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Hedge Funds as a Diversification Vehicle

TL;DR: In this paper, the authors evaluate the out-of-sample diversification benefits of including hedge fund indexes in global stock-bond portfolios and find no significant increase in performance when hedge funds are included in a portfolio, compared to a well-diversified portfolio as a benchmark.
Abstract: This study evaluates the out-of-sample diversification benefits of including hedge fund indexes in global stock-bond portfolios. The topic is investigated by assessing several asset allocation strategies from 1998 to 2016. Interestingly, the findings show, in general, no significant increase in performance when hedge funds are included in a portfolio, compared to a well-diversified portfolio as a benchmark. A certain degree of risk reduction is observed when including hedge funds in the portfolio, but the performance does not improve significantly, on average. This study extends the literature on portfolio performance when including hedge funds in a multi-asset portfolio, using more asset allocation strategies and a comprehensive dataset compared to previous studies. TOPICS:Real assets/alternative investments/private equity, portfolio construction, performance measurement, risk management

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  • Studies in Visual Communication Volume 10 Issue 2 Spring 1984 Article 6 1984 Drawing a Circle in Washington Square Park Sally Harrison Recommended Citation Harrison, S. (1984).
  • Drawing a Circle in Washington Square Park.

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Hedge funds as a diversification vehicle
I
Andreas Mikkelsen
a,b,
, Frode Kjærland
c,b
, Tom Erik Sønsteng Henriksen
d,
a
UiT The Arctic University of Norway, School of Business and Economics, Follumsvei 39, 9510 Alta,
Norway
b
Nord University Business School, 8049 Bodø, Norway
c
NTNU Business School, Norwegian University of Science and Technology, 7491 Trondheim, Norway
d
School of Economics and Business, Norwegian University of Life Sciences, PO Box 5003, 1432
˚
As,
Norway
I
We would like to thank an anonymous referee for helpful suggestions improving the paper.
Corresponding author. Andreas Mikkelsen is an Associate Professor at the School of Business and
Economics, UiT The Arctic University of Norway. Frode Kjærland is a Professor at NTNU Business School.
Tom Erik Sønsteng Henriksen is a PhD candidate at NMBU Business School.
Email addresses: andreas.mikkelsen@uit.no (Andreas Mikkelsen), frode.kjarland@ntnu.no (Frode
Kjærland), tom.erik.sonsteng.henriksen@nmbu.no (Tom Erik Sønsteng Henriksen)

‘Hedgefunds as a diversification vehicle’
Abstract
In this study, we evaluate the out-of-sample diversification benefits of including hedge
fund indices in global stock-bond portfolios. We investigate this topic by evaluating several
asset allocation strategies in the period from 1998 to 2016.
Interestingly, our findings show, in general, no significant increase in performance when
we include hedge funds in a portfolio, compared to a well-diversified portfolio as a bench-
mark. We observe a certain degree of risk reduction when including hedge funds in the
portfolio, but the performance does not improve significantly, on average. We extend the
literature on portfolio performance when including hedge funds in a multi-asset portfolio,
using more asset allocation strategies and a comprehensive dataset, compared to previous
studies.
Keywords: Finance, Asset Allocation, Hedge funds, Diversification, Performance evaluation.
JEL Classification: G11
March 27, 2019.
2

1. Introduction
This study investigates the out-of-sample effect of including hedge fund (HF) invest-
ments in a portfolio consisting of global stocks and bonds, by using several asset allocation
techniques.
After the financial crisis of 2008, the financial industry transitioned from promising high
returns to adopting a greater degree of risk management and diversification (Roncalli, 2013).
It is well-known that the risk-adjusted performance of a portfolio can be improved by di-
versification across imperfectly correlated assets. However, the study observes an increased
cross-country co-movement of equity and fixed income returns, since the late 1990s, mainly
driven by cross-country correlations of discount rate shocks and/or global capital markets
integration (Viceira et al., 2017).
Investors may also improve diversification by including in their portfolios alternative in-
vestments with low co-movement of equities and bonds. Both commodities and real estate
are said to have low correlation with stocks and bonds and tend to be inflation hedgers.
However, literature does not present a consensus on the diversification benefits of commodi-
ties due to the low returns and increased co-movement of equities around 2008 (e.g. for
a discussion, refer to Erb and Harvey, 2006; Irwin and Sanders, 2011; Basak and Pavlova,
2016; Bhardwaj et al., 2016). Concerning real estate, there is evidence of a low correlation
between private real estate and mixed-asset portfolios, but listed real estate stocks correlate
strongly with the general stock market. The drawback of private real estate investment is
the low liquidity and uncertainty of the duration and outcome of the sales process, which add
an extra risk factor to the asset. Furthermore, the correlation varies over time and increases
in turmoil (refer to Norges Bank Investment Management, 2015 for a review of real estate
diversification potential).
Another way to possibly increase diversification benefits is to include HFs in the portfolio.
With few constraints associated with the investment style, HF managers can follow a broad
variety of strategies. However, as with commodities and real estate, literature does not
present a clear consensus about whether to invest in HF. The returns generated by HF
from 2009 to 2016 was heavily outperformed by the S&P500, and July 2016 witnessed the
largest withdrawal from HF since the global financial crisis (GFC), with HF redemptions
amounting to $25.2 billion.
1
However, the standard deviation (SD) of the HFRI Asset
Weighted Composite Index was substantially lower than that for the S&P500, which gave
the HFRI index a Sharpe ratio that was 37% higher than the S&P500 index (Brown, 2016).
It is worth mentioning that this index is a diversified index and that a standalone HF may
not provide the same risk/return properties.
Another notable factor is that the term ‘hedge fund’ does not imply a homogeneous
asset class but, rather, describes the way in which the fund is organised. Additionally, this
property may give diversified HF strategies favourable returns per unit risk and can thus be
positioned in a well-diversified asset portfolio (Brown, 2016).
1
www.bloomberg.com/news/articles/2016-08-24/hedge-funds-suffer-biggest-redemptions-
since-2009-as-returns-lag.
3

Our findings have interesting implications for portfolio construction when assessing whether
to include HFs. As indicated above, we do not find any significant increase in performance
when introducing HF in a portfolio, on average. However, there are some HF portfolios
with significantly higher risk-adjusted performance in certain sub-periods. We extend the
literature on portfolio performance when including HFs in a multi-asset portfolio, using more
asset allocation strategies and a comprehensive dataset, compared to previous studies.
The remainder of the paper is organised as follows. In section 2, we examine the literature
on HF investments and its diversification properties. In the subsequent section, 3, we describe
the data used and the asset allocation strategies and how the strategies are carried out both
in-sample and out-of-sample. In the 4 section, we perform an empirical analysis and discuss
the results. Section 5 concludes and discusses the implications of our results for portfolio
management and HF as a diversification vehicle.
2. Literature review
There are several studies regarding HF performance. General diversification and the
performance of the HF industry are elaborated by, for example, Shawky et al. (2012). The
study provides insights on why some HFs outperform others upon the introduction of certain
dimensions. Other studies examine the return properties in bull versus bear markets, as done
by Frydenberg et al. (2017), and suggest that HF managers seem to perform better in bearish
markets. One line of research on HF performance is concerned with funds of hedge funds
(FOHFs) (Davies et al., 2009). Denvir and Hutson (2006) examine the performance and
diversification of several HFs and find, consistent with other studies, that FOHFs appear
to underperform. This is despite the fact that FOHFs have characteristics that offset their
apparent underperformance because their returns do not suffer from negative skewness—a
typical feature of many HF strategies.
Another aspect of these studies is performance measurement. Eling and Schuhmacher
(2007) study return data from many HFs and compare the Sharpe ratio with 12 other
performance measurements. Despite the fact that HFs deviate significantly from the normal
distribution, they find that ranking hedge funds by Sharpe Ratio provides identical ranking
when compared to other performance measurements (such as Treynor ratio, Jensens Alpha,
Omega, Sortino ratio, Kappa 3, Upside Potential ratio, Calmar ratio, Sterling ratio, Burke
ratio, Excess Return on VaR, Modified Sharpe ratio, and Conditional Sharpe ratio). They
deal with individual HFs rather than HF indices. Moreover, they analyse both the cases
wherein the HF represents the entire investment and wherein HF constitutes a part of the
total investment. They conclude that Sharpe Ratio is sufficient to analyse hedge funds,
both for stand-alone HFs or when HFs form only a part of an investment. We apply this
reasoning in our study. Brooks and Kat (2002) problematise some of the statistical features
of HF return studies. It is often found that HF indices exhibit highly unusual skewness and
kurtosis properties and first-order serial correlation. They show that although HF indices
are highly attractive in mean-variance terms, this is much less the case when skewness,
kurtosis, and autocorrelation are taken into account. Hence, Sharpe ratios are expected to
overestimate the true risk-return performance of (portfolios containing) HFs. Similarly, the
4

mean-variance portfolio analysis is expected to over-allocate to hedge funds and overestimate
the attainable benefits from including hedge funds in an investment portfolio. We notice this
aspect when interpreting our findings.
However, although the existing literature seems to have emphasised on HF as an invest-
ment alternative, there seems to be a lack of focus on shortcomings when presenting the
advantages of including hedge funds with an overall multi-asset allocation perspective. Our
study is connected to that of Cvitanic et al. (2003), who discuss how large a portion a long-
term investor should allocate to HF along with other asset classes. Among other results,
they find that low beta HFs can serve as substitutes for much of the risk-free placements in
larger portfolios. Another study, Haglund (2010), examines the use of higher moment betas
to examine the effects of the addition of HFs to an equity portfolio on portfolio volatility,
skewness, and kurtosis. The finding is that HFs lower the volatility, skewness, and kurtosis
of the portfolio - however the effect is heavily related to the type of HF strategy. Convertible
arbitrage, equity market neutral, fixed income arbitrage, merger arbitrage and macro are
identified as the most attractive strategies.
Our study is more in line with Haglund (2010). However, we use a different approach than
that adopted in the study and relate our portfolios, including HF, to a broader benchmark
portfolio—instead of viewing HF as a diversification vehicle for only equity investors.
Since the returns of alternative investment strategies exhibit, in general, a low correla-
tion with that of standard asset classes, the hedge funds are expected to occupy a significant
share in active allocation strategies. While in its infancy, the plethora of alternative invest-
ment strategies consisted of a disparate set of managers following very specific strategies.
Significant attempts have been made to structure the industry over the last decade that have
resulted in allowing the application of active asset allocation models to HFs and traditional
investment vehicles. Particularly, investable portfolios replicating broad-based HF indices
are available with a sufficient level of liquidity. In this study, we apply the model developed
in Cvitanic et al. (2003) to a database of hedge funds. Our results have important implica-
tions for investors who consider including alternative investment vehicles in their portfolios.
Particularly, they suggest that low beta HFs may serve as natural substitutes for a significant
portion of investor risk-free asset holdings. Since the model we use can be generalised in
several directions, this study attempts to provide money managers with a tool to allocate
assets among HFs.
3. Data and descriptive statistics
Our data set consists of monthly prices from January 1998 to December 2016, that is,
228 (227) observations for price levels (returns). We use 3 stock and bond indices and 9
different HF indices. The list of indices used are provided in Exhibit 1.
The HF data set consists of indices provided by the Hedge Fund Research (HFR). The
indices are value-weighted, net of transaction costs and fees, and survivorship bias-free. In
order for a fund to be included in HFRs indices, it must be an open-ended fund, active for
more than 2 years, and listed in USD. HFR classifies hedge fund strategies into the following
five categories: equity hedge, event-driven, macro, relative value, and fund of funds; each
5

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References
More filters
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Abstract: We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. In addition, we find that stocks with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.

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TL;DR: In this paper, the authors examined the pricing of aggregate volatility risk in the cross-section of stock returns and found that stocks with high sensitivities to innovations in aggregate volatility have low average returns.
Abstract: We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. Stocks with high idiosyncratic volatility relative to the Fama and French (1993, Journal of Financial Economics 25, 2349) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility. IT IS WELL KNOWN THAT THE VOLATILITY OF STOCK RETURNS varies over time. While considerable research has examined the time-series relation between the volatility of the market and the expected return on the market (see, among others, Campbell and Hentschel (1992) and Glosten, Jagannathan, and Runkle (1993)), the question of how aggregate volatility affects the cross-section of expected stock returns has received less attention. Time-varying market volatility induces changes in the investment opportunity set by changing the expectation of future market returns, or by changing the risk-return trade-off. If the volatility of the market return is a systematic risk factor, the arbitrage pricing theory or a factor model predicts that aggregate volatility should also be priced in the cross-section of stocks. Hence, stocks with different sensitivities to innovations in aggregate volatility should have different expected returns. The first goal of this paper is to provide a systematic investigation of how the stochastic volatility of the market is priced in the cross-section of expected stock returns. We want to both determine whether the volatility of the market

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  • ...The low volatility anomaly leads to an increase in popularity for this allocation strategy (Ang et al. 2006, Blitz and van Vliet 2007, and Jordan and Riley 2015)....

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TL;DR: In this article, the authors evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1-N portfolio.
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  • ...DeMiguel, Garlappi, and Uppal (2009) find that other optimized asset allocation techniques are not consistently better than the 1/N rule, based on several performance measures....

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TL;DR: Brown et al. as mentioned in this paper investigated the sensitivity of mean variance-efficient portfolios to changes in the means of individual assets and found that a positively weigbted mean-variance-efficient portfolio's weights, mean, and variance can be extremely sensitive to cbanges in asset means.
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Frequently Asked Questions (16)
Q1. What have the authors contributed in "Hedge funds as a diversification vehicle" ?

In this study, the authors evaluate the out-of-sample diversification benefits of including hedge fund indices in global stock-bond portfolios. The authors investigate this topic by evaluating several asset allocation strategies in the period from 1998 to 2016. The authors observe a certain degree of risk reduction when including hedge funds in the portfolio, but the performance does not improve significantly, on average. The authors extend the literature on portfolio performance when including hedge funds in a multi-asset portfolio, using more asset allocation strategies and a comprehensive dataset, compared to previous studies. 

The result of the return smoothing is a downward bias in return variance, leading to inflated Sharpe ratios and a positive auto-correlation. 

Since wi is a function of the risk contribution, which again depends on wi, there is a problem of endogeneity that is taken into account in this optimisation algorithm. 

The authors recall that a smoothing index near unity means that the time series is not plagued with auto-correlation and downward biased return volatility; this is true for the macro strategy index. 

The finding is that HFs lower the volatility, skewness, and kurtosis of the portfolio - however the effect is heavily related to the type of HF strategy. 

The authors test the Sharpe ratios and find that the merger arbitrage portfolios with both RRT and maxSR allocations have significant t-statistics, 2.99 and 1.95, respectively. 

A high value, close to unity, represents little return smoothing, while a low value represents a higher degree of return smoothing. 

The authors use a ‘rolling window’ procedure where the authors estimate the parameters needed for the asset allocations based on the previous 36-month returns (as in DeMiguel et al., 2009). 

the parameters are constrained to sum up to 1; this means that, eventually, all information will be reflected in the series, but that it may be take up to q+1 periods after the information is generated, before it is incorporated in the observed price, as shown in Equation 8. 

when using unadjusted returns, the portfolio with all the HFs had the lowest risk in only 50% of the allocation strategies. 

The portfolios, including HFs, that have a larger return, lesser volatility, or higher Sharpe ratio than the stock-bond portfolio, are highlighted in bold. 

a return series with a low smoothing index has a high degree of auto-correlation and a downward bias in the return volatility. 

The average annual return for the hedge fund indices varies from 0.96% for the market neutral index to7Exhibit 2: Descriptive statistics of the stock, bond, and hedge fund indices. 

The authors further note that in all the strategies, except for the strategic weighted allocation strategy, the lowest risk is found in the portfolio containing all the HF indices. 

This study indicates that satisfactory performance is achieved based on normal diversification in stocks and bonds, similar to the majority of mutual funds. 

To ensure that the parameters are constrained to 1, the authors divide the estimates by (1+θ0+...+θk) and assume that the original estimate of θ0 is 1.