity is
the Bridgewater Associates All Weather
fund launched in 1996. Bhansali et al.
(2012) provide a critical examination
of risk-parity techniques and benefits.
22
An EDHEC-Risk Institute Publication
Thomas et al. (2012) examine risk parity
in the context of momentum and trendfollowing strategies.
(v) VaR and leverage
In section 2.1 (iv) we discussed VaR as a risk
measurement technique. The Sharpe ratio
is not sensitive to leverage and therefore
it may mask the very different risk-return
combinations created by different levels
of debt. VaR is sensitive to leverage levels
and its use can therefore lead to better
risk management decisions.
As described above, short-selling, leverage,
financial derivatives, and alternative asset
classes are the key tools at the disposal
of hedge fund managers in their quest
of alpha. Restrictions on techniques to
achieve short exposure and prohibitions
of certain asset types point to a serious
potential downside of transporting
hedge fund techniques to the mutual
fund and UICTS space: potentially lower
investment returns. We discuss in detail
by investment objective which techniques
can be potentially implemented by mutual
funds. An important question is to know
whether structuring hedge fund strategies
in the mutual fund/UCITS manner will
compromise these strategies and provide
the same level of returns, considering
the constraints under UCITS regulations
such as investment restrictions, liquidity
requirements, operational requirements
and risk management.
Financial markets exhibit risk return
trade-offs and therefore it is likely that
transporting high return strategies
from the hedge fund space to the
more liquid and regulated mutual fund
space will also involve trade-offs in the
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
presence of restrictions on fees, liquidity
and risk. In theory, the current UCITS
framework (synthetic derivatives to get
short exposure, leverage level, financial
derivatives used for investment purposes)
should allow many alternative strategies
to be replicated. Nevertheless, UCITS won’t
be the appropriate format for certain
alternative strategies due to the liquidity
of underlying strategies/holdings, some
asset classes not being authorized under
UCITS, investment limits and borrowing
rules as well as rules governing the usage
of financial derivatives. For example,
shareholder activism strategies may be
difficult to implement under UCITS rules
due to very concentrated positions.
2.4 Hedge Fund Strategies
In sections 2.1-2.3 we have provided
a comprehensive review of different
techniques used by hedge funds to
generate different performance and risk
profiles. It is beyond the scope of this
paper to review the myriad of strategy
specific investment styles employed by
hedge funds, but below we provide a
selective list of the main hedge fund
strategies (Szylar (2012)). Some of them
may be transported to the mutual fund
space or structured within a UCITS.
(i) Long/short equity and short-selling
Long/short funds aim at identifying
undervalued and overvalued securities.
They then take long and short positions
to benefit from the expected price
corrections.
Short
positions
can
be used to exploit stock or asset specific
mispricing or to hedge the portfolio
against risk factors or market movements,
thus reducing portfolio volatility. As
reviewed above, shorting is also a source
of leverage and can drive performance.
(ii) Equity market neutral
The equity market neutral style generally
aims at creating beta neutral portfolios
relative to different risk factors. The neutral
position can refer to beta, sector, country,
currency, industry, market capitalization,
style neutral, or any combination of these
factors. In practice many equity market
neutral funds are however not beta
neutral (Patton (2008)).
(iii) Convertible arbitrage
Convertible bonds are bonds that give
their holders the right to periodic coupon
payments and, as of a fixed date, the right
to convert the bonds into a fixed number
of shares. Convertible bond arbitrage
strategies typically involve a long position
on the convertible bond, hedged with a
concurrent short sale of the underlying
common stock. Sometimes put options
or credit default swaps may also be used
to hedge the position depending on their
relative prices. The objective is often to
benefit from the underpriced option
embedded in the convertible bond. Other
potential sources of profit are the coupon
return and short rebate as well as gamma
trading. The coupon payments, however,
are usually low compared to normal bond
coupons, and the managers therefore
often use leverage. Choi et al. (2010)
examine the role of convertible bond
arbitrageurs as supplies of capital in the
convertible bond market.
(iv) Global macro
Global macro managers typically pursue
a top-down investment approach and
their choices are oftentimes based on
An EDHEC-Risk Institute Publication
23
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
the analysis of macro-economic variables
associated with the different countries
that they follow. The often make large
directional bets and make extensive use
of derivatives.
(v) Fixed Income Relative Value
Fixed income relative value funds pursue
diverse trading strategies. They include (a)
issuance driven arbitrage (trades involving
on-the-run and off-the-fun treasuries),
(b) yield curve arbitrage, (c) inter-market
spread trading, (d) futures basis trading, (e)
swap spread trading, (f) capital structure
arbitrage, (g) carry trades in fixed income
markets, (h) long/short credit strategies,
(i) break-even inflation trades (involving
nominal and real bonds), (j) emerging
markets fixed income, (k) treasuries over
Eurodollar spread trades, (l) mortgage
backed securities (MBS) trades.
(vi) CTA/Managed Futures funds
Commodity trading advisors (CTA) or
managed futures funds use futures,
forward and options to invest in
commodities, interest rates, equity indices
and currencies. They can be classified into
discretionary funds, which resemble global
macro funds in their trading approach,
and systematic funds. Their trading
strategies differ depending on the horizon
that they exploit as well as whether they
focus on trends or trend reversals and
spread trades. Arnold (2012) investigates
risk, performance and persistence of
systematic and discretionary CTAs.
Baltas and Kosowski (2012a, 2012b)
create futures-based trends following
benchmarks for CTAs and show that they
explain more than 50 percent of the
average CTA index performance.
24
An EDHEC-Risk Institute Publication
(vii) Event-driven strategies
Event driven managers pursue a large
number of different strategies. They
include merger arbitrage funds that
exploit merger events. Distressed securities
funds within this group are active in
companies that experience Chapter 11
filings, restructurings and bankruptcy
reorganizations or recapitalizations.
Other event driven strategies attempt to
capitalize on company news events, such
as earnings releases, spin-offs, carve-outs
and share buybacks.
2.5 Potential Restrictions Regarding
the Transportation of Alternatives
Strategies to the Mutual Fund and
UCITS Space
The transportation of alternative
strategies to the mutual fund space under
the UCITS format implies a certain number
of changes such as the limitation of
leverage, or the fact that physical shorting
is not allowed under UCITS which means
that short exposure can only be achieved
using synthetic derivatives. Moreover,
more parties are needed to manage an
onshore fund (notably the custodian as
well as the administrator), while there
are also increased burdens on operations,
compliance such as getting a pre-trade
monitoring system, marketing and to
some extent the investment staff of the
investment advisor. Such restrictions are
therefore likely to lead to higher costs
for the UCITS version of a fund which
contributes to cost differences between
UICTS and non-UCITS funds.
Three other constraints may also constitute
an obstacle (Szylar (2012)). “The first one
is the ineligibility of certain type of assets
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
such as physical commodities, credit,
distressed but it does not constitute an
insurmountable obstacle for the majority
of alternative strategies. No more than
10% of net assets (so called “trash-ratio”)
maybe invested, as non-core investments,
in transferable securities and money
market instruments which are not listed
on a stock exchange or dealt in on another
regulated market. The second constraint
is the liquidity requirement under UCITS
which has to be at minimum fortnightly.
As most of hedge funds offer monthly or
quarterly liquidity this may constitute a
strong limitation for alternative managers.
A UCITS fund must re-purchase or redeem
its shares / units at the request of any
unitholder. UCITS funds can operate daily,
weekly or bi-monthly dealing. A UCITS can
impose UCITS funds have also to comply
with investment diversification such as
the 10% limit and 5/40 rule as explained
in chapter V. Finally, the third one is linked
with exposures to OTC counterparties.
Exposures to OTC counterparties must be
maintained within the UCITS limits. Risk
exposure to an OTC counterparty may
not exceed 5% NAV or 10% (in case of
EU or equivalent credit institutions). All
these limitations are deeply explained in
chapter IV on counterparty risk.”
have an advantage. In the next section of
the paper we examine the literature on
UCITS funds and carry out an empirical
analysis of the differential performance
of UCITS hedge funds and regular hedge
funds.
Another difference between UHFs and
HFs that is important for the returns that
investors receive and that is not often
mentioned is related to an administrative
or contractual feature. The issue concerns
equalization differences between the two
groups (UHFs and HFs). For HFs a new
series is created for each share class at
the dealing date, but the same process
does not occur for UHFs. This means that
investors investing during a drawdows
An EDHEC-Risk Institute Publication
25
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
2. Review of Hedge Fund Techniques and how they
can be transported to the Mutual Fund/UCITS Space
26
An EDHEC-Risk Institute Publication
3. Literature Review
and our Contribution
An EDHEC-Risk Institute Publication
27
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
3. Literature Review and our Contribution
Our paper is related to three streams of
the literature. First, it is related to recent
work on ‘hedged mutual funds’ in the US
as well as studies on alternatives UCITS.
Agarwal, Boyson and Naik (2009) study
‘hedged mutual funds’ which they define
as mutual funds regulated by the SEC but
employing hedge fund like strategies. They
study 49 hedged mutual funds and find
that despite using similar trading strategies,
hedged mutual funds underperform hedge
funds. The authors attribute this finding
to hedge funds’ lighter regulation and
better incentives. In their sample, hedged
mutual funds outperform traditional
mutual funds. Their analysis suggests that
this superior performance is driven by
managers with experience implementing
hedge fund strategies. The authors use the
CRSP Survivorship-Bias-Free mutual fund
database and include all hedged mutual
funds which appear in the Morningstar
and Lipper databases that classify funds
as hedged mutual funds based on several
criteria other than the investment
objectives reported in Morningstar and
Lipper. In contrast to hedged mutual
funds studied by Agarwal, Boyson and
Naik (2009), alternative UCITS funds have
no restrictions on incentive structures. Our
research is also related to that of Koski and
Ponti (1999) and Almazan et al. (2004) who
investigate the differences in performance
of mutual funds that use derivatives and
mutual funds that do not.
In one of the more recent studies of UCITS
funds, Darolles (2011) examines alternative
UCITS funds. Similar to Agarwal, Boyson
and Naik (2009) he finds that hedge
fund experience counts when managing.
Alternative UCITS funds. Darolles (2011)
studies 450 alternative UCITS funds in the
28
An EDHEC-Risk Institute Publication
Morningstar database on 450 alternative
UCITS funds from June 2004 to May 2011
and compares them to 2782 hedge funds.
Furthermore, the study finds that that the
regulatory constraints come with a cost in
terms of performance, but that this cost
depends on the hedge fund’s investment
objective. This result is in line with the
study by Stefanini et al. (2010), who find
that, on average, Alternative UCITS funds
underperform by 3.50%. Stefanini et al.
(2010) employ a tracking error approach to
compare performance of offshore traditional
hedge fund and the corresponding onshore
UCITS version. Other studies examine UCITS
using smaller databases. Tuchschmid
et al. (2010) examine 191 Alternative UCITS
funds using the Alix UCITS Alternative Index
database. Pascalau (2011) uses a sample
of 66 USD share class UCITS from the
BarclayHedge database. Tuchschmid et al.
(2010) and Pascalau (2011) both use the
Fung and Hsieh (2004) model to calculate
risk adjusted return performance.
Our study builds on and extends the above
papers in three main ways. First, by using
one of the most comprehensive aggregate
UHF and HF databases constructed
to date, we increase the data sample
significantly both in the time series (with
data ending 2012) and the cross-section.
The advantage of using an aggregate
database is that given the relatively small
number of UHFs compared to HFs we are
more likely to capture the full universe
of exciting UCTS funds. Second, we use
a significantly larger number of crosssectional variables that allows us to capture
not only aspects of the incentives faced by
managers (performance fees, high water
markets, etc) but also liquidity conditions
(redemption, notice and lockup periods)
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
3. Literature Review and our Contribution
which have not been studied in the above
studies.
Third, our paper is related to the
literature that documents cross-sectional
performance differences among hedge
funds. This literature shows that funds with
greater managerial incentives (e.g. Agarwal,
Daniel, and Naik (2009), Aggarwal and
Jorion (2010)), strict share restrictions (e.g.,
Aragon (2007)), and less binding capacity
constraints (e.g., Teo (2010)), on average,
outperform their peers on a risk-adjusted
basis. On the relationship between liquidity
and hedge fund performance, Aragon
(2007) argues that share restrictions allow
hedge funds to manage illiquid assets and
earn an illiquidity premium. Teo (2011)
examines hedge funds that grant favourable
redemption terms to investors. He finds
that hedge funds that are exposed to
liquidity risk, but not shielded by strict
share restrictions, underperform during
times of financial distress due to costly
capital outflows. Joenväärä, Kosowski and
Tolonen (2012) aggregate five commercially
available hedge fund databases and show
that the stylized facts about hedge fund
performance and certain fund characteristics
are sensitive to the database used.
We build on this work by using information
about funds’ regulatory structure which
allows us to impose testable restrictions
on the relationship between liquidity and
UHFs’ performance and risk.
An EDHEC-Risk Institute Publication
29
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
3. Literature Review and our Contribution
30
An EDHEC-Risk Institute Publication
4. UCITS Restrictions and
Testable Hypotheses
An EDHEC-Risk Institute Publication
31
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
4. UCITS Restrictions and Testable Hypotheses
There is some misunderstanding in the
literature on UCITS funds since they are
sometimes viewed as a ‘regulated version
of hedge funds’ or a ‘deregulated version’
of classic mutual funds. In fact both mutual
funds and hedge funds are regulated. Since
both hedge funds and mutual funds are
regulated, it is more accurate to view the
UCITS fund structure as imposing additional
constrains on hedge funds while allowing
more flexibility for mutual funds.21
21 - Note that the hedge
fund manager is typically
regulated (and to a lesser
extent the offshore hedge
fund), but both the UCITS
fund and the UCITS
management company are
regulated.
32
An EDHEC-Risk Institute Publication
The UCITS directive was implemented by
the EU in 1985 with the aim of facilitating
cross-border marketing of investment
funds and maintaining a high level of
investor protection. The directive was
aimed at regulating the organization and
oversight of UCITS funds and imposed
constraints concerning diversification,
liquidity, and use of leverage. Tuchschmid
et al. (2010) summarize differences in the
implementation of the UCITS directive in
different countries: “The limited definition
of eligible assets, however, hampered
investment interest. As a consequence, in
the decade following 2000 the European
Commission adopted and applied several
significant directives referred to as Ucits
III. Due to the ambiguity of the Ucits
directives, the European Commission
granted The Committee of European
Securities Regulators (CESR) a mandate
to issue guidelines [CESR/07-044b] on what
constitutes eligible investment instruments.
In general, shares in companies, bonds
(government and corporate), and most
forms of derivatives on bonds and shares
are allowed. In addition, the investment
instrument must be easily traded in liquid
markets. Most jurisdictions do not allow
investments in physical commodities or
certificates which represent them. The
main exception is the German regulation
which does allow holdings in commodities
certificates. Hedge fund, private equity
and real estate holdings are not allowed.
However, the Luxembourg regulation allows
Ucits to invest in closed-ended real estate
investment trust (REIT) funds and closedended hedge funds. Many jurisdictions also
allow investment in indices representative
of such non-eligible assets as physical
commodities or hedge funds. An exemption
in the Ucits directive allows Ucits to hold
up to 10%, often called the trash ratio, in
non-eligible assets, which in practice allows
investments in hedge funds and private
equity. In general, Ucits funds are allowed
to synthetically achieve short positions
through derivatives. France and Ireland
are the exceptions where limited amounts
of short selling are allowed. There are,
however, additional rules which require
that the short position should be adequately
covered, either by the underlying asset or
by an asset which is highly correlated to
the underlying.”
The characteristics of UCITS funds lead
to several testable implications. First,
UCITS funds are subject to leverage
and VaR restrictions and also require a
separate risk management function. As
Tuchschmid et al. (2010 note ‘The most
significant requirement for a Ucits fund
from an organizational viewpoint is that it
should have a separate risk management
team, which is independent of the units in
charge of making portfolio management
decisions. Risk management functions
exist in all investment funds but the formal
requirement could be expected to enhance
risk management in principle,[...]’. From a
theoretical point of view, investment and
risk restrictions may prevent managers from
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
4. UCITS Restrictions and Testable Hypotheses
risk shifting, that is strategically changing
portfolio volatility, to maximize the value
of their implicit incentive contracts and
fees (Buraschi, Kosowski and Sritrakul
(2012)). This can be expected to reduce
the ex post risk of UHFs. We therefore test
the hypothesis (Hypothesis 1a) whether the
volatility of UHFs is lower than that of HFs
for the same investment objective.
Tuchschmid et al. (2010) state that ‘Many
implementations of the Ucits directive
center its risk management regulations
on the value at risk (VaR) measure.
Most countries distinguish two cases of
acceptable VaR levels for Ucits: relative VaR
and absolute VaR. Relative VaR is chosen
if there is a suitable reference index. Then
the VaR of the Ucits may not exceed twice
the level of VaR of the reference index.
The absolute VaR approach is chosen if
a reference index does not exist. Then
the VaR of the Ucits may not exceed a
specific absolute percentage of the net
asset value (NAV). Most jurisdictions have
ruled that the 99% monthly VaR may not
exceed 20% of NAV.’ Therefore we extend
our first hypothesis and examine whether
the relationship between the risk of HFs and
UHFs is dependent on country of origin of
the fund (Hypothesis 1b).
Second, restrictions regarding the
investment opportunity set may imply that
UHFs cannot use derivatives to the same
extent as HFs. Since there is evidence that
derivatives and dynamic trading strategies
can lead to non-normal return profiles we
test the hypothesis whether UHFs have
less non-normal returns and lower tail risk
than HFs (Hypothesis 2). The instruments
used by hedge funds depend on their
investment objective and therefore we
condition each of our tests on the type of
investment objective followed by the funds.
Third, restrictions on the use of derivatives
can also limit the ability to hedge against
market downturns and this leads us to test
the hypothesis whether the returns of UHFs
are less counter-cyclical than that of HFs
(Hypothesis 3).
Tuchschmid et al. (2010) summarize UCITS
regulation on concentration and counterparty risk as follows. ‘The Ucits directives
stipulate an array of rules concerning
concentration and counterparty risk.
These rules are in general the same in
regulatory implementations across member
states. Most notably, the exposure to any
security or money market instruments by
the same issuer may not exceed 10% of
NAV, and in combination with derivatives
it may not exceed 20% of NAV. Special
rules apply to securities or money market
instruments which are issued or guaranteed
by a member state of the EU where the
maximum exposure is 35% of NAV.’
Fourth we examine whether cross-sectional
fund characteristics are related to UHFs
performance. We add to the existing
literature by focusing on the impact of
investment objectives, trading instruments,
and share restrictions. Agarwal, Daniel
and Naik (2009) and Darolles (2011)
find that experience is positively related
to performance. As the above summary
shows that not only do UHF investment
restrictions differ from those faced by
HFs but UHF investment restrictions also
differ by country. Our fifth hypothesis
is based on the fact that we have
information on the domicile of the fund
and the management company which
leads to additional testable restrictions
An EDHEC-Risk Institute Publication
33
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
4. UCITS Restrictions and Testable Hypotheses
regarding the performance of different
funds. In contrast to Darolles (2011) we
study all investment objectives included in
the EurekaHedge database. Darolles (2011)
focuses on Equity Hedge, Relative Value
and Global Macro CTA strategies (from the
Morningstar™ classification) and excludes
the Event Driven and Multi-strategy
categories. Moreover, the risk-adjustment by
Darolles (2011) does not take into account
non-linearities captured by the Fung
and Hsieh (2004) model which we apply.
Darolles (2011) examines the relationship
between current performance as well as
(1) past performance, (2) fund age, (3) past
performance and (4) a year dummy.
We examine how share restrictions in terms
of notice, lockup and redemption periods
explain cross-section of UHFs performance.
Given that Teo (2011) shows that HFs
granting favourable redemption possibilities
for investors, but taking liquidity risk is
harmless for investors, it is interesting to
examine whether this is an issues for UHFs.
Indeed, Tuchschmid et al. (2010) summarize
issues related to liquidity risk and valuation
in following way: ‘Ucits funds are required
to consider liquidity risk [...] when investing
in any financial instrument. In practice, this
means that they are advised to consider
factors such as the bid-ask spread and the
quality of the secondary market. They are
specifically required to be able to allow
20% of NAV to be redeemed at any point.
Ucits funds are required to value their
investments at least twice a month. Illiquid
instrument are allowed to be held (up to
10% of NAV) as long as the fund is able
to meet foreseeable redemption requests.
Liquidity should be offered to clients at
least twice a month.’
34
An EDHEC-Risk Institute Publication
5. Data and Summary
Statistics
An EDHEC-Risk Institute Publication
35
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
5. Data and Summary Statistics
5.1 Alternative UCITS III and Hedge
Fund data
22 - TASS and Morningstar
hedge fund databases do not
include information whether
a fund is UCITS III fund or
regular hedge fund.
23 - We calculate aggregate
hedge fund AuM figures
using December observations
given that month’s AuM
figures are considered to
be more accurate for hedge
funds. See, Edelmann, Fung
and Hsieh (2012) for details.
36
An EDHEC-Risk Institute Publication
In this section, we review the construction
of the aggregate HF and UHF databases.
We combine three major hedge fund
databases (BarclayHedge, EurekaHedge,
and Hedge Fund Research (HFR)) to
the aggregate dataset. These databases
contain information on which hedge
funds are UCITS hedge funds. This allows
us to construct two samples consisting
of HFs and UHFs.22 The sample period
is from January 2003 to June 2012 and
contains funds with at least 12 nonmissing monthly returns. We find that
our consolidated database contains 642
alternative UCITS III funds, of which 550
are active and only 92 are defunct. In
terms of hedge funds, there are 14,216
funds including 6,315 active and 7,901
inactive funds.
It is not a trivial task to merge several
commercial hedge fund databases and
to identify unique hedge funds based on
information on multiple share classes.
The main reason is that commercial data
vendors only provide an identifier for
unique share classes, but they do not
provide identifiers for unique hedge funds.
Using JKT’s (2012) merging approach, we
identify unique alternative UCITS funds
and regular hedge funds. Given that UCITS
funds have their origin in the European
Union we do not limit our focus to USD
share classes, but also include funds that
have only non-USD share classes. In those
cases, we convert their returns and AuM
information into USD before including
them in the analysis. The baseline
version of our consolidated database
contains monthly net-of-fees returns,
AuM, and other characteristics, such as
manager compensation (management
fee, performance-based fee, and highwatermark provision), share restrictions
(lockup period, advance notification
period, and redemption frequency),
domicile, currency code, style category,
and inception.
Table 1 presents the aggregate AuM,
number of funds, attrition rates for the
HF and UHF universe. The table shows
that growth has been extremely fast for
the alternative UCITS universe during
the sample period from January 2003
to June 2012.23 Both aggregate AuM
and number of funds have increased
significantly. Currently there are 557
UHFs, with aggregate AuM of USD 115bn.
Our consolidated database contains a
significantly higher number of active HFs.
There are 6,207 HFs with aggregate AuM of
USD 1.6 trillion. This figure underestimates
the size of HF industry, because, unlike JKT
(2012), we do not include the TASS and
Morningstar databases in our consolidated
database. The reason for this is that these
two databases do not provide information
on whether a fund is an alternative UCITS
III compliant fund or a regular hedge
fund. One concern with databases is that
there may be an over-counting bias as
some funds have multiple share classes
and there is also a practice of ‘whitelabelling’ funds that lead to identical
portfolios appearing under different
names (Duncan, Liu and Skeggs (2012)).
We do not believe that this is an important
concern in our data since we carefully
removed duplicate share classes and the
number of UCITS funds in our data is very
similar to the number of funds in another
commercially available UCITS database
that we compared our data to.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
5. Data and Summary Statistics
Table 1: Capital formation of Alternative UCITS III and Hedge Funds
Table 1 presents the capital formation process of Alternative UCITS III and hedge funds from January 2003 to June 2012. N is the
number of funds in given year. 'Agg Aum' provides aggregate assets under management for Hedge Funds and Alternative UCITS III
Funds. Attrition rate is the percentage of funds that became inactive during the year.
24 - Given the fact that
UCITS hedge funds are a
relatively recent development
it is possible that in the early
part of our sample, some
funds that are now classified
as UCITS hedge funds may
have been non-UCITS hedge
funds initially.
Year
Hedge Funds
Alternative
UCITS III Funds
N
Agg AuM
2003
5674
548 301
Attrition rate
N
Agg AuM
63
4 053
Attrition Rate
2004
6588
836 893
8.7
89
8 165
0
2005
7271
966 500
11.6
117
13 103
0
2006
7917
1 304 928
11.9
166
20 680
0
2007
8171
1 667 172
13.2
224
30 130
0.6
2008
7621
1 158 129
20.0
289
20 327
0.9
2009
7348
1 158 525
14.1
415
50 285
0.7
2010
7077
1 283 742
13.7
554
95 962
5.1
2011
6207
1 242 661
16.5
557
115 078
11.7
Table 1 shows an interesting pattern
for attrition rates that measure the
percentage of funds that become defunct
during the year. On average, HFs attrition
rates are significantly higher compared
to UHFs, but at the end of the sample
the UHF’s attrition rate is almost as high
as that of HFs. During the period from
2003 to 2009, the UHFs attrition rate
is negligible. We believe that there are
two main reasons why the attrition rate
is so low. First, during this period, many
management companies started to offer
alternative UCITS III funds, and therefore
there are relatively few closed (or defunct)
funds in the database. Second, and more
importantly, during the period from 2003
to 2008 the BarclayHedge, EurekaHedge,
and HFR databases have not yet started
to gather information on whether a fund
is UCITS III compliant. This implies that if
a fund moved to the graveyard database
during that period, it did so without any
UCITS III indicator variable. Later in the
sample, commercial databases started to
provide UCITS indicator information for
alive funds, but not for those funds that
entered the graveyard data base earlier
on. In other words, commercial databases
only provide comprehensive data for UHFs
that survived. Therefore, the average UHF
return could be biased upwards at the
beginning of the sample.24 Hence, it is
important to examine subsamples of the
data given the potential survivorship bias
in the UHFs database.
5.2 Summary Statistics for
Compensation and Share
Restrictions Variables
Table 2 presents the compensation
structure and share restriction variables
for HFs and UHFs. We find that on average
HFs’ fees and share restrictions are higher
compared to UHFs. This result is intuitive,
since by regulation UHFs need to provide
at least bi-weekly liquidity and can
therefore be expected to have fewer share
restrictions than HFs.
UHFs can be expected to charge fees that
are lower than those of HFs and closer to
those of mutual funds. Panel A in Table 2
shows that HFs’ average management fee
is 1.58%, which is slightly higher than that
An EDHEC-Risk Institute Publication
37
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
5. Data and Summary Statistics
25 - Aragon, Liang and
Park (2012) and Liang
and Park (2008) provide a
detailed discussion about
US regulation’s impact on
the hedge fund firm’s lockup
decision.
26 - We exclude funds
belonging to Long Only and
Sector styles, since we focus
on funds employing dynamic
trading strategies based on
active use of derivatives,
leverage and short selling.
38
An EDHEC-Risk Institute Publication
of UHFs (1.37%). HFs also charge higher
performance-based fees and impose
more often high-water mark provisions.
Indeed, HFs’ average performance
based fee is 18.84% compared to UHFs’
13.33%. To examine the convergence of
compensation structures of UHFs and
HFs, we divide funds into two subsamples
based on the fund inception date. During
the early period when fund inception date
was from 2003 to 2007, we find slightly
higher fee difference between HFs and
UHFs. Indeed, HFs and UHFs that have
been opened after 2007 have imposed
more similar compensation structures.
Return differences between HFs and
UHFs can therefore potentially be at least
partly explained by the fact that UHFs
charge lower performance-based fees.
Both theoretical models and empirical
evidence suggest that compensation
structure variables are associated with
managerial incentives and potentially
higher gross returns. On the other hand,
by construction higher fees should also
imply lower net (after-fee) returns for
investors.
no lockups. Hence, there are significant
differences in redemption terms between
HFs and UHFs. Our subsample analysis
based on fund inception dates shows
that both HFs and UHFs share restriction
structure are quite similar over the time
even though that anecdotal evidence
suggests that investors are demanding
greater liquidity for their investments.
Panel B in Table 2 shows that HFs impose
remarkably tighter share restrictions
compared to UHFs. By regulation UHFs
need to provide at least bi-weekly
liquidity to investors. Many HFs are
domiciled in the US, however, and US
regulation may have an impact on hedge
funds’ willingness to impose long lockup
periods.25 According to Panel B, 25% of
HFs impose a lockup period. In addition,
HFs typically allow investors monthly
or quarterly redemptions with 30 days
advance notice. In contrast, the majority
of UHFs provide daily redemptions and
Table 3 presents the annualized crosssectional average mean return and
standard deviation as well as the average
Sharpe ratio for a specific category. The
sample contains 11,948 hedge funds, and
475 alternative UCITS funds, which have
at least 18 monthly return observations.
Table shows that HFs outperform UHFs
in terms of standard performance
and risk measures across investment
strategies. Indeed, the cross-sectional
average means and Sharpe ratios are
consistently higher for HFs compared to
those obtained for UHFs. Lower average
5.3 Summary Statistics for Standard
Performance and Risk Measures
We next provide cross-sectional summary
statistics for HFs and UHFs based on the
time period from January 2003 to June
2012. We estimate standard performance
and risk measures for each individual
fund having at least 18 monthly return
observations. We then take the crosssectional average within a specific
category. The results are provided for
HF and UHF funds across investment
strategies. We classify funds into 8 main
categories: CTA, Emerging Markets, Event
Driven, Global Macro, Long/Short Equity,
Market Neutral, Multi-Strategy, and
Relative Value.26
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
5. Data and Summary Statistics
Table 2: Summary statistics of compensation structure and share restrictions variables
This table presents the summary statistics for compensation and share restrictions variables of Alternative UCITS III and Hedge
Funds. The table divides funds into groups based on fund inception date to examine the convergence of fees and restrictions.
High-water mark indicates whether a fund imposes a high-water mark provision. Management Fee (%) shows the cross-sectional
average management fee within a specific category. Incentive Fee (%) shows the cross-sectional average incentive fee within a
specific category. Incentive Fee (Y=1, N=1) indicates the percentage of funds charging an incentive fee. Lockup denotes the length
of period when investors are restricted to withdraw their initial investment. Notice is the cross-sectional average advance notice
period. Redemption denotes the cross-sectional average redemption frequency.
Panel A: Compensation structure variables
Hedge Funds
Compensation Structure
Alternative UCITS III Funds
N
Mean
Min
Max
N
Mean
Min
Max
High-Water Mark (%)
14100
83.55
0
1
635
62.20
0
1
Management Fee (%)
14127
1.58
0
20
640
1.37
0
3
Incentive Fee (%)
14116
18.84
0
65
636
13.33
0
30
Incentive Fee (Y=1, N=1)
14216
95.62
0
1
642
76.48
0
1
Fund inception date between January 2003 and Decmeber 2006
High-Water Mark (%)
N
Mean
Min
Max
N
Mean
Min
Max
5619
84.66
0
1
122
54.92
0
1
Management Fee (%)
5614
1.59
0
6
122
1.26
0
3
Incentive Fee (%)
5603
18.93
0
65
122
12.75
0
30
Incentive Fee (Y=1, N=1)
5642
95.94
0
1
122
69.67
0
1
N
Mean
Min
Max
N
Mean
Min
Max
High-Water Mark (%)
4187
85.03
0
1
468
65.38
0
1
Management Fee (%)
4246
1.66
0
6
474
1.38
0
3
Incentive Fee (%)
4240
18.73
0
60
469
13.85
0
30
Incentive Fee (Y=1, N=1)
4261
95.28
0
1
475
80.42
0
1
Fund inception date between January 2007 and June 2012
Panel B: Share restriction variables
Hedge Funds
Share restrictions
Alternative UCITS III Funds
N
Mean
Min
Max
N
Mean
Min
Max
13289
0.23
0
5
594
0
0
0
Lockup (Y=1, N=1)
8496
25.45
0
1
217
0
0
0
Notice (days)
13837
32.55
0
365
590
1.93
0
14
Redemption (days)
12930
53.45
0
1825
640
3.37
1
14
Lockup (Years)
Fund inception date between January 2003 and Decmeber 2006
N
Mean
Min
Max
N
Mean
Min
Max
Lockup (Years)
5323
0.22
0
5
107
0.00
0
0
Lockup (Y=1, N=1)
3468
25.14
0
1
43
0.00
0
0
Notice (days)
5502
33.10
0
365
110
1.97
0
14
Redemption (days)
5180
50.13
0
1825
122
3.67
1
14
N
Mean
Min
Max
N
Mean
Min
Max
Fund inception date between January 2007 and June 2012
Lockup (Years)
3981
0.21
0
5
449
0.00
0
0
Lockup (Y=1, N=1)
2444
22.55
0
1
151
0.00
0
0
Notice (days)
4135
33.40
0
365
436
1.99
0
14
Redemption (days)
3894
49.54
0
730
473
3.13
1
14
An EDHEC-Risk Institute Publication
39
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
5. Data and Summary Statistics
standard deviations for HFs suggest that
HFs’ higher risk level do not explain their
superior performance. It is plausible
that limitations in the implementation
of hedge fund-like strategies may lead
to UHFs lower performance and higher
volatility.
To examine whether fund returns are nonGaussian, we also estimate third and forth
moments of returns as well as JarqueBera test statistics for HFs and UHFs. Table
3 presents the cross-sectional average
skewness, kurtosis and Jarque-Bera
test statistic for a specific category. The
results suggest that HFs have significantly
more non-Gausian returns than UHFs.
The cross-sectional average of JarqueBera test statistic is considerable higher
for HFs compared to UHFs respective test
statistic. The results seem to be driven
by HFs higher kurtosis, but not negative
skewness, in their returns.
Finally, Table 3 presents the crosssectional average Fung and Hsieh (2004)
alphas and their associated t-statistics
across specific categories. The Fung and
Hsieh (2004) seven-factor model is based
on the following regression,
Ri,t = a + b1(SP - Rf)
+ b2(RL - SP)
+ b3(TY - Rf) + b4(BAA - TY)
+ b5(PTFSBD - Rf) + b6(PTFSFX -Rf)
+ b7(PTFSCOM - Rf) + e,
(Equation 1)
in which the fund’s excess returns are
regressed on traditional buy-and-hold and
primitive trend-following factors. The risk
factors are defined as the excess return of
the S&P 500 index (SP-RF), the return of
40
An EDHEC-Risk Institute Publication
the Russell 2000 index minus the return
of the S&P 500 index (RL-SP), the excess
return of ten-year Treasuries (TY-RF), the
return of Moody's BAA corporate bonds
minus ten-year Treasuries (BAA-TY), the
excess returns of look-back straddles on
bonds (PTFSBD-RF), currencies (PTFSFXRF), and commodities (PTFSCOM-RF).
Table 3 shows that the cross-sectional
average alphas and their t-statistics are
significantly higher for HFs compared
to UHFs. This preliminary evidence
suggests that regular hedge funds seem
to outperform alternative UCITS funds
in terms of risk-adjusted performance.
Hence, it is interesting to examine more
formally performance differences and
reasons for these differences.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
5. Data and Summary Statistics
Table 3: Summary statistics for Alternaltive UCITS III and hedge funds
Table 3 presents summary statistics for Alternative UCITS III funds and hedge funds over the period from January 2003 to June 2012.
Using our consolidated database (BarclayHedge, EurekaHedge, and HFR), we estimate the following statistics for each individual
fund. Mean represents the annualized cross-sectional average return for a specific category. Std is the annualized cross-sectional
average standard deviation. Sharpe is the cross-sectional average Sharpe ratio for specific category. Skewness and Kurtosis are
cross-sectional averages of individual funds' skewness and kurtosis. JB Stat denotes the cross-sectional average Jarque-Bera test
statistic for a specific category. P-value is the cross-sectional average p-value of Jarque-Bera test within a specific category. Alpha
is the annualized cross-sectional average Fung and Hsieh (2004) alpha within a specific category. T-stat of Alpha presents the
cross-sectional average t-statistic of alpha with a specific category.
UCITS
Strategy
N
Mean
Std
Sharpe
Skewness
Kurtosis
JB Stat
P-value
Alpha
t-stat
of alpha
No
All Hedge
Funds
11948
6.76
15.67
0.59
-0.23
3.14
161.24
0.22
4.11
0.85
Yes
All UCITS
III Funds
475
2.56
18.16
0.14
-0.26
1.01
18.77
0.41
-5.39
-0.63
No
CTA
1276
8.50
18.50
0.42
0.23
2.35
70.48
0.25
7.04
0.69
40
0.39
19.83
0.06
-0.25
0.69
10.09
0.51
-8.21
-0.72
Emerging
Markets
1796
8.75
20.42
0.57
-0.35
3.09
126.30
0.18
5.08
0.90
105
5.82
26.17
0.23
-0.38
1.47
34.42
0.29
-3.19
-0.25
Event
Driven
659
7.87
12.70
0.82
-0.34
3.91
192.68
0.16
4.83
1.30
8
-0.59
11.91
-0.02
-0.48
2.44
80.57
0.31
-7.04
-1.25
Global
Macro
690
6.18
16.12
0.37
0.06
2.77
113.18
0.24
3.89
0.53
38
0.56
14.16
0.03
-0.20
0.50
5.07
0.51
-6.21
-0.75
Long/Short
Equity
3542
6.09
15.06
0.46
-0.18
2.07
56.84
0.24
2.95
0.57
109
3.30
17.77
0.19
-0.24
0.78
10.94
0.41
-5.30
-0.61
Market
Neutral
712
3.92
10.29
0.43
-0.22
2.04
56.71
0.29
1.89
0.60
34
-0.16
12.91
-0.01
-0.07
0.70
23.11
0.58
-8.11
-1.18
MultiStrategy
1509
6.10
15.84
0.53
-0.15
2.91
169.14
0.26
4.24
0.81
45
0.17
16.02
0.03
-0.23
0.67
7.46
0.49
-6.48
-0.93
1518
6.15
12.58
1.12
-0.79
6.62
494.23
0.15
3.93
1.67
85
1.95
13.78
0.20
-0.25
1.36
19.72
0.32
-4.70
-0.59
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Relative
Value
An EDHEC-Risk Institute Publication
41
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
5. Data and Summary Statistics
42
An EDHEC-Risk Institute Publication
6. Average HF and UHF
Performance
An EDHEC-Risk Institute Publication
43
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
27 - We thank Kenneth
French for making available
value-weighted market
returns for these regions on
his webpage.
In this section, we investigate how
average HF and UHF performance differs.
We examine the issue using equal- and
value weight portfolios across subperiods,
investment strategies and fund domiciles.
investors, but hold illiquid assets, deliver
poor performance during the liquidity
shocks. Hence, it is interesting to examine
whether the liquidity-based story can
explain performance difference between
HFs and UHFs.
6.1. Average Performance
of HFs and UHFs
We compare hedge fund average
performance to equity markets’ overall
performance. Specifically, we estimate
standard performance and risk measures
also for Global, North American and
European equity markets.27
To evaluate average performance of HFs and
UHFs, we construct equal-weight (EW) and
value-weight (VW) portfolios. EW portfolios
measures how an average hedge fund
performs, while VW portfolio summarizes
the performance of the aggregate wealth
invested in hedge funds. We expect to
find that the aggregate EW portfolio has
a higher average performance compared to
the VW portfolio, since the earlier studies
suggest (e.g., Teo (2010)) that smaller hedge
funds, on average, outperform larger ones.
We construct a subsample analysis given
that alternative UCITS funds have gained
popularity during the more recent periods.
The early period before the 2008-2009
financial crisis contains only very few UHFs.
In addition, results in Table 1 suggest that
UHF data may suffer from survivorship
bias.
To avoid comparing apples and oranges,
we create liquidity matched HF and UHF
portfolios so that we exclude all hedge
funds that do not provide at least bi-weekly
liquidity to investors. Both theoretical asset
pricing models and empirical evidence
suggest that illiquid assets should deliver
higher average returns. Aragon (2007)
shows that hedge funds with strict share
restrictions are able to earn an illiquidity
premium. In addition, Teo (2010) shows
that hedge funds that provide liquidity for
44
An EDHEC-Risk Institute Publication
Overall average performance results
presented in Tables 4 and 5 - show that
HFs seem to outperform UHFs. Consistently
across time-periods and portfolio weighing
schemes, standard performance measures
are higher for HFs even after accounting
for tail risk. Average Fung and Hsieh (2004)
alphas and associated t-statistics are also
significantly higher for HFs compared to
those obtained for UHFs. In addition, we
find that systematic risk exposure is higher
for UHFs than HFs, since their market beta
coefficients and R-squares of the Fung and
Hsieh (2004) seven-factor model are higher
across model specifications.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
Table 4: Performance of Alternative UCITS III and Hedge Funds
Table 4 presents standard performance measures for Alternative UCITS III and Hedge Funds. 'Number of funds in portfolio' refers
to the number of funds in a given portfolio (mean, minimum and maximum). Mean represents the annualized average return of
a specific portfolio. Std denotes the annualized standard deviation of a specific portfolio. Sharpe is defined as the annualized
mean return of a portfolio divided by the standard deviation of portfolio. CVaR refers to empirical expected shortfall at 5% level. M.
Sharpe refers to modified Sharpe ratio defined as the annualized mean divided by expected shortfall.
Panel A: Equal-weighted Portfolio from January 2003 to June 2012
Number of funds in portfolio
Performance measures
UCITS
Mean
Min
Max
Mean
Std
Sharpe
CVaR
M. Sharpe
No
6979
4530
8200
7.23
7.65
0.94
5.34
1.35
Yes
266
46
589
7.81
13.63
0.57
10.00
0.78
Panel B: Equal-weighted Portfolio from January 2003 to December 2007
UCITS
Mean
Min
Max
Mean
Std
Sharpe
CVaR
M. Sharpe
No
6815
4530
8199
11.12
5.06
2.20
1.95
5.69
Yes
117
46
226
16.78
8.92
1.88
3.42
4.91
Panel C: Equal-weighted Portfolio from January 2008 to June 2012
UCITS
Mean
Min
Max
Mean
Std
Sharpe
CVaR
M. Sharpe
No
7160
4846
8200
2.90
9.66
0.30
6.98
0.42
Yes
431
235
589
-2.17
17.08
-0.13
12.67
-0.17
Panel D: Value-weighted Portfolio from January 2003 to June 2012
Number of funds in portfolio
Performance measures
UCITS
Mean
Min
Max
Mean
Std
Sharpe
CVaR
M. Sharpe
No
5607
3000
6655
6.19
6.62
0.94
4.15
1.49
Yes
164
21
492
7.99
13.46
0.59
9.77
0.82
Panel E: Benchmark Portfolio from January 2003 to June 2012
Mean
Std
Sharpe
CVaR
M. Sharpe
Global Equities
9.72
20.67
0.47
14.38
0.68
European Equities
8.44
16.78
0.50
12.08
0.70
North American Equities
7.94
15.84
0.50
10.89
0.73
An EDHEC-Risk Institute Publication
45
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
Table 5: Average risk-adjusted returns of Alternative UCITS III and hedge funds
Table 5 presents average risk-adjusted returns and risk loadings. The risk is adjusted using the Fung and Hsieh (2004) seven-factor
model. Alpha refers to the annualized intercept of the Fung and Hsieh (2004) model. T-value is the t-statistic of the Fung and Hsieh
(2004) model's intercept. Risk loadings of the Fung and Hsieh (2004) model are obtained as the excess return of the S&P 500 index
(SP), the return of the Russell 2000 index minus the return of the S&P 500 index (SCLC), the excess return of ten-year Treasuries
(CGS10), the return of Moody's BAA corporate bonds minus ten-year Treasuries (Credspr), the excess returns of look-back straddles
on bonds (PTFSBD), currencies (PTFSFX), and commodities (PTFSCOM). RSQ refers to the R-squared of the Fung and Hsieh (2004)
model.
Panel A: Equal-weighted Portfolio from January 2003 to June 2012
UCITS
Alpha
t-value
SP
SCLC
CGS10
Credspr
PTFSBD
No
3.917
2.494
Yes
0.943
0.327
PTFSFX
PTFSCOM
RSQ
0.335
0.001
0.032
0.263
-0.004
0.642
-0.052
0.272
0.410
-0.011
0.016
0.007
0.689
0.026
0.014
0.669
CGS10
Credspr
PTFSBD
PTFSFX
PTFSCOM
RSQ
Panel B: Equal-weighted Portfolio from January 2003 to December 2007
UCITS
Alpha
t-value
SP
SCLC
No
6.902
3.688
0.297
0.119
0.078
0.227
0.006
0.021
0.016
0.606
Yes
9.175
2.672
0.553
0.090
0.325
0.403
0.010
0.036
0.030
0.572
CGS10
Credspr
PTFSBD
PTFSFX
PTFSCOM
RSQ
Panel C: Equal-weighted Portfolio from January 2008 to June 2012
UCITS
Alpha
t-value
SP
SCLC
No
1.695
0.647
0.339
-0.136
0.025
0.267
-0.008
0.014
-0.010
0.755
Yes
-6.132
-1.279
0.647
-0.203
0.282
0.398
-0.013
0.017
-0.008
0.737
PTFSCOM
RSQ
Panel D: Value-weighted Portfolio from January 2003 to June 2012
UCITS
Alpha
t-value
SP
SCLC
CGS10
Credspr
PTFSBD
PTFSFX
No
3.586
2.146
0.259
-0.054
0.051
0.229
-0.003
0.016
0.010
0.528
Yes
1.502
0.515
0.615
-0.104
0.201
0.419
-0.016
0.018
0.013
0.653
However, Tables 6 and 7 show that when we
compare the performance of the two groups
for ‘liquidity’ matched portfolios, HFs do
not seem not to outperform UHFs. In fact,
UHFs deliver slightly higher average return
and Sharpe ratio. On the other hand, UHFs
risk measures are higher compared to HFs.
Table 6 shows that both HFs and UHFs
provide more stabile returns that equity
markets across regions and time-periods.
Risk measures are significantly lower for
HFs and UHFs compared to respective
measures for equity markets. On the other
hand, equity markets average returns are
higher than those obtained by HFs and
UHFs. However, both versions of Sharpe
ratios are higher for HFs and UHFs compared
respective measures for equity markets.
Hence, risk averse investor may view active
managers as an attractive alternative.
46
An EDHEC-Risk Institute Publication
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
Table 6: Comparing performance of share restrictions matched Alternative UCITS and Hedge Funds
Table 6 presents standard performance measures for share restrictions matched Alternative UCITS and Hedge Funds. Specifically,
share restriction matched portfolios contains only European domiciled funds, which offers at least bi-weekly liquidity to investors
and do not impose lockup periods. We remove funds following Sector and Long Only strategies. The table also presents performance
measures for a set of equity benchmark indexes. The definitions of performance measures can be found in the previous table.
Panel A: January 2003 to June 2012
Number of funds in portfolio
UCITS
Mean
Min
Max
No
314
178
396
Yes
232
36
529
Performance measures
Mean
Std
Sharpe
CVaR
M. Sharpe
5.67
10.07
0.56
6.63
0.86
8.60
13.99
0.61
10.25
0.84
Global Equities
9.72
20.67
0.47
14.38
0.68
European Equities
8.44
16.78
0.50
12.08
0.70
North American Equities
7.94
15.84
0.50
10.89
0.73
Mean
Std
Panel B: January 2003 to December 2005
Number of funds in portfolio
UCITS
Performance measures
Mean
Min
Max
Sharpe
CVaR
M. Sharpe
No
188
147
212
7.66
6.43
1.19
3.33
2.30
Yes
26
12
43
21.97
9.44
2.33
3.94
5.58
Global Equities
21.95
12.92
1.70
3.39
6.47
European Equities
18.51
9.87
1.88
3.77
4.91
North American Equities
14.87
9.72
1.53
3.77
3.94
Panel C: January 2006 to December 2008
Number of funds in portfolio
UCITS
Performance measures
Mean
Min
Max
Mean
Std
Sharpe
CVaR
M. Sharpe
No
213
201
223
5.94
7.74
0.77
4.51
1.32
Yes
75
48
113
0.22
15.33
0.01
16.13
0.01
-6.71
21.09
-0.32
22.14
-0.30
European Equities
-9.69
17.54
-0.55
19.46
-0.50
North American Equities
-10.80
16.07
-0.67
18.23
-0.59
Global Equities
Panel D: January 2009 to December 2011
Number of funds in portfolio
UCITS
Performance measures
Mean
Min
Max
Mean
Std
Sharpe
CVaR
M. Sharpe
No
197
184
202
6.37
10.32
0.62
5.02
1.27
Yes
198
113
252
5.71
16.20
0.35
8.81
0.65
10.67
25.99
0.41
12.05
0.89
Global Equities
European Equities
13.30
20.67
0.64
9.94
1.34
North American Equities
16.47
19.56
0.84
9.85
1.67
An EDHEC-Risk Institute Publication
47
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
Table 7: Comparing the Fung and Hsieh (2004) alphas and betas of share restrictions matched Alternative UCITS III and Hedge
Funds
Table 7 presents average risk-adjusted returns and risk loadings for share restrictions matched Alternative UCITS III funds and
Hedge Funds. Share restriction matched portfolios contains only European domiciled funds, which offers at least bi-weekly liquidity
to investors and do not impose lockup periods. We remove funds following Sector and Long Only strategies. Alpha refers to
annualized intercept of the Fung and Hsieh (2004) model. T-value is the t-statistic of the Fung and Hsieh (2004) model's intercept.
Risk loadings of the Fung and Hsieh (2004) model are obtained as the excess return of the S&P 500 index (SP), the return of the
Russell 2000 index minus the return of the S&P 500 index (SCLC), the excess return of ten-year Treasuries (CGS10), the return of
Moody's BAA corporate bonds minus ten-year Treasuries (Credspr), the excess returns of look-back straddles on bonds (PTFSBD),
currencies (PTFSFX), and commodities (PTFSCOM). RSQ refers to the R-squared of the Fung and Hsieh (2004) model.
Panel A: January 2003 to December 2011
UCITS
Alpha
t-value
SP
SCLC
CGS10
Credspr
PTFSBD
PTFSFX
PTFSCOM
RSQ
No
1.464
0.619
0.472
-0.086
0.182
0.272
0.004
0.026
0.019
0.592
Yes
1.442
0.484
0.653
-0.052
0.285
0.423
-0.014
0.026
0.016
0.664
PTFSBD
PTFSFX
PTFSCOM
RSQ
Panel B: January 2003 to December 2005
UCITS
Alpha
t-value
SP
SCLC
CGS10
Credspr
No
1.048
0.265
0.329
-0.044
0.260
0.415
0.017
0.034
0.014
0.542
Yes
6.372
1.442
0.591
0.056
0.435
0.700
-0.020
0.028
0.052
0.734
SCLC
CGS10
Credspr
PTFSBD
PTFSFX
PTFSCOM
RSQ
Panel C: January 2006 to December 2008
UCITS
Alpha
t-value
SP
No
9.443
2.047
0.034
0.003
-0.007
0.376
0.032
-0.002
0.033
0.359
Yes
10.247
1.601
0.474
0.005
0.138
0.552
0.011
-0.014
0.015
0.685
Credspr
PTFSBD
PTFSFX
PTFSCOM
RSQ
Panel D: January 2008 to December 2011
UCITS
Alpha
t-value
SP
SCLC
CGS10
No
0.003
0.647
0.477
-0.370
-0.061
0.071
-0.001
0.042
0.022
0.651
Yes
-5.836
-0.959
0.761
-0.471
0.064
0.319
-0.043
0.089
-0.044
0.799
Table 7 shows that HFs and UHFs
risk-adjusted average performance
converges when we draw inference from
liquidity matched portfolios. Fung and
Hsieh (2004) alphas are very similar for
both UHFs and HFs. During the early period
UHFs seem to outperform, but HFs deliver
higher alpha during the later period. HFs’
risk level is lower, since their equity betas
and R-squares of the Fung and Hsieh (2004)
model are lower compared to those received
by UHFs. Overall, our findings support the
view that HFs and UHFs performance seem
to converge especially if we take liquidity
into account.
Figure 1 summarizes our main findings
about average performance of HFs and UHFs
over the sample period from January 2003
to December 2011. The figure shows that the
48
An EDHEC-Risk Institute Publication
12-month rolling average return for share
restrictions matched HFs and UHFs with
European domicile is very similar. During
the early periods UHFs seem to outperform,
but, as mentioned above, this may be due to
survivorship bias in commercial databases.
HFs also have lower maximum drawdown
at the time of the financial crisis. Finally,
it is interesting to note that both HFs and
UHFs provide more stabile returns than
equity markets across regions.
Our analysis above is based on net returns
after fees and costs. UHFs are likely to be
smaller than HFs on average. The smaller
size is likely to introduce a performance
drag resulting from the total expense ratio
and is likely to be higher for smaller funds
since some costs do not scale with size.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
Figure 1: Rolling mean Return 2003-2011
Figure 1 presents the 12-month rolling mean return for alternative UCITS III funds, liquid hedge funds, European equities from
December 2003 to December 2011. 'Liquid hedge funds' contains only hedge funds domiciled in Europe that provide at least biweekly liquidity to their investors.
28 - One important caveat
related to the currently
available datasets is that
we do not know whether an
UHF in the data started as a
mutual fund or a hedge fund.
This information is likely to
affect the fund’s strategy but
does not seem to be provided
by data vendors.
6.1. Investment Objectives
Next, we turn to the average performance
of the main investment strategies. We
classify HFs and UHFs into 8 categories:
CTA, Emerging Markets, Event Driven,
Global Macro, Long/Short, Market Neutral,
Multi-Strategy, and Relative Value. We also
show results for Long Only HFs and UHFs
given that it is interesting to compare how
they behave compared to strategies that
employ dynamic trading strategies.28
The overall results across investment
strategies show that HFs deliver a
significantly higher average performance
with a lower risk compared to UHFs.
An EDHEC-Risk Institute Publication
49
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
Table 8: Average performance across investment strategies
Table 8 presents the equal-weight average performance of Alternative UCITS and Hedge Funds from January 2005 to June 2012.
'Number of Funds in Portfolio' presents the minimum (Min), average (Mean), and maximum (Max) number of funds a specific
strategy contains during the holding period. Mean is the annualized average excess return over a risk-free rate for a specific
strategy. Std is the annualized standard deviation of a strategy's returns. Sharpe ratio is defined as a strategy's annualized average
excess returns divided by its annualized standard deviation. CVaR is strategy's empirical expected shortfall at 5% level. M. Sharpe
is defined by annualized mean return divided by the expected shortfall at 5% level.
Number of Funds in Portfolio
UCITS
No
Min
Mean
Max
CTA
583
792
870
6.80
6.04
1.12
2.38
2.85
2
23
48
2.35
15.14
0.16
9.99
0.24
722
1197
1409
8.29
14.54
0.57
10.58
0.78
24
75
131
8.10
23.68
0.34
17.29
0.47
230
416
506
4.98
8.25
0.60
6.16
0.81
3
6
14
-1.40
8.83
-0.16
6.85
-0.21
271
408
432
4.55
5.52
0.82
2.79
1.63
5
24
52
1.55
10.08
0.15
6.95
0.22
214
276
313
4.03
14.68
0.27
11.00
0.37
50
88
117
2.03
18.33
0.11
13.64
0.15
1300
2199
2594
3.73
10.09
0.37
7.14
0.52
23
72
143
2.75
14.32
0.19
10.42
0.26
271
407
447
3.20
5.37
0.60
3.78
0.85
2
20
43
0.19
10.07
0.02
7.07
0.03
618
925
1032
4.82
6.71
0.72
2.98
1.62
6
29
58
0.66
11.86
0.06
8.18
0.08
572
861
994
5.12
6.84
0.75
5.89
0.87
26
61
96
0.76
11.25
0.07
8.18
0.09
Yes
No
Emerging
Markets
Yes
No
Event Driven
Yes
No
Global Macro
Yes
No
Long Only
Yes
No
Long/Short
Yes
No
Market Neutral
Yes
No
Multi-Strategy
Yes
No
Yes
Relative Value
Specifically, Table 8 presents the average
performance and risk measures for HFs and
UHFs. We form equal-weighted strategy
portfolios using funds that have 12 return
observations during the period from January
2005 to June 2012. We also calculate
the number of funds in a given strategy
during the holding period since some of
the UHF strategy portfolios may contain
a small number of funds. Therefore, these
results must be interpreted with caution.
In addition to the standard Sharpe ratio
and volatility, we estimate each strategy
portfolio’s empirical expected shortfall at
the 5% level and use it to calculate the
Modified Sharpe ratio. Table 8 shows that
mean returns and both specifications of
the Sharpe ratio are consistently higher
50
An EDHEC-Risk Institute Publication
Performance Measures
Main Strategy
Mean
Std
Sharpe
CVaR
M. Sharpe
for HFs. Furthermore, UHFs’ risk is higher,
since volatility and tail risk estimates
are consistently higher for UHFs across
investment strategies.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
Table 9: Invesment strategy and average risk-adjusted returns of Alternative UCITS III and Hedge Funds
Table 9 presents the average risk-adjusted returns and risk loadings across main investment strategies over the period from January
2005 to June 2012. The risk is adjusted using the Fung and Hsieh (2004) seven-factor model. Alpha refers to annualized intercept
of the Fung and Hsieh (2004) model. T-value is the t-statistic of the Fung and Hsieh (2004) model's intercept. Risk loadings of the
Fung and Hsieh (2004) model are obtained as the excess return of the S&P 500 index (SP), the return of the Russell 2000 index
minus the return of the S&P 500 index (SCLC), the excess return of ten-year Treasuries (CGS10), the return of Moody's BAA corporate
bonds minus ten-year Treasuries (Credspr), the excess returns of look-back straddles on bonds (PTFSBD), currencies (PTFSFX), and
commodities (PTFSCOM). RSQ refers to the R-squared of the Fung and Hsieh (2004) model.
UCITS
Main
Strategy
Alpha
t-value
SP
SCLC
CGS10
Credspr
PTFSBD
PTFSFX
PTFSCOM
RSQ
No
CTA
6.947
3.250
0.162
-0.032
-0.008
0.058
0.015
0.013
0.035
0.290
-1.123
-0.233
0.523
-0.151
0.245
0.365
-0.006
0.011
0.066
0.422
5.808
1.643
0.549
-0.123
-0.076
0.461
-0.014
0.010
-0.001
0.664
2.793
0.505
0.960
-0.138
0.090
0.742
-0.022
0.018
0.010
0.690
3.486
2.405
0.299
0.006
-0.095
0.304
-0.012
0.003
-0.003
0.825
-3.699
-1.424
0.375
-0.093
0.093
0.114
-0.015
0.007
0.007
0.509
4.193
2.429
0.209
-0.056
0.001
0.098
0.009
0.005
0.026
0.445
-0.135
-0.046
0.444
-0.146
0.062
0.132
0.001
0.000
0.035
0.518
1.391
0.584
0.673
0.006
-0.021
0.407
0.000
0.002
0.005
0.850
-1.657
-0.484
0.894
-0.086
0.067
0.427
-0.001
0.007
0.019
0.802
2.422
1.236
0.449
0.011
-0.101
0.236
0.000
0.006
0.010
0.786
-0.347
-0.110
0.692
-0.099
0.101
0.296
-0.003
0.009
0.023
0.724
2.479
1.506
0.207
-0.053
-0.002
0.094
0.000
0.002
0.010
0.467
-2.032
-0.657
0.408
-0.121
0.175
0.173
0.000
0.000
0.022
0.464
4.892
2.205
0.215
-0.059
-0.031
0.102
0.016
0.012
0.040
0.379
-2.169
-0.625
0.526
-0.101
0.226
0.156
0.000
0.012
0.040
0.514
3.384
2.598
0.213
-0.099
0.035
0.362
-0.010
-0.004
-0.004
0.794
-2.112
-0.634
0.456
-0.151
0.251
0.256
-0.001
0.002
0.025
0.503
Yes
No
Emerging
Markets
Yes
No
Event
Driven
Yes
No
Global
Macro
Yes
No
Long
Only
Yes
No
Long/
Short
Yes
No
Market
Neutral
Yes
No
MultiStrategy
Yes
No
Yes
Relative
Value
The Fung and Hsieh (2004) alphas and
risk loadings in Table 9 supports the
conclusion that HFs outperform UHFs
in terms of risk-adjusted performance
across investment strategies. We find that
alphas are always higher for HFs across
strategies. All of the alphas are positive
for HFs and 5 of 8 them are statistically
significant. Only Emerging Market’s alpha
is positive for UHFs. The other strategies
deliver negative and insignificant alphas.
The results in Table 9 also suggest that UHFs
take more systematic risk than HFs. Indeed,
UHFs show often higher market exposures
model across investment strategies even
though the average number of funds in
strategies is significantly lower compared
to HFs. Also, equity and credit risk exposures
are often higher for UHFs than HFs.
An EDHEC-Risk Institute Publication
51
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
6.3. Domiciles
Next we investigate HF and UHF performance
based on fund domicile. The prior literature
on hedge funds such as Aragon, Liang, and
Park (2011) documents that onshore hedge
funds registered in the USA deliver higher
performance than the offshore hedge funds.
We extend previous work by examining
hedge fund average performance around
the world. The domicile regions of funds are
divided into five groups: Asia and Pacific,
Caribbean, Europe, North America and Rest
of world. Given that only very few of the
UHFs are domiciled outside of Europe, we
exclude other domicile regions from the
analysis.
Table 10 presents average performance
results for HFs and UHFs across fund
domiciles. Overall, we find that European
domiciled funds deliver lower risk-adjusted
compared to funds domiciled in other
regions. Both versions of Sharpe ratios and
Fung and Hsieh alphas (2004) are lower
for European domiciled funds suggesting
that they underperform. The risk-adjusted
performance is highest for North American
and Asia/Pacific domiciled funds.
We find similar performance between
main UHFs domiciles. Indeed, Ireland and
Luxembourg domiciled funds exhibit very
similar performance measures.
The differences cannot be explained by
the benchmark model choice, because
systematic risk exposures do not differ
significantly across fund domiciles. We
also find similar results using benchmark
free measures like Sharpe ratio. We also
find similar performance between main
UHFs domiciles. Indeed, Ireland and
52
An EDHEC-Risk Institute Publication
Luxembourg domiciled funds deliver very
similar performance measures. Our finding
does not support the hypothesis that
regulatory differences between countries
affect performance in a systematic way.
Although UCITS restrictions vary across
countries, it is important to bear in mind
that the restrictions by country for UCITS
are not be set in stone and may vary over
time.
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
Table 10: Fund domicile and performance of alternative UCITS III and hedge funds
Table 10 presents standard risk and performance measures (Panel A) and Fung and Hsieh (2004) alphas and risk loadings (Panel B)
for alternative UCITS III and hedge funds. The sample period is from January 2005 to June 2012. Definitions of the standard risk
and performance measures as well as Fung and Hsieh (2004) model can be found in previous tables.
Panel A: Standard performance measures across fund domiciles
Number of funds in portfolio
UCITS
Fund Domicile
Mean
Min
Max
Performance measures
Mean
Std
Sharpe
CVaR
M.
Sharpe
Yes
Europe
396
142
714
3.05
15.50
0.20
11.56
0.26
Yes
Ireland
53
18
111
2.97
15.36
0.19
11.10
0.27
Yes
Luxembourg
103
31
205
2.52
15.38
0.16
11.47
0.22
No
Europe
998
625
1136
3.34
10.79
0.31
7.66
0.44
No
Asia and
Pacific
160
127
174
11.15
14.25
0.78
8.89
1.26
No
Caribbean
3411
2621
3914
4.64
8.93
0.52
6.53
0.71
No
Rest of the
world
674
527
771
6.33
10.27
0.62
7.56
0.84
No
North
America
2751
2238
2975
6.02
6.89
0.87
4.43
1.36
Panel B: Fung and Hsieh (2004) alphas and risk loadings across fund domiciles
UCITS
Fund Domicile
Alpha
t-value
SP
SCLC
CGS10
Credspr
PTFSBD
PTFSFX
PTFSCOM
RSQ
Yes
Europe
0.225
0.061
0.696
-0.119
0.158
0.399
-0.006
0.006
0.024
0.708
Yes
Ireland
0.288
0.087
0.695
-0.107
0.103
0.433
-0.008
0.014
0.013
0.758
Yes
Luxembourg
-0.068
-0.017
0.671
-0.123
0.173
0.383
0.000
0.003
0.033
0.654
No
Europe
2.176
0.775
0.480
-0.139
0.067
0.274
0.007
0.006
0.023
0.648
No
Asia and
Pacific
9.949
2.744
0.606
-0.161
0.022
0.384
0.011
-0.007
0.035
0.664
No
Caribbean
3.819
1.670
0.354
-0.099
-0.038
0.274
-0.001
0.004
0.012
0.659
No
Rest of the
world
5.565
2.206
0.399
-0.039
-0.094
0.286
-0.001
0.003
0.005
0.687
No
North
America
5.915
4.075
0.278
0.038
-0.132
0.176
0.003
0.008
0.010
0.770
An EDHEC-Risk Institute Publication
53
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
6. Average HF and UHF Performance
54
An EDHEC-Risk Institute Publication
7. Conclusion
An EDHEC-Risk Institute Publication
55
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
7. Conclusion
In this paper we examine the convergence
between the mainstream and the
alternative asset management industry.
First, we provide an academic analysis of
the main techniques that are currently
used by hedge fund managers and that
could be transported to the mutual fund
and alternative UCITS space. We categorize
techniques according to three groups
of techniques: (i) risk management, (ii)
alpha generation and (iii) leverage. UCITS
regulations impose several investment
restrictions and we therefore discuss
whether techniques employed by hedge
funds can be transported to the UCITS and
mutual fund space. Second, we provide an
empirical comparison of the performance of
UCITS and non-UCITS hedge funds. Based
on regulatory constraints, which differ
by country, we economically motivate a
range of hypotheses regarding differences
in performance and risk between UHFs and
HFs. We empirically test them using one
of the most comprehensive hedge fund
databases constructed to date. We find
that UHFs underperform HFs on a total and
risk-adjusted basis. However, UHFs have
more favourable liquidity terms and when
we compare liquidity matched groups of
UHFs and HFs we find that their performance
seems to converge. We uncover an
important liquidity-performance trade-off
in the sample of UHFs. Our results also show
that HFs generally have lower volatility and
tail risk than UHFs which is consistent with
hurdles to the transportation of the risk
management techniques discussed. Finally,
we find important domicile effects related to
firm and fund performance. Our paper sheds
light on the convergence of mainstream
and alternative investment management
as well as drivers of performance and risk
for different types of UCITS funds.
56
An EDHEC-Risk Institute Publication
References
An EDHEC-Risk Institute Publication
57
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
References
• Ackermann, C., R. McEnally and D. Ravenscraft. 1999. The performance of hedge funds:
risk, return and incentives. Journal of Finance 54: 833-874.
• Agarwal, V., N. Boyson and N. Naik. 2009. Hedge Funds for Retail Investors? An Examination
of Hedged Mutual Funds. Journal of Financial and Quantitative Analysis 44(2): 273-305.
(April).
• Agarwal, V., N. D. Daniel and N.Y. Naik. 2009. Role of managerial incentives and discretion
in hedge fund performance. The Journal of Finance 64(5): 2221-2256.
• Agarwal, V. and N.Y. Naik, 2000. Multi-period performance persistence analysis of hedge
funds. Journal of Financial and Quantitative Analysis 53: 327-342.
• Aggarwal, R. K. and P. Jorion. 2010. The performance of emerging hedge funds and
managers. Journal of Financial Economics 96: 238-256.
• Almazan, A., K.C. Brown, M. Carlson and D.A. Chapman. 2004. Why Constrain your Mutual
Fund Manager? Journal of Financial Economics 73: 289-321.
• Aragon, G. O. 2007. Share restrictions and asset pricing: Evidence from the hedge fund
industry. Journal of Financial Economics 83(1): 33-58.
• Aragon, G.O., M. Kang and J.S. Martin. 2011. Risk-Shifting in Hedge Funds: Evidence from
Option Holdings. Working paper.
• Aragon, G. O., B. Liang and H. Park. 2011. Onshore and offshore hedge funds: Are they
twins? Arizona State University.
• Arnold, J. 2012. Performance, Risk and Persistence of the CTA Industry: Systematic vs.
Discretionary CTAs. Available at SSRN: http://ssrn.com/abstract=2081937 or http://dx.doi.
org/10.2139/ssrn.2081937.
• Asness, C., T. Moskowitz and L.H. Pedersen. 2012. Value and Momentum Everywhere.
Forthcoming Journal of Finance.
• Baltas, A. N. and R. Kosowski. 2012a. Improving Time-Series Momentum Strategies:
The Role of Trading Signals and Volatility Estimators. Available at SSRN: http://ssrn.com/
abstract=2140091 or http://dx.doi.org/10.2139/ssrn.2140091. (30 August 2012).
• Baltas, A. N. and R. Kosowski. 2012b. Momentum Strategies in Futures Markets and
Trend-following Funds. Finance Meeting EUROFIDAI-AFFI Paper (Paris). Available at SSRN:
http://ssrn.com/abstract=1968996 or http://dx.doi.org/10.2139/ssrn.1968996. (December).
• Barnes, D. 2011. Buyside IT department of the year – Marshall Wace tops the league of
innovation. Financial News, (10 October 2011).
• Barroso, P. and P. Santa-Clara. 2012. Managing the Risk of Momentum. Available at SSRN:
http://ssrn.com/abstract=2041429 or http://dx.doi.org/10.2139/ssrn.2041429
• Bhansali, V., J. Davis, G. Rennison, J.C. Hsu and F. Li. 2012. The Risk in Risk Parity: A Factor
Based Analysis of Asset Based Risk Parity. Journal of Investing 21(3). Available at SSRN:
http://ssrn.com/abstract=2167058.
58
An EDHEC-Risk Institute Publication
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
References
• Black, B. and H.T.C. Hu, 2006. Empty Voting and Hidden (Morphable) Ownership: Taxonomy,
Implications and Reforms. The Business Lawyer 71(3): 1011-1070.
• Blair, B.J., S. Poon and S.J. Taylor. 2001. Forecasting S&P 100 volatility: the incremental
information content of implied volatilities and high-frequency index returns. Journal of
Econometrics 105: 5-26.
• Bollen, N.P.B. and V.K. Pool. 2009. Do hedge fund managers misreport returns? Evidence
from the pooled distribution. Journal of Finance 64: 2257-2288.
• Bollen, N. P.B., and V.K. Pool. 2008. Conditional return smoothing in the hedge fund
industry. Journal of Financial and Quantitative Analysis 43: 267-298.
• Bossu, S., E. Strasser and R. Guichard. 2005. Just what you need to know about variance
swaps. JP Morgan Equity Derivatives. Quantitative Research and Development.
• Brav, A.P., W. Jiang and H. Kim. 2009. Hedge Fund Activism: A Review. Foundations and
Trends in Finance 4(3).
• Buraschi, A., R. Kosowski and W. Sritrakul. 2012. Incentives and Endogenous Risk Taking:
A Structural View of Hedge Funds Alphas. AFA 2012 Chicago Meetings Paper. Available
at SSRN: http://ssrn.com/abstract=1785995 or http://dx.doi.org/10.2139/ssrn.1785995.
• Buraschi, A., R. Kosowski and F. Trojani. 2012. When there is no place to hide' - correlation
risk and the cross-section of hedge fund returns. SSRN Working Paper.
• Burghardt, G. and L.Liu. 2012. It’s the autocorrelation stupid. NewEdge AlternativeEdge
Note. Alternative Investment Solutions.
• Choi, D., M. Getmansky, B.J. Henderson and H. Tookes. 2010. Convertible Bond Arbitrageurs
as Suppliers of Capital. The Review of Financial Studies (23)6: 2492-2522.
• Darolles, S. 2011. Quantifying Alternative UCITS. Working Paper.
• Derman, E. and N. N. Taleb. 2005. The illusions of dynamic replication. Quantitative
Finance 5(4): 323-326. (August).
• Devasabai, K. 2012. Hedge fund investors get creative with tail hedges - Taming the tails.
Hedge Fund Review. (23 May 2012)
• Duncan, R., L. Liu and J. Skeggs. 2012. There are databases, and then there are databases.
NewEdge AlternativeEdge Note. Alternative Investment Solutions.
• Easley, D., M. Lopez de Prado and M. O'Hara. 2012. Optimal Execution Horizon. Johnson
School Research Paper Series 10. Available at SSRN: http://ssrn.com/abstract=2038387 or
http://dx.doi.org/10.2139/ssrn.2038387.
• Edelmann, D., W. Fung and D.A. Hsieh. 2012. Exploring Uncharted Territories of the
Hedge Fund Industry: Empirical Characteristics of Mega Hedge Fund Firms. Working Paper.
• Embrechts, P. C. Kluppelberg and T. Mikosch. 1997. Modelling extremal events for insurance
and finance. Springer. Berlin.
An EDHEC-Risk Institute Publication
59
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
References
• Fama, E. F. and J.D. MacBeth. 1973. Risk, return, and equilibrium: Empirical tests. Journal
of Political Economy 81: 607-636. Frazzini, A. and L.H. Pedersen. 2011. Embedded Leverage.
NYU Working Paper.
• Fung, W., D. Hsieh. 2000. Performance characteristics of hedge funds and CTA funds:
Natural versus spurious biases. Journal of Financial and Quantitative Analysis 35: 291-307.
• Fung, W. and D. Hsieh. 2004. Hedge fund benchmarks: a risk based approach. Financial
Analyst Journal 60: 65-80.
• Fung, W. and D. Hsieh. 2009. Measurement biases in hedge fund performance data: An
Update. Financial Analysts Journal 65: 36-38.
• Fung, W., D. Hsieh, N. Naik and T. Ramadorai. 2008. Hedge funds: performance, risk, and
capital formation. Journal of Finance 63(4): 1777-1803.
• Güner, B., S. T. Rachev, B. Edelman and F.J. Fabozzi.. 2010. Bayesian inference for hedge
funds with stable distribution of returns. Chapter 4 in “Rethinking Risk Measurement and
Reporting: Volume II”. Klaus Böcker, eds.
• Harvey, C.R. and A. Siddique. 2000. Conditional Skewness in Asset Pricing Tests. Journal
of Finance 55(3): 1263-1295.
• Jagannathan, R., A. Malakhov and D. Novikov. 2010. Do hot hands exist among hedge
fund managers? An empirical evaluation. The Journal of Finance 65(1): 217.255.
• Joenväärä, J., R. Kosowski and P. Tolonen. 2012. Revisiting stylized facts about hedge
funds. Imperial College Business School.
• Jylhä, P. 2011. Hedge fund return misreporting: Incentives and effects. Aalto University.
• Kahneman, D. and A. Tversky.1979. Prospect theory: An analysis of decision under risk.
Econometrica 47: 263–292.
• Kelly, B.T. and H. Jiang. 2012. Tail Risk and Hedge Fund Returns. Chicago Booth Research
Paper No. 12-44; Fama-Miller Working Paper. Available at SSRN: http://ssrn.com/
abstract=2019482 or http://dx.doi.org/10.2139/ssrn.2019482.
• Koski, J. and J. Pontiff. 1999. How are Derivatives Used? Evidence from the Mutual Fund
Industry. Journal of Finance 54: 791-816.
• Kosowski, R., N.Y. Naik and M. Teo. 2007. Do hedge funds deliver alpha? A Bayesian and
bootstrap analysis. Journal of Financial Economics 84(1): 229-264.
• Kraus, A. and R. Litzenberger. 1976. Skewness Preference and the Valuation of Risk Assets.
Journal of Finance 31(4): 1085-1100.
• Lei, A.Y.C. and H. Li. 2009. The value of stop loss strategies, Financial Services Review
18: 23-51.
• Levich, R.M. and M. Pojarliev. 2007. Do Professional Currency Managers Beat the Benchmark.
Financial Analyst Journal (Sept./Oct. 2008): 18-32.
60
An EDHEC-Risk Institute Publication
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
References
• Liang, B. 1999. On the performance of hedge funds. Financial Analysts Journal 55(4):
72-85.
• Liang, B. 2000. Hedge funds: The living and the dead. Journal of Financial and Quantitative
Analysis 35(3): 309-326.
• Liang, B. and H. Park. 2008. Share Restrictions, Liquidity Premium and Offshore Hedge
Funds. University of Massachusetts. Working Paper.
• Light, J.O. 2001. Harvard Management Company. Harvard Business School Case (October):
201-129.
• Litterman, B. 2003. Modern Investment Management: An Equilibrium Approach. John
Wiley & Sons.
• McLean, R.D. and J.E. Pontiff. 2012. Does Academic Research Destroy Stock Return
Predictability? AFFI/EUROFIDAI, Finance Meetings Paper. (Paris). Available at SSRN: http://
ssrn.com/abstract=2156623 or http://dx.doi.org/10.2139/ssrn.2156623. (December).
• Newey, W. and K. West. 1987. A simple, positive semi-definite, heteroscedasticity and
autocorrelation consistent covariance matrix. Econometrica 55: 703-708.
• Pascalau, R. 2011. An Empirical Analysis of UD Dollar Trading Newcits. SSRN Working paper.
• Patton, A. J. 2009. Are ‘Market Neutral’ Hedge Funds Really Market Neutral? Review of
Financial Studies 22 (7): 2495-2530.
• Patton, A. J. and T. Ramadorai. 2011. On the Dynamics of Hedge Fund Risk Exposures.
Forthcoming Journal of Finance.
• Patton, A. J., T. Ramadorai and M. Streatfield. The reliability of voluntary disclosures:
Evidence from hedge funds. Duke University and University of Oxford.
• PerTrac. 2012. The Coming of Age of Alternative UCITS Funds. Pertrac Research Study.
(January).
• Record, N. 2003. Currency Overlay. Wiley Publishing.
• Stefanini, F., T. Derossi, M. Meoli and S. Vismara. 2010. Newcits: Investing in UCITS
Compliant Hedge Funds. Wiley Finance Series, First Edition.
• Szylar, C. 2012. UCITS Handbook. Wiley-Blackwell.
• Taleb, N. N. 2007. Fooled By Randomness: The Hidden Role of Chance in Life and in the
Markets. Penguin Publishing.
• Taleb, N. N. 2008. The Black Swan: The Impact of the Highly Improbable. Penguin Publishing.
• Teo, M. 2010. Does size matter in hedge fund industry? SMU - School of Business.
• Teo, M. 2011. The Liquidity Risk of Liquid Hedge Funds. Journal of Financial Economics
100: 24–44.
An EDHEC-Risk Institute Publication
61
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
References
• Thomas, S. H., A.D. Clare, J. Seaton and P.N. Smith. 2012. Trend Following, Risk Parity and
Momentum in Commodity Futures. Available at SSRN: http://ssrn.com/abstract=2126813.
• Tuchschmid, N.S., E. Wallerstein and L. Zanolin. 2010. Will Alternative UCITS Ever be
Loved Enough to Replace Hedge Funds. Haute Ecole de Gestion de Genève. SSRN Working
paper, http://ssrn.com/abstract=1686055.
• Womack, K. 1996. Do Brokerage Analysts' Recommendations have Investment Value?
The Journal of Finance 54(March): 137-157.
62
An EDHEC-Risk Institute Publication
About Newedge
An EDHEC-Risk Institute Publication
63
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
About Newedge
Newedge is a leading force in global multiasset brokerage, with a presence in more
than 20 locations across 16 countries and
access to more than 85 derivatives and
stock exchanges. Newedge is independently
run, whilst still benefitting from the support
of its two global banking shareholders —
Société Générale and Credit Agricole CIB —
and offers a full range of execution, clearing
and prime brokerage services.
Newedge Alternative Investment Solutions
(“Newedge AIS”) has developed to become a
leading independent prime broker. It offers
global hedge funds a multi-asset class prime
brokerage platform, covering a variety of
assets and instruments across equities, fixed
income, foreign exchange and commodities,
cash and derivatives.
Recognising the quality of its Prime
Brokerage offer and team, Newedge
received numerous industry awards in 2012,
including HFMWeek’s Best Prime Broker –
Capital Introduction’ awards in Europe and
in the U.S. and the Hedge Fund Journal
‘Leading Prime Broker for Liquid Strategies’
award for service providers to the European
hedge fund industry. Newedge was also
named 'Top Rated in North America' as
a prime broker in the Global Custodian
Awards for Excellence. Furthermore, Preqin,
the alternative assets industry's leading
source of data and intelligence, ranked
the Newedge Prime Brokerage Group
as number one in terms of CTA market
share.
rates and foreign exchange. Newedge is
committed to helping their hedge fund
clients maximise efficiency in trading
multiple asset classes, while minimising
operational risks.
• Capital Introduction
Newedge's knowledge and in-depth
experience enables introductions to
sophisticated investors where a match in
criteria is found. Newedge specialises by
investor type, with dedicated coverage for
pension funds, Middle Eastern investors,
sophisticated global investors and family
offices. Capital introductions are subject
to local rules and practices.
• Financing, Risk Management and
Reporting
Newedge AIS offers financing and reporting
options that are specifically tailored to
client requirements, and to the different
assets and instruments traded. The firm
focuses on analysing and understanding
customers' requirements, legal structure and
business, before providing a personalised
risk management solution.
• Research
An innovator in providing investors with
benchmarking tools that accurately
represent key hedge fund strategy styles,
Newedge AIS also produces an exclusive
series of industry-leading research reports
and provides quantitative statistics on
hedge fund return information to investors.
• UCITS
• Multi-Asset Prime Brokerage
Newedge offers clients a single, multi-asset
class prime brokerage platform that covers
a variety of financial assets including listed
products, equities, fixed income, interest
64
An EDHEC-Risk Institute Publication
For investors looking to allocate capital
to liquid and transparent fund structures,
Newedge AIS offers an open architecture
UCITS compliant servicing platform. This
allows investors to access best-of-breed
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
About Newedge
hedge funds and CTAs that offer a UCITS
compliant investment solution within their
product range.
• Managed Account Services
Newedge AIS provides an independent
managed account service enabling investors
to access the performance of hedge fund
strategies through a separately managed
account.
An EDHEC-Risk Institute Publication
65
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
About Newedge
66
An EDHEC-Risk Institute Publication
About EDHEC-Risk Institute
An EDHEC-Risk Institute Publication
67
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
About EDHEC-Risk Institute
Founded in 1906, EDHEC is one
of the foremost international
business schools. Accredited by
the three main international
academic organisations,
EQUIS, AACSB, and Association
of MBAs, EDHEC has for a
number of years been pursuing
a strategy of international
excellence that led it to set up
EDHEC-Risk Institute in 2001.
This institute now boasts a team
of 90 permanent professors,
engineers and support staff, as
well as 48 research associates
from the financial industry and
affiliate professors..
The Choice of Asset Allocation
and Risk Management
EDHEC-Risk structures all of its research
work around asset allocation and risk
management. This strategic choice is
applied to all of the Institute's research
programmes, whether they involve
proposing new methods of strategic
allocation, which integrate the alternative
class; taking extreme risks into account
in portfolio construction; studying the
usefulness of derivatives in implementing
asset-liability management approaches;
or orienting the concept of dynamic
“core-satellite” investment management
in the framework of absolute return or
target-date funds.
Academic Excellence
and Industry Relevance
In an attempt to ensure that the research
it carries out is truly applicable, EDHEC has
implemented a dual validation system for
the work of EDHEC-Risk. All research work
must be part of a research programme,
the relevance and goals of which have
been validated from both an academic
and a business viewpoint by the Institute's
advisory board. This board is made up of
internationally recognised researchers,
the Institute's business partners, and
representatives of major international
institutional investors. Management of the
research programmes respects a rigorous
validation process, which guarantees the
scientific quality and the operational
usefulness of the programmes.
68
An EDHEC-Risk Institute Publication
Six research programmes have been
conducted by the centre to date:
• Asset allocation and alternative
diversification
• Style and performance analysis
• Indices and benchmarking
• Operational risks and performance
• Asset allocation and derivative
instruments
• ALM and asset management
These programmes receive the support of
a large number of financial companies.
The results of the research programmes
are disseminated through the EDHEC-Risk
locations in Singapore, which was
established at the invitation of the
Monetary Authority of Singapore (MAS);
the City of London in the United Kingdom;
Nice and Paris in France; and New York in
the United States.
EDHEC-Risk has developed a close
partnership with a small number of
sponsors within the framework of
research chairs or major research projects:
• Core-Satellite and ETF Investment, in
partnership with Amundi ETF
• Regulation and Institutional
Investment, in partnership with AXA
Investment Managers
• Asset-Liability Management and
Institutional Investment Management,
in partnership with BNP Paribas
Investment Partners
• Risk and Regulation in the European
Fund Management Industry, in
partnership with CACEIS
• Exploring the Commodity Futures
Risk Premium: Implications for
Asset Allocation and Regulation, in
partnership with CME Group
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
About EDHEC-Risk Institute
• Asset-Liability Management in Private
Wealth Management, in partnership
with Coutts & Co.
• Asset-Liability Management
Techniques for Sovereign Wealth Fund
Management, in partnership with
Deutsche Bank
• The Benefits of Volatility Derivatives
in Equity Portfolio Management, in
partnership with Eurex
• Structured Products and Derivative
Instruments, sponsored by the French
Banking Federation (FBF)
• Optimising Bond Portfolios, in
partnership with the French Central
Bank (BDF Gestion)
• Asset Allocation Solutions, in
partnership with Lyxor Asset
Management
• Infrastructure Equity Investment
Management and Benchmarking,
in partnership with Meridiam and
Campbell Lutyens
• Investment and Governance
Characteristics of Infrastructure Debt
Investments, in partnership with Natixis
• Advanced Modelling for Alternative
Investments, in partnership with
Newedge Prime Brokerage
• Advanced Investment Solutions for
Liability Hedging for Inflation Risk,
in partnership with Ontario Teachers’
Pension Plan
• The Case for Inflation-Linked
Corporate Bonds: Issuers’ and Investors’
Perspectives, in partnership with
Rothschild & Cie
• Solvency II, in partnership with Russell
Investments
• Structured Equity Investment
Strategies for Long-Term Asian Investors,
in partnership with Société Générale
Corporate & Investment Banking
The philosophy of the Institute is to
validate its work by publication in
international academic journals, as well as
to make it available to the sector through
its position papers, published studies, and
conferences.
Each year, EDHEC-Risk organises three
conferences for professionals in order to
present the results of its research, one in
London (EDHEC-Risk Days Europe), one
in Singapore (EDHEC-Risk Days Asia), and
one in New York (EDHEC-Risk Days North
America) attracting more than 2,500
professional delegates.
EDHEC also provides professionals with
access to its website, www.edhec-risk.com,
which is entirely devoted to international
asset management research. The website,
which has more than 58,000 regular
visitors, is aimed at professionals who
wish to benefit from EDHEC’s analysis and
expertise in the area of applied portfolio
management research. Its monthly
newsletter is distributed to more than 1.5
million readers.
EDHEC-Risk Institute:
Key Figures, 2011-2012
Nbr of permanent staff
90
Nbr of research associates
20
Nbr of affiliate professors
28
Overall budget
€13,000,000
External financing
€5,250,000
Nbr of conference delegates
1,860
Nbr of participants
at research seminars
640
Nbr of participants at EDHEC-Risk
Institute Executive Education seminars
182
An EDHEC-Risk Institute Publication
69
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
About EDHEC-Risk Institute
The EDHEC-Risk Institute PhD in
Finance
The EDHEC-Risk Institute PhD in Finance
is designed for professionals who aspire
to higher intellectual levels and aim to
redefine the investment banking and asset
management industries. It is offered in two
tracks: a residential track for high-potential
graduate students, who hold part-time
positions at EDHEC, and an executive track
for practitioners who keep their full-time
jobs. Drawing its faculty from the world’s
best universities, such as Princeton,
Wharton, Oxford, Chicago and CalTech,
and enjoying the support of the research
centre with the greatest impact on the
financial industry, the EDHEC-Risk Institute
PhD in Finance creates an extraordinary
platform for professional development and
industry innovation.
Research for Business
The Institute’s activities have also given
rise to executive education and research
service offshoots. EDHEC-Risk's executive
education programmes help investment
professionals to upgrade their skills with
advanced risk and asset management
training across traditional and alternative
classes. In partnership with CFA Institute,
it has developed advanced seminars based
on its research which are available to CFA
charterholders and have been taking
place since 2008 in New York, Singapore
and London.
In 2012, EDHEC-Risk Institute signed two
strategic partnership agreements with
the Operations Research and Financial
Engineering department of Princeton
University to set up a joint research
programme in the area of risk and
investment management, and with Yale
70
An EDHEC-Risk Institute Publication
School of Management to set up joint
certified executive training courses in
North America and Europe in the area of
investment management.
As part of its policy of transferring knowhow to the industry, EDHEC-Risk Institute
has also set up ERI Scientific Beta. ERI
Scientific Beta is an original initiative
which aims to favour the adoption of the
latest advances in smart beta design and
implementation by the whole investment
industry. Its academic origin provides the
foundation for its strategy: offer, in the
best economic conditions possible, the
smart beta solutions that are most proven
scientifically with full transparency in
both the methods and the associated
risks.
EDHEC-Risk Institute
Publications and Position Papers
(2010-2013)
An EDHEC-Risk Institute Publication
71
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
EDHEC-Risk Institute Publications
(2010-2013)
2013
• Cocquemas, F. Towards better consideration of pension liabilities in european union
countries (January).
• Blanc-Brude, F. Towards efficient benchmarks for infrastructure equity investments
(January).
2012
• Amenc, N., and F. Ducoulombier. Proposals for better management of non-financial
risks within the european fund management industry (December).
• Cocquemas, F. Improving Risk Management in DC and Hybrid Pension Plans (November).
• Amenc, N., F. Cocquemas, L. Martellini, and S. Sender. Response to the european
commission white paper "An agenda for adequate, safe and sustainable pensions"
(October).
• La gestion indicielle dans l'immobilier et l'indice EDHEC IEIF Immobilier d'Entreprise
France (September).
• Real estate indexing and the EDHEC IEIF commercial property (France) index (September).
• Goltz, F., S. Stoyanov. The risks of volatility ETNs: A recent incident and underlying
issues (September).
• Almeida, C., and R. Garcia. Robust assessment of hedge fund performance through
nonparametric discounting (June).
• Amenc, N., F. Goltz, V. Milhau, and M. Mukai. Reactions to the EDHEC study “Optimal
design of corporate market debt programmes in the presence of interest-rate and
inflation risks” (May).
• Goltz, F., L. Martellini, and S. Stoyanov. EDHEC-Risk equity volatility index: Methodology
(May).
• Amenc, N., F. Goltz, M. Masayoshi, P. Narasimhan and L. Tang. EDHEC-Risk Asian index
survey 2011 (May).
• Guobuzaite, R., and L. Martellini. The benefits of volatility derivatives in equity portfolio
management (April).
• Amenc, N., F. Goltz, L. Tang, and V. Vaidyanathan. EDHEC-Risk North American index
survey 2011 (March).
• Amenc, N., F. Cocquemas, R. Deguest, P. Foulquier, L. Martellini, and S. Sender. Introducing
the EDHEC-Risk Solvency II Benchmarks – maximising the benefits of equity investments
for insurance companies facing Solvency II constraints - Summary - (March).
• Schoeffler, P. Optimal market estimates of French office property performance (March).
• Le Sourd, V. Performance of socially responsible investment funds against an efficient
SRI Index: The impact of benchmark choice when evaluating active managers – an update
(March).
• Martellini, L., V. Milhau, and A.Tarelli. Dynamic investment strategies for corporate
pension funds in the presence of sponsor risk (March).
72
An EDHEC-Risk Institute Publication
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
EDHEC-Risk Institute Publications
(2010-2013)
• Goltz, F., and L. Tang. The EDHEC European ETF survey 2011 (March).
• Sender, S. Shifting towards hybrid pension systems: A European perspective (March).
• Blanc-Brude, F. Pension fund investment in social infrastructure (February).
• Ducoulombier, F., Lixia, L., and S. Stoyanov. What asset-liability management strategy
for sovereign wealth funds? (February).
• Amenc, N., Cocquemas, F., and S. Sender. Shedding light on non-financial risks – a
European survey (January).
• Amenc, N., F. Cocquemas, R. Deguest, P. Foulquier, Martellini, L., and S. Sender. Ground
Rules for the EDHEC-Risk Solvency II Benchmarks. (January).
• Amenc, N., F. Cocquemas, R. Deguest, P. Foulquier, Martellini, L., and S. Sender. Introducing
the EDHEC-Risk Solvency Benchmarks – Maximising the Benefits of Equity Investments
for Insurance Companies facing Solvency II Constraints - Synthesis -. (January).
• Amenc, N., F. Cocquemas, R. Deguest, P. Foulquier, Martellini, L., and S. Sender. Introducing
the EDHEC-Risk Solvency Benchmarks – Maximising the Benefits of Equity Investments
for Insurance Companies facing Solvency II Constraints (January).
• Schoeffler.P. Les estimateurs de marché optimaux de la performance de l’immobilier
de bureaux en France (January).
2011
• Amenc, N., F. Goltz, Martellini, L., and D. Sahoo. A long horizon perspective on the
cross-sectional risk-return relationship in equity markets (December 2011).
• Amenc, N., F. Goltz, and L. Tang. EDHEC-Risk European index survey 2011 (October).
• Deguest,R., Martellini, L., and V. Milhau. Life-cycle investing in private wealth
management (October).
• Amenc, N., F. Goltz, Martellini, L., and L. Tang. Improved beta? A comparison of indexweighting schemes (September).
• Le Sourd, V. Performance of socially responsible investment funds against an
Efficient SRI Index: The Impact of Benchmark Choice when Evaluating Active Managers
(September).
• Charbit, E., Giraud J. R., F. Goltz, and L. Tang Capturing the market, value, or momentum
premium with downside Risk Control: Dynamic Allocation strategies with exchange-traded
funds (July).
• Scherer, B. An integrated approach to sovereign wealth risk management (June).
• Campani, C. H., and F. Goltz. A review of corporate bond indices: Construction principles,
return heterogeneity, and fluctuations in risk exposures (June).
• Martellini, L., and V. Milhau. Capital structure choices, pension fund allocation decisions,
and the rational pricing of liability streams (June).
• Amenc, N., F. Goltz, and S. Stoyanov. A post-crisis perspective on diversification for risk
management (May).
An EDHEC-Risk Institute Publication
73
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
EDHEC-Risk Institute Publications
(2010-2013)
• Amenc, N., F. Goltz, Martellini, L., and L. Tang. Improved beta? A comparison of indexweighting schemes (April).
• Amenc, N., F. Goltz, Martellini, L., and D. Sahoo. Is there a risk/return tradeoff across
stocks? An answer from a long-horizon perspective (April).
• Sender, S. The elephant in the room: Accounting and sponsor risks in corporate pension
plans (March).
• Martellini, L., and V. Milhau. Optimal design of corporate market debt programmes in
the presence of interest-rate and inflation risks (February).
2010
• Amenc, N., and S. Sender. The European fund management industry needs a better
grasp of non-financial risks (December).
• Amenc, N., S, Focardi, F. Goltz, D. Schröder, and L. Tang. EDHEC-Risk European private
wealth management survey (November).
• Amenc, N., F. Goltz, and L. Tang. Adoption of green investing by institutional investors:
A European survey (November).
• Martellini, L., and V. Milhau. An integrated approach to asset-liability management:
Capital structure choices, pension fund allocation decisions and the rational pricing of
liability streams (November).
• Hitaj, A., L. Martellini, and G. Zambruno. Optimal hedge fund allocation with improved
estimates for coskewness and cokurtosis parameters (October).
• Amenc, N., F. Goltz, L. Martellini, and V. Milhau. New frontiers in benchmarking and
liability-driven investing (September).
• Martellini, L., and V. Milhau. From deterministic to stochastic life-cycle investing:
Implications for the design of improved forms of target date funds (September).
• Martellini, L., and V. Milhau. Capital structure choices, pension fund allocation decisions
and the rational pricing of liability streams (July).
• Sender, S. EDHEC survey of the asset and liability management practices of European
pension funds (June).
• Goltz, F., A. Grigoriu, and L. Tang. The EDHEC European ETF survey 2010 (May).
• Martellini, L., and V. Milhau. Asset-liability management decisions for sovereign wealth
funds (May).
• Amenc, N., and S. Sender. Are hedge-fund UCITS the cure-all? (March).
• Amenc, N., F. Goltz, and A. Grigoriu. Risk control through dynamic core-satellite portfolios
of ETFs: Applications to absolute return funds and tactical asset allocation (January).
• Amenc, N., F. Goltz, and P. Retkowsky. Efficient indexation: An alternative to cap-weighted
indices (January).
• Goltz, F., and V. Le Sourd. Does finance theory make the case for capitalisation-weighted
indexing? (January).
74
An EDHEC-Risk Institute Publication
An Analysis of the Convergence between Mainstream and Alternative Asset Management - February 2013
EDHEC-Risk Institute Position Papers
(2010-2013)
2012
• Till, H. Who sank the boat? (June).
• Uppal, R. Financial Regulation (April).
• Amenc, N., F. Ducoulombier, F. Goltz, and L. Tang. What are the risks of European ETFs?
(January).
2011
• Amenc, N., and S. Sender. Response to ESMA consultation paper to implementing
measures for the AIFMD (September).
• Uppal, R. A Short note on the Tobin Tax: The costs and benefits of a tax on financial
transactions (July).
• Till, H. A review of the G20 meeting on agriculture: Addressing price volatility in the
food markets (July).
2010
• Amenc, N., and V. Le Sourd. The performance of socially responsible investment and
sustainable development in France: An update after the financial crisis (September).
• Amenc, N., A. Chéron, S. Gregoir, and L. Martellini. Il faut préserver le Fonds de Réserve
pour les Retraites (July).
• Amenc, N., P. Schoefler, and P. Lasserre. Organisation optimale de la liquidité des fonds
d’investissement (March).
• Lioui, A. Spillover effects of counter-cyclical market regulation: Evidence from the 2008
ban on short sales (March).
An EDHEC-Risk Institute Publication
75
For more information, please contact:
Carolyn Essid on +33 493 187 824
or by e-mail to: carolyn.essid@edhec-risk.com
EDHEC-Risk Institute
393 promenade des Anglais
BP 3116 - 06202 Nice Cedex 3
France
Tel: +33 (0)4 93 18 78 24
EDHEC Risk Institute—Europe
10 Fleet Place, Ludgate
London EC4M 7RB
United Kingdom
Tel: +44 207 871 6740
EDHEC Risk Institute—Asia
1 George Street
#07-02
Singapore 049145
Tel: +65 6631 8501
EDHEC Risk Institute—North America
1230 Avenue of the Americas
Rockefeller Center - 7th Floor
New York City - NY 10020 USA
Tel: +1 212 500 6476
EDHEC Risk Institute—France
16-18 rue du 4 septembre
75002 Paris
France
Tel: +33 (0)1 53 32 76 30
www.edhec-risk.com