Strategic Asset Allocation and the Role of Alternative Investments

DOIhttp://doi.org/10.1111/j.1468-036X.2012.00642.x
Date01 June 2014
Published date01 June 2014
European Financial Management, Vol. 20, No. 3, 2014, 521–547
doi: 10.1111/j.1468-036X.2012.00642.x
Strategic Asset Allocation and the Role
of Alternative Investments
Douglas Cumming
YorkUniversity – Schulich School of Business, 4700 Keele Street, Toronto, Ontario M3J 1P3,
Canada
E-mail: dcumming@schulich.yorku.ca.
Lars Helge Haß
Lancaster University Management School, Lancaster University,Lancaster, LA1 4YX, UK
E-mail: l.h.hass@lancaster.ac.uk.
Denis Schweizer
WHU – Otto Beisheim School of Management, Assistant Professor of Alternative Investments,
Burgplatz 2, 56179 Vallendar, Germany
E-mail: Denis.Schweizer@whu.edu.
Abstract
We introduce a framework for strategic asset allocation with alternative invest-
ments. Our framework uses a quantifiable risk preference parameter, λ, instead
of a utility function. We account for higher moments of the return distributions
and approximate best-fit distributions. Thus, we replace the empirical return
distributions with two normal distributions. We then use these in the strategic
asset allocation. Our framework yields better results than Markowitz’s framework.
Furthermore, our framework better manages regime switches that occur during
crises. To test the robustness of our results, we use a battery of robustness checks
and find stable results.
Keywords: alternative investments;higher moments;strategic asset allocation
JEL Classification: G2, G12, G31
We are very grateful to an anonymous referee for many helpful comments and to the editor
John Doukas for very useful suggestions. Moreover,the paper has benef itted from comments
by Greg N. Gregoriou, Dieter G. Kaiser, Harry M. Kat, Lutz Johanning, Christian Koziol,
Rainer Lauterbach, Michael McDonald, Mark Mietzner, Juliane Proelss, and Maximilian
Trossbach as well as the participants of the EFM Alternative Investments Conference
2011 (Toronto), International Business Research Conference (8th Annual Meeting, Dubai),
European Financial Management Association (17th Annual Meeting, Athena), Campus for
Finance 2009 (Vallendar), and Midwest Finance Association (58th Annual Meeting, Chicago)
for helpful comments and suggestions. All remaining errors are our own.
C
2012 Blackwell Publishing Ltd
© 2012 John Wiley & Sons Ltd
Douglas Cumming, Lars Helge Haß and Denis Schweizer
522
1. Introduction
Alternative investment funds, which exceeded US$9 trillion worldwide in 2009, have
become increasingly important for the portfolios of institutional investors. This paper
proposes a framework for strategic asset allocation that is able to incorporate the special
characteristics of alternative investments.
If investors want to build exposure to alternative investments, they must decide on
their strategic asset allocation. Because strategic asset allocation explains most of a
portfolio’s retur n variability, it is the major determinant of investment performance and
the most critical decision in the investment process (Hoernemann et al., 20051). Use of
an appropriate strategic asset model is even more important when alternativeinvestments
are considered.
Alternative investments typically suffer from data biases due to appraisal smoothing
and stale pricing. Furthermore, return distributions of alternative investments have
significantly higher moments (skewness and kurtosis) which the standard deviation
does not cover. Thus, every standard method for portfolio optimisation employing
alternative investments is likely to be inaccurate (see, e.g., Fung and Hsieh, 1997;
Fung and Hsieh, 2001; Martin, 2001; Brooks and Kat, 2002; Popova et al., 2003;
Agarwal and Naik, 2004; Jondeau and Rockinger, 2006). Furthermore, institutional
investorshave different objective functions than individual investors(Morton et al., 2006;
Cumming and Johan, 2006; Cumming et al., 2011; Groh and von Liechtenstein, 2011;
Nielsen, 2011).
Therefore, our framework corrects for data biases in the return time series of some
alternative investments (private equity and hedge funds). We use a mixture of normal
methods to replace the empirical return distributions, which often exhibit skewness
and positive excess kurtosis, with two normal distributions to approximate a best-fit
distribution. This approach ensures that the best-fit return distributions exhibit higher
moments close to their empirical pendants. We then use the best-fit distributions in the
optimisation procedure. To derivethe strategic asset allocation, we apply a goal function
to examine real investor preferences for risk aversion. Our investors’ objective function
maximises the probability of outperforming some benchmark return while minimising
the probability of underperforming another benchmark.
The previous literature on asset allocation with alternative investments focuses on the
effects of adding one alternative investment class to a traditional mixed-asset portfolio.
It associates the addition of hedge funds with positive effects on portfolio performance
(see, e.g., Amin and Kat, 2002; Lhabitant and Learned, 2002; Amin and Kat, 2003;
Gueyie and Amvella, 2006; Kooli, 2007). In addition, findings assign positive effects
for privateequity (see, e.g., Chen et al ., 2002; Schmidt, 2004; Ennis and Sebastian, 2005).
The literature also finds that real estate investment trusts (REITs) can increase portfolio
performance (see, e.g., National Association of Real Estate Investment Trusts, hereafter
NAREIT, 2002; Hudson-Wilson et al., 2004; Chen et al., 2005; Lee and Stevenson,
2005; Chiang and Ming-Long, 2007).
Huang and Zhong (2011) are a notable exception to this literature. Their work,which is
the most similar to ours, shows that commodities, REITs, and treasury inflation-protected
1The authors present an alternative to the often-cited studies of Brinson et al. (1986, 1991).
They use a slightly different framework and cover a longer time horizon. They also include
alternative assets and use synthetic portfolios.
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2012 Blackwell Publishing Ltd
(2013)
© 2012 John Wiley & Sons Ltd

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