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MEASURING THE POTENTIAL AND IMPLICATIONS OF WINE AS AN
ALTERNATIVE INVESTMENT ASSET
A Thesis
Presented to the Faculty
of ISM University of Management and Economics
in Partial Fulfillment of the Requirements for the Degree of
Master of Financial Economics
by
Tomas Ruikys
May 2014
WINE AS AN ALTERNATIVE INVESTMENT ASSET
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ABSTRACT
The aim of the paper was to analyze the plausibility of wine as an alternative
investment asset and checking for any implications of including wine assets into the portfolio.
Upon closer examination, due to reasons of illiquidity and the costs associated with storing, a
decision was made to choose wine indices over the other two alternatives: en primeur, also
referred to as wine futures and direct investments into wine, by purchasing bottles or barrels.
For the purpose of analyzing the effects of wine indices on investment portfolios, under the
theory of Markowitz mean - variance optimization theory, five different types of portfolios
for three different regions were composed. A total of fifteen portfolios were tested. The data
obtained supports the assumption of wine being a hedge against traditional assets, as
statistically significant, but weak correlation was observed between the returns of financial
indices and Liv - Ex Fwin. Furthermore, positive impact on the risk and return characteristics
was observed upon including wine into investment portfolios. Subsequently, lagged CAPM
was used in order to estimate the effects of financial indices on wine index with different
indicator lags. This did not have any significant impact on the correlation results. Finally, in
two out of free regions under analysis, financial indices were found to be the Granger cause
of the wine index. However, caution should be exercised as Granger Causality test shows
only a mathematical relationship between the parameters, and not the interactions based on
economic logic.
Key words: Wine investments, alternative investments, portfolio diversification, Markowitz
mean-variance optimization, wine indices.
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TABLE OF CONTENTS
ABSTRACT ................................................................................................................................... 2
T ABLE OF C ONTENTS ..................................................................................................................... 3
L IST OF FIGURES ............................................................................................................................ 5
L IST OF TABLES .............................................................................................................................. 6
INTRODUCTION ............................................................................................................................... 7
1. L ITERATURE R EVIEW ............................................................................................................... 11
1.1 R EAL E STATE ................................................................................................................ 11
1.2 R EAL E STATE INVESTMENT T RUSTS ............................................................................ 14
1.3 P RIVATE E QUITY .......................................................................................................... 15
1.4 VENTURE C APITAL ....................................................................................................... 16
1.5 B UYOUT F UNDS ............................................................................................................ 17
1.6 DISTRESSED DEBT INVESTMENTS ................................................................................ 18
1.7 C OMMODITIES .............................................................................................................. 20
1.8 M ANAGED FUTURES ..................................................................................................... 22
1.9 HEDGE FUNDS ............................................................................................................... 23
1.10 C OLLECTIBLES ............................................................................................................ 26
1.11 WINE ........................................................................................................................... 29
2. R ESEARCH M ETHODOLOGY ..................................................................................................... 39
2.1 DATA SAMPLE SELECTION ............................................................................................ 42
3. E MPIRICAL R ESULTS ................................................................................................................ 44
3.1 "GLOBAL" S UB - SAMPLE ANALYSIS ............................................................................ 44
3.1.1 R ATE OF RETURN AND INVESTMENT RISK OF FINANCIAL INDICES ................. 44
3.1.2 CORRLATION ANALYSIS ............................................................................ 45
3.1.3 GRANGER C AUSALITY T EST ........................................................................... 47
3.1.4 C OINTEGRATION TESTING ............................................................................... 48
3.1.5 L AGGED CAPM ............................................................................................... 49
3.1.6 M ARKOWITZ MEAN -VARIANCE OPTIMIZATION .............................................. 50
3.2 "E UROPE1" SUB - SAMPLE ANALYSIS .......................................................................... 53
3.2.1 R ATE OF RETURN AND INVESTMENT RISK OF FINANCIAL INDICES ................. 53
3.2.2 C ORRELATION ANALYSIS ................................................................................ 54
3.2.3 GRANGER C AUSALITY T EST ........................................................................... 56
3.2.4 C OINTEGRATION TESTING ............................................................................... 56
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3.2.5 L AGGED CAPM ............................................................................................... 57
3.2.6 M ARKOWITZ MEAN -VARIANCE OPTIMIZATION .............................................. 58
3.3 "E UROPE2" SUB - SAMPLE ANALYSIS .......................................................................... 60
3.3.1 R ATE OF RETURN AND INVESTMENT RISK OF FINANCIAL INDICES ................. 61
3.3.2 C ORRELATION ANALYSIS ................................................................................ 62
3.3.3 GRANGER C AUSALITY T EST ........................................................................... 63
3.3.4 C OINTEGRATION TESTING ............................................................................... 64
3.3.5 L AGGED CAPM ............................................................................................... 64
3.3.6 M ARKOWITZ MEAN -VARIANCE OPTIMIZATION .............................................. 67
4. C ONCLUSIONS ........................................................................................................................... 70
Reference List................................................................................................................................... 73
APPENDICES .................................................................................................................................. 78
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List of Figures
Figure 1. Liv-ex Fwin index and STXE 600 Europe index ...............................................46
Figure 2. Liv-ex Fwin index and BarclaysCpBdGlbl index ..............................................47
Figure 3. Liv-ex Fwin index ir S&P EU Sovereign bond Index ........................................55
Figure 4. Liv-ex Fwin index ir Liv-ex 100 index .............................................................55
Figure 5. Liv-ex Fwin index ir Liv-ex 100 index .............................................................62
Figure 6. Liv-ex Fwin index ir FTSE 100 INDEX ...........................................................63
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List of Tables
Table 1. Indices used in three different sub - samples ......................................................43
Table 2. Mean return, volatility and Sharpe-ratios "Global" sub-sample ...........................45
Table 3. Engel-Granger cointegration test results ............................................................48
Table 4. The regresion results of the effects of financial indices on wine index ................50
Table 5. Weightings of different investment portfolios (%) for sub - sample "Global" ......52
Table 6. Rates of return, risk and Sharpe - ratios (Global sub - sample)............................53
Table 7. Mean return, volatility and Sharpe-ratios "Europe1" sub - sample ......................54
Table 8. Engel Granger cointegration test results.............................................................57
Table 9. The regresion results of the effects of financial indices on wine inde ..................58
Table 10. Weightings of different investment portfolios (%) for sub - sample "Europe1" .60
Table 11. Rates of return, risk and Sharpe - ratios (Euro1 sub - sample) ...........................60
Table 12. Mean return, volatility and Sharpe-ratios "Europe2" sub - sample ....................61
Table 13. Engel Granger cointegration test results ...........................................................64
Table 14. The regresion results of the effects of financial indices on wine index ..............65
Table 15. Rates of return, risk and Sharpe - ratios (Euro2 sub - sample) ...........................67
Table 16. Weightings of different investment portfolios (%) for sub - sample "Europe2" .67
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INTRODUCTION
In the world of investments there are few predominant asset classes - stocks, bonds
and cash. These three asset classes have long dominated the strategic investment portfolio
composition and clearly, even to this day their preeminence cannot be denied when
considering an investment strategy. Though these asset classes are at the core of investment,
one cannot deny the crippling effects which financial crises and economic downturns have
had on them.
Even though, each financial crisis is different in its nature and has contrasting traits, in
most cases, their outcomes and effects on traditional assets seem to be rather resembling.
With each financial crisis that shook the markets, those predominant assets tended to undergo
astringent periods which in turn, damaged the portfolios of financial investors. One of the
most recent examples is the global financial crisis of 2007 - 2008, regarded by many as the
most severe and grandiose crisis after the Great Depression of the 1930s (Pendery, 2009).
The crisis led not only bail outs, and in some cases collapses of financial institutions, but also
to detrimental effects on the returns of many traditional investments. Moreover, due to
globalization, each crisis, though to a varying extent, has the spillover effect, and detrimental
ripples are felt by the neighboring economies, and the largest trade partners, thus making the
crisis more severe.
Due to these reasons the need for an alternative investment asset which would have
negative correlations with the conventional assets, thus providing diversification benefits,
together with an opportunity to mitigate risk, especially during the periods of economic
turmoil has arisen.
In most cases during the recent decades, such alternative assets as real estate, real
estate investment trusts, private equity and commodities were primary and sole choice of the
investors for the purpose of diversification. While such assets as art, wines, and other
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collectibles were not given much attention. This was done, due to a few reasons. Firstly, the
collectibles are known for offering less liquidity to the investor, when compared to the
traditional alternative investments. Secondly, the markets of these collectible assets were
rather underdeveloped and undergoing their embryonic stages. Another important factor for
the investor, was the storage costs associated with the asset. Some of these assets require
unique care and in some cases even maintenance. In addition to the costs associated with
storage, collectibles do not generate any cash flows, and the only profit is comes upon selling.
Furthermore, collectibles can be rather expensive. Finally, due to the lack of transparency of
the collectible markets there is a chance that the investor would have purchased a counterfeit
good, instead of the real thing, thus rendering the investment completely worthless. All these
risks associated with alternative investments, has rendered them rather unwanted in the
portfolio.
In the light, of the crisis of 2007 - 2008 it was seen that traditional alternative
investments such as real estate, tend to disappoint the investors just as some of the stocks and
bonds during a financial turmoil. Therefore, popularity of collectible assets has increased
substantially after the crisis of 2008 (Deloitte Art & Finance Report, 2011). Due to this
increased recognition and interest of collectibles, their respective markets are experiencing
growth and transformation. According to another study by Deloitte (2014) on alternative
investments, the need for them is steadily rising, as investors seek new opportunities to
invest, and markets respond with such.
Another type of trend worthy of mentioning is the "SWAG" investment. "SWAG"
being an abbreviation of Silver, Wine, Art and Gold investments. Roseman (2012) in his
book (titled "Silver, Wine, Art and Gold: Alternative Assets for the Coming Decade") , refers
to these four alternative assets doing very well in the upcoming decade, which by his words,
might be quite turbulent. The main argument made by Roseman is that these assets have the
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ability to act as pseudo currencies, and also being more stable then fiat money, as these
particular assets cannot increase in supply by a simple "press of a button". Furthermore, Saft
(2012) supports some of the statements by saying that these assets can bring diversification
benefits to a portfolio, due to the desirability, limited supply and relative price independence
from the stock market.
Due to the desirable characteristics of wine, such as low correlation with traditional
markets which give a diversification benefit and higher than expected returns (Masset 2010),
interest in wine as an investment asset class has only been growing. According to Turan
(2013) over the last forty years red Bordeaux wines have outperformed the majority of
financial indices, and in the recent ten years the prices of these wines have risen for more
than over one thousand percent. To continue with, auction houses are expanding their
presence not only in Europe and the United States, but also to new regions, especially Asia,
showing an increase in the number of auctions held and transactions which take place
(Masset & Weisskopf 2010). To continue with, The International Spirits and Wines Research
Organization (IWSR) in one its global market overview of 2013 has stated that demand for
investment grade fine wine in Asian countries has risen by 40% and is expected to rise up to
55% in the upcoming few years, while global wine consumption is said to increase in stable
rate of above 6%.
As investors are constantly searching for different types of alternatives assets to better
diversify their portfolio risks, and generate higher rates of return, their attention especially in
the recent decade has been turned to wine. This may be the result of developments in the
wine market in the recent years and also due to the characteristics of wine as an investment
asset. Wine is said to provide a hedge against risk and to be negatively correlated with the
traditional assets.
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Exposure to wine market can achieved in three ways. Firstly, direct investments into
wine, by purchasing bottles, or barrels of wine. Secondly, En primeur investments, by
purchasing wine which is yet to be bottled at a discount price, in hopes that it will appreciate
once bottled and thus generating a premium. En primeur investments are to some extent
similar to futures. Finally there is the opportunity to invest in wine indices. This is probably
the most liquid form of wine investment investments.
Thus the main contribution of the following thesis would be to analyze the
diversification, risk and return benefits together with any implications of including wine into
a conventional investment portfolio. The second objective, would be to compare wine to the
traditional investment assets such as: gold, silver, oil, coffee and crop, in order to see if wine
deserves the same place in a portfolio, as do the mentioned traditional commodities. Finally
composing different investment portfolios in order to analyze if adding wine to a portfolio is
beneficial in the sense of risk diversification, generation of higher returns or for inflation
hedging purposes.
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1. LITERATURE REVIEW
By definition, an alternative investment is any asset class that falls outside the
traditional classes of stocks, bonds and cash. Thus, in other words, investing in anything else
than the three previously mentioned dominant classes, can be considered an alternative
investment. This is a rather broad definition and encompasses quite too many offerings for
one to follow. Robinson, Schneeweis, Weiss and Yau (2007) offer a more compact
classification of alternative investments by categorizing them into a few major groups. To
begin with, authors separate alternative investments into two larger groups: traditional
alternative, such as: real estate, private equity and commodities, and modern alternative
investments: managed futures, hedge funds and distressed securities. These are in turn, split
into respective smaller sub-groups.
1.1 R EAL ESTATE
One of the oldest and popular alternative investment assets, due to its size and the
potential returns would probably be the real estate. A tangible, immovable asset, which can
be owned in various forms - private, public or even financed through equity of debt. It has
become an unquestionable investment opportunity that can be tailored to goals of investors of
all sizes. The main rationale for real estate inclusion into a traditional multi-asset portfolio
tends to be the benefits of risk diversification, inflation protection, low volatility and finally
return enhancement characteristics (Bond & Chang, 2012).
Real estate is divided into three main types. First type - commercial real estate, which
is used entirely for business purposes such as malls, hotels, warehouses, cafes, restaurants
and office towers. Second type - industrial real estate, is used for the purpose of facilitating
manufacturing processes. Finally, the third type - residential real estate, as the name suggests
used for living purposes.
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To continue with, when considering real estate as an investment, the prices can no
longer provide a sufficient statistic to reflect the market value, due to the illiquidity of the
property itself and adjustment of the price to the time needed to sell the property (Baker &
Filbeck, 2013). In real estate markets, this clearing comes from a mixture of prices and
quantity. This can be easily illustrated with an example. When an owner wishes to sell his
property he must place a listing, but the fact that he isn't the only owner in the market wishing
to sell his property, results in a sales/listing ratio. The mean prices only apply to the houses
that actually sell, while the effective price comes from the mean price and the sales/listing
ratio (Baker & Filbeck , 2013).
The recent financial crisis of 2008 has crippled the returns on real estate and many
investors started questioning it's eligibility to be held in a multi-asset portfolio. Though
according to authors as Fisher and Simons (2012) real estate should not be underestimated as
an investment asset and it would be a poor choice to decrease the amount of it in an
investment portfolio.
To begin with, according to Fisher and Simons, the fundamental characteristic of real
estate of being negatively correlated with traditional assets is said to have not changed. The
bond and stock markets have done rather well in the recent years, while real estate has
showed poor results, this reinforces the statement of negative correlation between the two.
Authors further point that such factors as unexpected changes in inflation, interest and
exchange rates historically had had different effects on both real estate and the traditional
markets, and in their eyes there is really no cause for this negative relationship to change.
However, Fisher and Simons point out that the quality of adequate risk adjusted returns that
direct investments in real estate had enjoyed historically might be justified on the basis of risk
factors that were not fully captured in the mean-variance framework and were not adequately
measured, which might be the primary reason explaining why real estate has outperformed
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stocks and bonds on the risk adjusted basis. These risk factors include legal complications,
illiquidity and agency problems in the ownership structure. Authors state that although some
adjustments must be made in the way that the risk level of real estate is measured, there is no
reason at all to believe that the overall riskiness of this asset class will experience an increase,
and in fact point out that as the transparency and liquidity of this asset increases as more
information becomes available, real estate may even decrease in riskiness. Another point
made by Fisher and Simons is that though value of real estate has experienced a period of low
rates of return, the current market value of the property is expected to provide a rather
competitive risk - adjusted rate of return to investors who purchase the property today.
Finally, the third quality emphasized by Fisher and Simons is the inflation hedge. Real estate
has shown to be positively correlated with inflation, meaning that it preserves the real rate of
return.
The previously mentioned points by Fisher and Simons are reinforced by Ross and
Mancuso (2011) whom also add the factor of low volatility that real estate seems to
demonstrate. In their words, real estate demonstrates a lower volatility due to two reasons.
Firstly, real estate investments are rather long - term in nature, with only gradual adjustments
to the market, therefore resulting in a rather stable return pattern. Second being that the
returns from real estate are dependent on periodic asset valuations and do not exhibit similar
levels of volatility as the returns on assets listed on exchanges.
To conclude, though real estate has started as a niche investment sought ought by only
a few, now it offers a large array of different investment products and caters to a wide
audience of investors with varying portfolio goals and constraints. In spite, of recent
downturn in returns during the global financial crisis of 2008, real estate seem to continue to
offer a competitive edge, with low volatility, inflation hedging, diversification benefits and
return enhancement, which should only increase in the future (Fisher & Simons, 2012).
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1.2 Real Estate Investment Trusts
Real estate investment trusts (REITs) were developed as a passive investment vehicle
to obtain the same benefits with real estate as mutual funds provide for investors in securities,
thus making real estate investment opportunities available not only for large investors, but
also for investors who would otherwise lack the required amount of capital to invest into real
estate directly.
REITs are clearly an important source of liquidity for investors wishing to either
develop or own real estate. Since REITs are traded through stock markets, the liquidity of the
market should suggest that any new information that would have an impact on the intrinsic
value of the property, should be rapidly reflected by the stock prices and returns of the REIT
as is the case with stocks. Though, as Baker and Filbeck (2013) point out, this is not the case,
due to the effect of the innate illiquidity of the real estate market, which stalls the information
and incorporates it into the returns of the REIT only after a prolonged period of transactions
and valuations.
Baker, Filbeck and Case mention a, rather unique characteristic of real estate smoothing of measured historical series. This is done because of the infrequent transactions
of differentiated products. Real estate data is also subject to two different types of smoothing.
Firstly, the illiquidity smoothing which is done because of the fact, that transaction
occurrence over a discrete period, generally ranging from one month to one quarter have to
be aggregated for the sole purpose of producing a respective index for that period. However,
due to the liquidity of REITs, their return is measured with the last transactions of each
period. And another type of smoothing - the appraisal smoothing is done for a few reasons,
first it is due to the fact that only a part of the assets in a property portfolio undergo valuation
at any given quarter, this may result in the usage of outdated appraisals when valuing other
assets (Case, 2013).
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To continue with, REITs offer a much higher (stock like) liquidity, when compared to
the direct investments in property markets. As REITs are traded through broad-market
instruments on the stock market, their prices might be affected by factors and information
which are completely unrelated to the underlying property market, for example trades
completed through such instruments as exchange traded funds (Case 2013).
In summary, REITs provide highly liquid exposure to the real estate market. The fact
that REITs are traded through stock markets results in short term returns as being unrelated to
the underlying real estate market. Yet this is only the case with short term returns, as shown
by Case (2013) long term returns are in close correlation with other types of unlisted real
estate investments and also a low correlation with the stock market returns. Looking from a
historical perspective, the data on REIT returns show that it is a superior investment to
unlisted real estate property (Baker & Filbeck 2013). Furthermore, volatility for unlisted
investments cannot be adequately measured as it is cloaked by both, the process of appraisal
and liquidity smoothing, while the volatility for REITs can be measured to full extent. To
continue with, REITs have a much higher level of transparency which leads to higher market
discipline and reduced capital costs. Finally, the distribution requirements that REITs must
adhere to (at least ninety percent of taxable income must be distributed to shareholders)
removes the abundance of free capital which executives in some cases tend to use in order to
make investments that may not be in the investors best interests and not consistent with
intended trading strategy.
1.3 Private Equity
Private equity consists of unregistered equity and equity linked securities which are
sold by both public and private companies to capable financial buyers. Such buyers would
include high net worth individuals and institutional investors commanding substantially large
funds, which are able to commit large sums of money for lengthy periods of time. These
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Investments are made directly into operating companies, through equity securities and debt
(British Private equity and Venture Capital Association, 2008). There are four main types of
private equity: venture capital, buyout funds and distressed debt investing, all of which shall
be discussed in greater detail. All of these types of investments provide capital to companies,
be it for the reason of product development, strengthening of the balance sheet, expansion
purposes, or either management and ownership restructuring.
1.4 Venture Capital
Venture capital is a type private equity capital, that is commonly provided to high potential companies in their early stages of development, in hopes of generating a substantial
return through future sale of the company itself or either the stake in the company, through an
initial public offering. Majority of venture capital projects are short to medium term projects,
ranging from five to over ten year periods (Institutional Advisory Service Group, 2014). The
process of raising capital can take up to several years and is done through investment funds,
which pool the money from limited partners. Once the required amount has been raised, the
fund accepts no further investments, and begins investing. Moreover, these funds tend to have
a fixed life span of around ten years, in some cases there are possible extensions. After that,
the fund begins the management of the portfolio of assets that it has acquired during the
decade of investments.
Venture capital investments require a lot of expertise not only in the sector and
company vise, but also in managerial and technical skills (IASG 2014). To continue with, the
compensation for venture capitalist comes in two sources. First, are the management fees,
which are annual payments, that are calculated percentage vise, depending on the capital
invested. Second source is the carried interest, which is a share of the profits that the fund
generates, this works as an incentive to the fund managers. Finally, venture capital investors
have an advantage over the typical investors.
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By investing directly into a developing company, venture capitalists tend to play a
role in the management of that company, this leads to a more efficient management, due to
the expertise of the professionals working in the investment fund. This fact also reduces the
amount of information asymmetries (Vanacker & Manigart, 2014).
An investor should be careful though, as these funds invest mostly into promising
startup companies, which are rather risky investments. According to statistics provided by
Stanger (2013) the success of a startup company ranges somewhere between twelve to thirty
percent. This information is reinforced by Blodget (2013) who provides the statistics of over
ninety percent of startups eventually failing.
1.5 Buyout Funds
Buyout funds are to some extent similar to venture capital investment funds, but while
venture capital investments commonly involve smaller cap (start up) companies, the buyout
funds are all about investments into large companies for growth related perspectives. Due to
this reason, buyout funds are considered to be less risky, than venture capital investments
(Ljungqvist, Richardson, & Wolfenzon, 2008). Just as venture capital, buyout funds also pool
money from investors in order to acquire not only a stake in the company, but rather the
whole company, for the purpose of restructuring and then selling at a premium to the original
price of acquisition. The fund is structured similarly to a general partnership, but instead of
having additional general partner, the outside investors become limited partners (LPs) and the
managers of the fund, are the general partners (GPs) (Rauch & Wahrenburg, 2010). As in all
general partnerships, GPs are the ones with authority and run the fund, while having
unlimited liability. The LPs on the other hand, have no say whatsoever in the management of
the fund, and have limited liability.
During and after the financial crisis of 2008, the buyout industry experienced some
the most challenging times and continues to struggle to this day, even though the worst
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periods of the crisis have already passed (Wahrenburg & Rauch 2010). The overall economic
downturn caused by the subprime financial crisis of 2008 negatively affected potential
investors by reducing their potential capital. Moreover, it caused distress in the world
banking system, that in turn led towards reduced lending to customers, making buyout funds
unable to pool enough capital for investments. Lastly the financial regulation tightened after
the financial crisis in 2008. According to Dodd-Frank Act, banks were no longer allowed to
invest into buyout funds (Wahrenberg & Rauch). How all of these changes will affect the
future of buyout funds is yet to be seen, but as the latest evidence suggest, buyout funds have
began adapting their business model in order to cope with these difficulties (Baker &
Filbeck).
1.6 Distressed Debt Investments
Distressed debt investment is a form of alternative investment. A form in which high
net worth individuals and institutional investors invest into significantly discounted company
securities or government bonds which are under distress and heading towards bankruptcy.
This is done in hopes of realizing high returns in the case if those entities do not default or go
bankrupt. Acquisition of such bonds pose high levels of risk, due to the high probability of
the company or government going under and rendering the whole investment worthless
(Barclay Hedge 2013).
To continue with, investments in distressed debts is a highly lucrative and risky
undertaking. First point to consider, would be the ability of the investor to analyze the
financial situation that the company under distress is in. At times, investors may be limited
only to the publicly available information, which may cause an inadequate valuation of the
company's ability to improve its current state of operations, leading to an increased risk
investment. Moreover, the distressed debt investors have the opportunity to purchase two
types of debt: secured and unsecured. Though, when considering the characteristics of the
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two, there is a chance that neither of them will be repaid, as they usually have too low of a
priority when considered in respect to other debts that the entity may have, for example the
senior debt. When buying the secured debt, there is only a slightly higher chance that the debt
will be repaid, as it is covered by the collateral. The collateral though, will first cover the debt
instruments which again, rank higher than the secured debt of distress investor (Harner,
Martin, Singer, 2012).
Distressed debt investment can be narrowed down into two types of investment
strategies: passive and active. Passive strategy can be characterized by investors frequently
changing debt positions, when new information in the market becomes available. This type of
investors tend to resell the debt when the price is higher, than the one initially paid, or either
holding the debt until the company's bankruptcy distribution is made. Moreover, passive
investors minimize risks by diversifying among a number of distress investments (Harner,
Martin, Singer). Active investors on the other, ten to invest into a company with a goal of
assuming control, or at least enough influence in the company. This type of investor has a
goal of restructuring the company and steering it out of bankruptcy, by offering solutions
based on their previous experience in distressed situations; by doing so, they hope to increase
the value of their initial investment in the company (Rosenberg 2000). Furthermore, as
pointed by Harner, Margin and Singer, distressed debt investors tend to disrupt and prolong
the bankruptcy cases in hopes of receiving rather minuscule rewards.
1.7 Commodities
Commodities are tangible assets, such as raw materials, precious metals and
agricultural goods. They give the investor an opportunity to invest into a material, which is in
turn, used in production of other goods. The demand and supply of commodities tends to vary
during different periods of the year. In some cases commodities also tend to have a seasonal
component (exp. agricultural goods). Commodities are separated into two main groups: hard
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and soft. Soft commodities are usually the ones that are grown, in contrast of hard
commodities, that are mined. Another important quality of commodities is storability. It is
important, because some of them might be stored and by doing so negate the affects of
seasonality to some extent. Finally, commodities do not earn income, therefore, the return can
only be measured upon selling the commodity.
Due to the specific characteristics of commodities, they offer a diversification benefit
when compared to traditional investment instruments, such as bonds and stocks (Rotblut
2013). Paper by Chong and Miffre (2010) examines the performance of commodities during
the period of 2007 - 2008 financial crisis and find evidence that commodities bring the most
diversification benefit during a period of economic turmoil. Moreover, the benefits of
diversification seem to be more pronounced during long - term periods, and are rather
unnoticeable in short - term (Magoon 2013). Rotblut also points out that commodities tend to
have low correlation with traditional assets, while exhibiting high correlation with inflation.
Therefore, there is an opportunity to use commodities as an inflation hedge.
There are several possible ways of investing into commodities. Each of them has both
risks and benefits associated with it. The most simple and straightforward way is the purchase
of the tangible good itself. In doing so, an investor must first be able to identify the quality of
the tangible good. This can sometimes be problematic, as the investors may lack the required
knowledge about the product (for example the quality of wheat). A solution to this might be
to hire an expert in the field, though this will surely cause additional expenses, decreasing the
returns generated by the asset. Moreover, one must consider the storagibilty of the good and
storage costs associated with it. In some cases, investors might have to consider an insurance
policy purchase.
However, in order to avoid the additional costs of, hiring an expert, storing and
insuring, the investor can choose to invest directly into a company that has its core business
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21
associated with the particular commodity. These can include, mining, oil, gas companies, etc.
First thing to keep in mind when buying a stock of a company, rather than commodity itself,
is that the returns no longer depend only on the commodity, but also on the stock market.
Another thins is the addition of systematic industry and market specific risks (Rotblut 2013).
A possible way to avoid purchasing commodities directly or having a stake in a
company is an ETF or either an ETN investment. ETF and ETN track the price of the
commodity without the risks associated with stocks. An ETF tracks the price of either a
commodity or a basket of commodities, but is commonly not backed by the particular
commodity. On the other hand, an ETN mimics price movements of either single commodity
or a basket of commodities and is backed by the issuer.
To continue with, a simple, low - cost way of investing into commodities can be
achieved through mutual funds, which offer a well balanced diversification, because fund
managers spread the portfolio among a variety of different assets (Rotblut 2013).
To continue with the most popular type of commodity investments is the commodity
futures market (Devcic 2012). By investing in futures market, investors gain exposure to
commodity markets by taking a long position and agreeing to buy or sell the chosen
commodity at predetermined price on the set future date. Companies use these contracts to
hedge against severe price movements in either direction and lock in the price. The
advantages of futures are that due to leverage, an investor is able to gain a substantially large
amount in relatively short period of time, but again this acts as a double edged sword,
because the investors might lose a substantial amount in similar period as well. Moreover, the
futures market can at times become very volatile, thus rendering all potential investments
highly risky, especially for inexperienced traders (Devcic 2012).
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1.8 Managed Futures
Managed futures is a form of alternative investment strategy in which experienced
money managers known as Commodity Trading Advisors (CTAs) use futures and options
contracts in managing investment portfolios of their clients. Moreover, advisors which
manage pooled funds instead of previously mentioned single client portfolios, are referred to
as Commodity Pool Operators (CPOs).
In order to mitigate the portfolio risk of their clients, CTAs and CPOs employ
different investment strategies and styles over a large variety of asset classes. Investing can
range from different types of commodities and government bonds to various indices
(Accomazzo 2007). Furthermore, managed futures have a high level of transparency, due to
the fact that they are highly regulated by two large entities: the National Futures Association
(NFA) and the Commodity Futures Trade Commission (CFTC). To continue with, CTAs and
CPOs must not only undergo thorough background checks before they are able to manage
portfolios of clients, but also annual audits of their financial statements (Summa 2011).
Moreover, individuals who choose to invest into managed funds, have the ability to track
their own accounts and view all trading done daily.
CTAs and CPOs in most part use two different investment strategies: trend following
and mean - reversion (Accomazzo 2012). The major part of managers can be classified as
trend followers, these managers tend to follow lasting trends, while other type of managers,
use mean - reversion and try to exploit arbitrage opportunities between markets, investment
instruments and different time periods (Summa 2011). The benefits of investing into a
managed fund can be employed by investors with varying levels of capital. The minimum
account depending on the CTA, can range from twenty - five to fifty thousand USD, while in
some cases the highest amount can reach as high as five million US dollars (Summa 2011).
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1.9 Hedge Funds
A hedge fund is a form of alternative investment which pools investors money and
employs various investment strategies in order to provide a stable rate of return even during a
period of financial turmoil. Holzhauer (2012) points out that hedge funds do not necessarily
seek to reduce market risk by engaging in hedging techniques; some of these funds actually
seek higher returns and willingly accept higher risk, making them rather speculative in
nature. Moreover, are known not to rely on any benchmarks, but rather employ a strategy
called - absolute returns (Baker & Filbeck, 2013). This strategy uses advanced techniques of
investment to earn high returns independently of the overall state of financial markets,
whether they are on the booming, or under a recession. Due to this fact, investing into hedge
funds is available only to accredited investors, with high net worth. This is done to protect the
middle - class investors from engaging into processes, they do not truly understand (Smith
2013).
To continue with, risk reduction is not the only benefit of including hedge funds into
an investment portfolio. According to a detailed research by Hsieh and Fung (2006) where
they examine the returns of over two thousand hedge funds, traditional assets and hedge
funds have a low correlation.
To some extent, hedge funds are not so far apart from mutual funds. The core
difference being regulation. Though it might change in the future, at the time of writing this
paper, of the two, only mutual funds are regulated by the Securities Exchange Commission
(SEC). Due to this reason, hedge funds can engage in wider range of investments. However,
hedge funds are not as liquid, as mutual funds. In most cases investors in hedge funds are not
able to retrieve or sell their shares for disclosed periods of time, that are referred to as "lock
up periods". These are designed for the purpose of overcoming illiquidity associated
problems in the fund.
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Moreover, hedge funds can be classified into various categories, depending on the
strategy employed when investing. Though these strategies are distinct and in most cases are
employed separately, at times a mixture of them is used. Halbert (2014) provides a concise
list of the main types of hedge fund strategies: event driven - investments are made into
stocks in anticipation of movement which are caused by corporate events; global - investing
into equities of different regions; fund of funds - investments are made into other hedge funds
with either the same or differing strategy; macro - rather than benefiting from investments
into a particular type of security, profit is made from overall market shifts which are
influenced by trends and events; managed futures - investments into listed financial futures,
commodity and currency markets; short only - investment into stocks that are ripe for a
downward correction in value; long - only leveraged - investment in securities which are
deemed to have potential in future price; market neutral - simultaneously investing in long
and short portfolios of same sizes in order to exploit marker inefficiencies; market timing investment in different types of assets, while switching between those classes depending on
the economic conditions of the market; opportunistic - a strategy where investor switches
from one style to another, depending on market conditions; sector - the strategies of fund
managers are specific to a particular sector of the market; value - investing into stocks which
are either undervalued or overvalued, based on their worth.
To continue with, due to the nature of lessened regulation of hedge funds, they seem
to suffer from a selection bias (Preece, 2013). Selection bias stems from the ability of hedge
fund managers to choose whether they wish to report performance to databases. Naturally,
only the funds with better performance tend to report, making the data publicly available. To
continue with, hedge funds that are no longer accepting capital from new investors, have no
need to publicly disclose their return data, therefore, making unclear, whether hedge funds
that are actually disclosing information about performance have higher or lower returns that
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25
the ones choosing not to disclose (Preece, 2013). As can be seen, the selection bias posses
difficulties associated with estimations and measurement.
Another type of bias which Preece points out, is called the "backfill bias". In the case
a hedge fund is performing well and the manager decides to disclose the returns of the fund
with a database in order to attract new investments, the fund is set on a waiting period, before
the information can become available publicly. When the database finally publishes the
information about the returns of the hedge fund, any prior periods of performance are
backfilled. This, according to Preece, causes the returns associated performance to become
overestimated.
To conclude, hedge fund managers are known of using various types of strategies in
order to earn high returns, with no regard to market conditions. Also, according to such
authors as Hsieh and Fung, risk reduction and low correlation of hedge funds with traditional
investment assets are some of the main advantages of hedge funds. However, hedge funds
suffer from selection and backfill biases, which tend to distort the data on hedge fund returns.
Also, hedge fund returns tend to exhibit negative skewness and excess kurtosis, meaning
both, that there is a greater likelihood of negative returns and a rather high likelihood of
either very low or very high returns (Fung & Hsieh 1999).
1.10 Collectibles
Another type of alternative investments are the collectibles, or sometimes referred to
as emotional assets. They are included into investment portfolios due to low correlation with
traditional assets (Masset & Weisskopf, 2010) and also positive correlation with inflation
(Dimson, Rosseau & Spanjeres, 2013) thus, making them an inflation hedge. Collectibles
would include a very wide range of goods, for example: rare stamps, antiques, wines, ancient
coins, specific models of cars, manuscripts, musical instruments, diamonds, comic books and
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26
so on. Though these goods may differ to a great extent, they do have some unifying
characteristics.
To begin with, it is the "emotional dividend" that the ownership of such an item
bestows on the owner (Emotional Assets Management & Research, 2009). In other words, it
is the emotional attachment to a particular good, for which a person is willing to pay a
premium, simply to have it. This premium can differ greatly from one person to another.
Secondly, traditional assets, and some of the previously mentioned alternative assets,
tend to generate cash flows, for example in the form of dividends or rents. On the other hand,
collectibles do not share this characteristic. They do however have the aesthetic value. For
example, stamps, paintings and cars, jewelry can be viewed and shown to others, in such way
providing some form of enjoyment, while in possession. Another reason to own exclusive
collectibles, according to Belk (1995) is associated to some extent with vanity, it is the ability
to show other people that you have a good which they cannot afford.
On the flipside, though stocks and bonds do not provide this aesthetic pleasure, they
also do not have any maintenance costs associated with them. The same cannot be said about
collectibles, as they usually tend to require some sort of maintenance to upkeep the condition
of the good. This can range from a simple plastic cover on a collectible card, to a special kind
of room, with moisture regulation and even air pressure (Beattie, 2009). Though special
storing conditions come at a price, one must also consider insurance for some of the higher
value goods. As these are tangible assets, they are subject to natural disasters and overall
effects of the elements. For example, a collection of paintings can be damaged by fire, while
a cellar of wine - by flooding or either an earthquake. Therefore an insurance is usually
mandatory. Theft is also an option, thus some sort of respective security systems must be
installed. Or the asset must be kept in some sort of a bank vault. Not only that the collectibles
produce no cash flows, but they consume capital while in possession.
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27
To continue with, all collectibles have a limited supply. In some cases this supply is
reduced over time, by consumption, destruction or any other way. An example could be with
wine. Let us imagine that only one hundred bottles of a particular vintage were produced. In
the upcoming years after the production, some of them will be consumed, a part of the bottles
broken, and with each reduction in the supply, the price of the remaining bottles will rise.
This rarity is one of the main factors that gives the collectibles their value (Belk, 1995).
However, an investor must be cautious, as collectibles are subject to counterfeiting. This may
lead the investor into a possession of a rather expensive piece of junk. According to Beattie
(2009), even the most skilled appraisers tend to make mistakes, and forgeries make their way
to the collectors.
Also, most types of collectibles are not only quite expensive, but also rather illiquid
assets, with markets that have limited regulation and are still in developing stages (Campbell,
2007). Therefore, it may take a while, before an item will be sold. And in some cases, an
individual wishing to sell such assets, might be forced to seek out an auction or a dealer. To
continue with, the illiquidity of such assets might increase during times of financial distress
of the market (i.e. financial crisis of 2008), due to the fact that the overall amount of available
investment capital diminishes. In addition, investing in collectibles, in most cases requires a
substantial amount of capital. For example, a price of a sought after investment grade bottle
of wine can range from two to six thousand euro (Fraser, 2011). With art, a considerable
threshold is around one million dollars, for this sum an investor can buy something of value
(Hoffman, 2013). Due to such relatively high sums, an investor must have an equivalently
large portfolio to benefit from such diversification.
Day, Favato and Mamarbachi (2006) point that markets associated to collectibles,
have weak equilibrium processes when compared to other traditional assets. Whilst
equilibrium price is rather unknown it is hard to make any valuations about the asset.
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Moreover, the liquidation process of some collectibles, especially wines can take
lengthy time periods. Depending on the size and vintages of the portfolio, it can take
approximately between four to six months (Mcnutt, 1999). This illiquidity problem can be
resolved by investing into wine indices instead of hard assets themselves. Another thing is the
maturation process, in which wine accumulates value. For a high quality, famous wine this
can take from twenty to forty years (L.W. Sanning & S. Shaffer & J. M. Sharratt 2006). In
this period of "hibernation" wine is subject to breaking if optimal storage conditions are not
met. This is yet another risk factor associated with investing into wine directly.
In summary, collectible and traditional assets differ to the extent of generating cash
flows, the degree of market development, having aesthetic value and absence of equilibrium.
Though these are only a few of the differences, collectible assets can still be considered as
financial assets. Firstly, due to the fact that people are buying and selling collectibles around
the world, making them a desired asset. Secondly, the markets of the assets are becoming
more transparent, performance of some collectibles can be traced for a few decades. Finally,
they are traded like other assets, through an over - the - counter markets.
In light of the problems associated with collectibles, wine indices instead of direct
investments into specific wine vintages will be considered in this paper. These indices give
exposure to the wine market with much higher liquidity than the physical acquisition of the
wine and not only do not charge any additional fees for storing or transactions, they are not
subject to many of the risks associated with hard assets.
1.11 Wine
Wine from the prestigious regions has for a long time been a status of wealth and
prosperity and while both its survival and quality are dependent on the capricious nature of
weather, the prosperity of wine has always been subject to the shifting fortunes of the global
economy.
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Wine as an asset has a long and rich history, dating back as far as 6000 BC with roots
of production in the Caucasus region of Eurasia (Berkowitz, 1996). Eventually, reaching the
Balkans around 4000 BC and being celebrated by the Romans. Wine played a pivotal role in
the Roman society, which in turn lead to a wealthy accumulation of literature, on many
various aspects of wine, such as how to properly run a wine farm, accompanied by the
methods of growing yield in different soils, grape storage and finally elements of wine
trading, and the usage of wine as a value storage (Fleming 2001). Some of these oldest
writings "De agri cultura" (Concerning the Cultivation of the Land) by a Roman statesman
Markus Porcius Cato the Elder (234 -149 BC) provide a detailed explanation on how to
profitably run a wine farm and trade wine itself.
Considering more recent literature, the father of economics - Adam Smith, could be
also called the "father of wine economics". In his book "An Inquiry into the Nature and
Causes of the Wealth of Nations" (1776) he does not only write about the famous "invisible
hand" or the "pin factory", but touches on such subjects as wine, with an emphasis on wine
markets in France, which are relevant even in contemporary times (Veseth, 2008). Another
forefather of economics David Ricardo, also touched on the subject of wine, though only as
an example, in order to explain the logic behind free trade and comparative advantage. To
continue with, Carl Marx wrote extensively not only about capital and socialism, but about
wines and vineyards from both investment and security perspectives. He focused the most on
the third largest wine region in Germany - Mosel.
Though wine has a deep history and many literature concerning different applications
and varying aspects of the asset, it is only quite recently, particularly in the last few decades,
that wine's popularity and application as an alternative investment asset has reached a peak,
and thus lead to the emergence of such a distinctive discipline as wine economics.
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30
The few most notable establishments that that emerged in the recent decade would be
the London International Vintners Exchange (Liv-ex), a marketplace for investment grade
wine, established in the 1999. Liv-ex publishes five different wine indices, while at the same
time providing an overall overview of the fine wine market. The indices published by Liv ex include: Liv - ex Fine Wine 50, which tracks the daily movements of the most heavily
traded fifty wines from the top ten vintages of the Bordeaux first growths. Liv - ex 100 is the
leading benchmark in the industry and represents price movements in the top one hundred
wines in the secondary market. The Liv - ex 500 which is a monthly index reflecting price
movements in a much wider market of five hundred most popular wines in the market. Liv ex 1000 index is the most global index from the Liv - ex family of indices and while previous
indices are somewhat focused on the European wines, this index tracks one thousand
different wines across the world, including both the "Old World wine regions" such as
France, Germany, Italy, Austria and the "New World wine regions": The Americas, Australia
and South Africa. Finally, the Liv - ex Fine Wine Investables is by far the oldest index in the
Liv - ex family of indices, starting from 1988. It tracks around two hundred different wines
from twenty - four top Bordeaux chateaux. The purpose of this index is to mirror the
performance of a typical wine investment portfolio.
To continue with, another establishment worth mentioning would be the American
Association of Wine Economists (AAWE), which publishes the "Journal of Wine
Economics", a scholarly journal, which encourages and communicates wine research. What is
more, AAWE catalogs the majority of academic and research papers available on wine as an
alternative asset and also provides many working papers by itself. Lastly, it provides an
overview about wine economics through media reports.
Another point worth noting is the wine obsession that is currently taking place in
Asian market. Chinese continue to show unprecedented interest in Bordeaux wines, and are
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31
prepared to pay hefty amounts of money to acquire this luxury asset (Ross, 2013). Traditional
and the most renowned wine producing countries such as France, Italy and Austria are
increasing the prices of wine and shifting their attention towards Asian markets, while to
some extent divorcing their traditional markets such as Europe and USA (Ross, 2013). This
can be explained by the fact that Chinese GDP from 2008 until 2013 has been growing by an
average of 13.6 percent, with fluctuations, reaching a peak in 2008 of 18.1 percent and
dropping to the lowest of 8.6 percent in 2009, then picking up again in the following years
(World Bank, 2014). To continue with, as recently as 2000, only around 4% of urban
households in China were classified as being middle - class, while in 2014 it has soared and
now around 45% of China's total population are classified as middle - class and is currently
larger than the entire population of the United States of America (Barton, 2013). Barton adds
that in the period of around ten years, this number of middle - class consumers is projected to
increase by around 170 billion. Furthermore, according to a research conducted by Mckinsey
& Company (2013), these urban middle - class consumers will earn from 60 000 to 230 000
renminbi (9 000 to 34 000 USD) annually. As this happens, new generations of Chinese,
being more globally minded, will exercise an enormous influence in the global market.
The increase of attention in wine as an investment asset could be in most part
associated with the never-ending search for the optimal portfolio together with the benefits of
diversification and maximization of expected returns (Markowitz, 1952). Secondly, the
perception of an asset as being traditional seems to change with time. Best examples being
real estate and the commodities, such as gold, silver and oil, which first were considered as
alternative investments, but eventually increased in popularity and found their way into the
ranks of traditional assets. Finally, collectibles such as wine are enjoying and increased
awareness is because they have a tendency to retain their value or even appreciate as inflation
WINE AS AN ALTERNATIVE INVESTMENT ASSET
32
rises, and act as an inflation hedge, though at times their value can be unpredictable
(Skidmore, 2010).
A major breakthrough in wine economics was made by an economics Professor Orley
Ashenfelter in the university of Princeton, in the early 1986, when he introduced his work
named - "Liquid Assets - The International Guide to Fine Wines" (1986). In this publication
he took on the wine markets with a quantitative analysis approach. One of the main purposes
of this paper, was to determine the price of the fine wine, and the quality of a vintages
coming from varying regions (Ashenfelter, 1986). In the paper Ashenfelter presented the
econometric model which he had derived in order to determine the auction price of red
Bordeaux wines. The model itself was composed of the price of a wine portfolio as a
dependent variable, and wine's age together with various weather data as independent
variables. This model has proven to be staggeringly effective in determining the prices of red
Bordeaux vintages and also predicting the prices of matured wines, thus, rendering opinions
of critics obsolete (Storchmann, 2011).
The series of Professor's O. Ashenfelter's papers lead not only to the support of the
conclusion that weather conditions are crucial to the success of a vintage, but also to revised
econometric model which he had previously published. Though, the biggest contribution was
the laying of the foundation for wine to be considered as an alternative asset. Furthermore,
results of Ashenfelter's econometric model show that it is in fact more reliable than the
opinions of the famed critics, when trying to predict the quality of red Bordeaux wine. The
best example is presented by K. Storchmann with the 1975 vintage of St. Emilion, a region in
southwest France, famous for its wine. As the time goes, and more information about the
wine becomes available, critics tend to adjust their previously given forecasts, some vintages
are underrated and others, usually the mediocre ones, overrated, which was exactly the case
with St. Emilion, when the wine received close to 100 points, a rating which is considered
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33
extraordinary, together with a suggestion to be stored for a quite a long time, of
approximately 20 years, before being consumed. Long storing periods are another
characteristic of a great wine. But as the time went on, critics decreased the rating to below
the mark of 90 points, a rating of an average wine, and a suggestion to be consumed within a
few years after bottling. On the other hand, the econometric model derived by Ashenfelter,
predicted the mediocrity of that wine, instantly after harvesting (Storchmann, 2011). Though,
that does not mean that the opinions of the critics can be disregarded completely even with
red types of Bordeaux. Such critics as for example Robert M. Parker Jr., or Victor
Morgenroth have an international influence as wine critics and are able to make or break a
vintage. In the words of Berry Bros & Rudd fine wine buying director Max Lalondrelle:
"Nobody sells wine like Robert Parker. If he turns around and says 2012 is the worst
vintage I've tasted, nobody will buy it, but if he says it's the best, everybody will."
This can be reinforced with the Chinese example. When in 2009 R. M. Parker Jr. and
other critics stated that Lafite Rotschild is the best vintage in many years, investors,
especially Chinese flocked and bought the wine without any hesitations, giving little attention
to the prices of particular bottle. It was enough for them that this wine was a symbol of
wealth and status.
Thus, one can see that critics play a crucial role in the future prospects of wine, and
their opinion cannot be completely ignored as they shape the opinion of the majority, and
should be used in conjunction with the quantitative models.
To continue with, an investor has to make a decision what type of wine investment he
would prefer the most, as there are three different types:
Investing directly into wine and buying wine bottles in an auction. When undertaking
this type of investor, one also becomes subject to the risks and additional costs of holding a
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34
wine bottle. Such as: damage while storing the bottle, insurance upon storage, probability of
purchasing a counterfeit wine bottle, illiquidity issues.
En primeur investing is a method of buying wine before it is bottled, prior to official
release of the vintage. Wines tend to be sold at a substantial discount during the period of en
primeur, than they would be after bottling process is finished. Though one must note, that
there is no guarantee that the value of a particular vintage will increase after bottling and in
some case it might even result in a loss of value. En primeur is sometimes referred to as
buying wine futures. Investor becomes subject only to the risks associated with price
fluctuations, as there is no requirement to store the bottles by himself.
Wine indices is yet another type of how an investor may gain exposure to wine
markets. The previously mentioned five different wine indices offer the investor a wide array
of choices without many of the burdens of holding the hard asset itself.
After the previously mentioned Ashenfelter's publications, the sudden increase of
attention was accompanied by a respective increase in literature about wine that sought to
investigate the potential of wine as an investment asset, firstly by assessing risk and returns
associated with it.
Robert Weil in his work, named - "Do not invest in wine, at least in the US, unless
you plan to drink it, and maybe not even then" (1993) Analyzes an existing portfolio of wine,
tracking all purchases and sales in the period of seventeen years (1976 - 1992). In total there
were 68 transactions which were analyzed. Weil takes into account all the costs and fees
associated with storing and selling the wine, and then compares them to investments done in
the Dow Jones index during the same periods. What he finds is that the investments into the
Dow Jones index would have an annualized rate of return of around 19%, while the actual
wine transactions accumulated only up to 6.5% annually. Thus denying the wine as a viable
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35
choice for investors. Another important thing is that the author put a major focus only on the
wines produced in the United States.
Previous opinion is reinforced by Weil (1993) as he analyzes a wine portfolio, with a
thirteen year holding period (1980 - 1992). He also takes into account all the costs and fees
associated with purchasing, storing and selling the wine. The results of his work suggest that
returns to wine are around 9.9%, and increasing by another 1,1% if the whole portfolio is
composed only of Bordeaux wines. The author comes to the same conclusion as in the first
case, returns to wine are much less that those of NYSE stocks, given the same period.
Similar findings are reported in an early paper by Krasker (1979) in which the author
using 137 observations of Bordeaux and Cabernet Sauvignon over a period from 1973 to
1977, conducted a times series analyses of the rate of return of storing wine. Krasker found
no risk premium of storing wine and stated that it is not significantly different from risk free
US treasury bills. It is worth noting that the period under investigation included two
exceptionally bad years for the wine industry, thus almost half the tested period was faulty.
In contrast, Jaeger (1981) with a similar wine portfolio like Krasker's, comes to an
exact opposite conclusion. Firstly, Jaeger had increased the period of observation from four
to eight years (1969 - 1977), by doing this the previously mentioned effect of the two bad
years for wine industry (1973 - 1975) dissipates through the longer period and is not so
roughly felt. Secondly, she added dummy variables, which allowed for every year to have its
own intercept. In addition, Jaeger assumes annual storage costs per box of around $0.5 which
in the period were actual prices published by Freemark Abbey Winery. Krasker on the other
hand, used drastically different prices of around $16.60 annually per box. Jaeger (1981) also
wrote that the $16.60 cost of storing might reflect a financial return to wine storage, which is
not explained by the other variables of the respective model. Thus, to sum up, two key
WINE AS AN ALTERNATIVE INVESTMENT ASSET
36
changes were applied, first, the time period under observation was doubled and secondly,
storage costs substantially lowered.
To continue with, Burton and Jacobsen (2001) use repeat-sale regression in order to
analyze the returns of Bordeaux wines over the period of ten years (1986 - 1996). The semiannual returns of wine portfolios were compared to the ones of financial assets. The Dow
Jones industrial average was used as a benchmark for wine portfolios. The authors found that
the first growth portfolios, outperformed the risk free Treasury bills by around 1% and
underperformed compared to the Dow Jones industrial benchmark by around 6.5%. The only
portfolio that outperformed the benchmark during the same time frame was the 1982 top
Parker rated vintage. Burton and Jacobsen concluded that not only do wines yield a lower
return than stocks, they also carry much more risk. Authors find that the standard deviation
exhibited by the first growth wines is more than two times higher than the one of the Dow
Jones index.
One of the more recent works by Sanning, Shaffer and Sharatt (2008) uses FamaFrench Three-Facto model, and Capital Asset Pricing Model in order to analyze the Bordeaux
hammer prices over the period of 1996 to 2003. The authors found that the annualized rate of
return is around 9%. What is more interesting, is that the covariance of equity markets and
wine returns is close to zero, thus making wine a hedging asset during turmoil in stock
markets.
Previously mentioned finds are backed by Masset, Henderson and Weisskopf (2010)
who analyze wine prices in the period of 1996 - 2009 using the repeat-sales regressions, to
show that wine has lower volatility than stocks and also yields higher returns, especially
during the times of economic downturn. They take different amounts of wine indices and
include them into different portfolios to prove the benefit of diversification to private
investors. They show that not only the returns are positively affected, but also at the same
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37
time the risk is being minimized, together with the skewness and kurtosis which are
positively affected. The beneficial effects of wine inclusion into portfolio are the most
pronounced during the recent financial crisis of 2008. Moreover, the authors show that
correlation between wine and equities varies. Before the recent financial crisis there was no
significant correlation between the two, but after the crisis it changed significantly.
Similarly, another piece of literature is presented by Fogarty (2010) in the paper
"Wine investment and portfolio diversification gains", where he shows that wine does have
lower returns than the standard financial assets, but at the same time wines do provide some
diversification benefit.
Additional support is provided by Masset and Weisskopf (2010) where a repeat-sale
regression is used to show that wine provides higher yields and has lower volatility than
stocks, especially during an economic crisis. They analyze five different types of investors,
from conservative to aggressive, and apply different wines and assets to show that addition of
wine to a portfolio is beneficial in the sense that it improves not only returns, but also
skewness and kurtosis. Finally, authors show that wine returns tend to be unrelated to market
risks, but behave cyclically with the economy.
In conclusion, though wine market was rather underdeveloped a few decades ago, and
it was hard to consider wine as an investment asset and accommodate investment, today the
situation is quite on the contrary. As wine prices under the last decade have appreciated by
more than one thousand percent, it became too expensive to drink. That coupled with wine's
negative correlation with traditional investment assets, and positive correlation with inflation,
made it a sought after addition to the investor's portfolio.
In the recent years, many authors have debated the plausibility of wine as an
alternative asset class. Majority of the authors, such as Masset, Henderson and Weisskopf use
particular types of wines, for example: Merlot, Chardonnay, Mauzac and others, or more
WINE AS AN ALTERNATIVE INVESTMENT ASSET
38
generalized types as: red or white Bordeaux. By doing so, they restrict the investment
portfolio to physical investments, meaning that the investor will have to take into account
different types of additional costs, for example storage and insurance of the goods. Some of
these additional costs are at the core of deciding whether wine is a investment asset. For
example two authors, Jaeger (1981) and Krasker (1979) come to different conclusions about
investing into wine, due to a major difference being the storage costs. Moreover, the
illiquidity issues with wine have been mentioned many times by Mcnutt (1999), Storchmann
(2011) and many others. Though this risk might be less of a concern in the future, due to the
fact of increasing wine demand in Asian market. Furthermore, when choosing a particular
chateaux of wine, some authors, for example Sanning, Shaffer and Wyoming (2006) come
under the need to undergo data smoothing processes, due to missing sales data on some of the
wines that they have chosen.
Thus, the main purpose of this research would be to analyze the potential and possible
implications of investing into wine indices, instead of direct asset investments, in order to
avoid all the aforementioned drawbacks.
2. Research Methodology
The methodological part begins with a mean return and volatility testing. Each
individual indices from all datasets are tested, as these are the two main statistics describing
the potential yield and riskiness of any investment.
Afterwards the least risky asset from every dataset is chosen as a proxy for a risk free asset in order to calculate the Sharpe - ratio. This is done, because Sharpe - ratio
estimates the relationship between risk and return, thus providing a reliable estimation of
assets viability for investment. Sharpe ratio is calculated using the following formulas:
Sharpe ratio = average (d)/standard deviation (d)
d = Return rate - risk free rate
WINE AS AN ALTERNATIVE INVESTMENT ASSET
39
Subsequently, a correlation analysis is applied, in order to test the relationship
between the rates of return of other assets and wine index in respective datasets. After that, a
Granger Causality test is done in order to determine whether and how useful is one times
series in forecasting another. Simply put, to check whether other financial assets are reliable
predictors in forecasting the movement of the wine index. The Granger Causality test is
conducted between the Liv-Ex Fwin index (𝑌𝑖 ) and all the other indices (𝑋𝑖 ) in the respective
sample sub - sets (Global, Europe1 and Europe2) that are lagged from 1 to 6 months, due to
the fact that test results are sensitive to lagged row selection. The financial index is
considered to the Granger Cause of Liw-Ex Fwin index, if at least for one lag, the value of F
statistic falls between the null hypothesis (H0: 𝛽𝑖 = 0) rejection region. If indicator 𝑋𝑖−𝑡
explains the movements of 𝑌𝑖 then it is capable of predicting the movements of Liv - Ex Fwin
index.
Afterwards, the research proceeds with Engel - Granger cointegration test. This is
done in order to investigate any long - term equilibrium relationships between the expected
return of all the financial indices and Liw - Ex Fwin index, in all three sample sub -sets.
Engel - Granger tests the null hypothesis, that the number of cointegrating vectors is less than
or equal to r (rank), where r is 0, 1. The null hypothesis is tested against a general alternative.
In the next stage of research, lagged CAPM is used in order to estimate the effects of
financial indices on wine index with different indicator lags. In the regression equation the
average rate of return of all the indices is used as the independent variable, and the rate of
return of Liv - ex Fwin wine index is used as the dependent variable. Three different models
are formed with different lags. In the first model, the independent variable is not lagged, in
the second model it is lagged by one period (month) and in the third model, it is lagged by up
to two periods (months). By doing this, the effects that alternating lags have on the overall
regression model and the variation of returns of wine index can be evaluated.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
40
A lagged CAPM is chosen in order to address the issue of non - synchronicity of returns
between emotional assets and stocks. As sometimes a low correlation between wine and other
financial indices can be explained by application of previous period prices. The prices of
wine may sometimes be known only after an auction has taken place, as majority of them are
sold through auction houses, and because the wine indices track these prices, they might be
subject to prices from previous periods, and adjust more slowly than the rest of the market.
The Lagged CAPM model is defined as follows:
𝑏
𝑅𝑡 = 𝛼𝑡 + ∑ 𝛽𝑖 ∙ 𝑅 𝑚 𝑡+𝑖
𝑖=−𝑎
𝑅𝑡 - The return of the asset on the period t;
𝑅 𝑚𝑡 - The return of all the indices on period t;
a - The number of prior market returns which are taken into account;
b - The number of following market returns.
Finally, Markowitz mean - variance optimization is used in order to determine the
optimal strategic allocation of the portfolio. Five different investment portfolios are created,
each having different characteristics. Portfolios are assigned with different weights of
financial indices, depending on the constraint that of each portfolio.
The main objective is to create a portfolio which would have the lowest risk and the
highest monthly rate of return, in this way, confirming or either refuting the benefits if
diversification by comparing the results of portfolios.
The latter portfolio should have the highest Sharpe - ratio and meet the weighting
requirements imposed on the portfolio. The index weightings in the portfolio must be greater
WINE AS AN ALTERNATIVE INVESTMENT ASSET
41
than or equal to 0%, meaning that no short selling is allowed and also the size of one
underlying security must not exceed 25% of total portfolio size
For each analyzed portfolio the constraints on weightings were different depending on
the investment type.
In the first portfolio, named "A" equal amounts of indices are used. The investment
portfolio is composed of 5% of each index, while total amount being 100%. This portfolio is
not considered as a potential best choice investment portfolio, and is used only for the aims of
comparing the results.
The second portfolio, named "B" is oriented towards return maximization, as
portfolio's objective function is maximizing. Portfolio restrictions state that the portfolio risk
must be less or equal to the lowest individual asset risk. Asset weights must be greater or
equal to 0, and the total assets must amount to 100%.
The third portfolio is named "C" and is focused on minimizing the risk, as the
portfolio's objective function is minimizing. Portfolio restrictions show that the portfolio
return is to be no less than the maximum return on highest individual asset. Asset weights
must be greater or equal to 0, and the total assets must be equal to 100%.
The fourth portfolio - "D" is focused on maximizing the Sharpe ratio. Portfolio's
objective function is maximizing. Portfolio restrictions suggest that active portfolio weights
must be greater or equal to 0, and total assets must constitute to 100%.
The fifth portfolio - "E" is also focused on maximizing the Sharpe ratio. Portfolio's
objective function is maximizing. Portfolio restrictions suggest that active portfolio weights
must be greater or equal to 0, and the total amount to 100%.
In addition, the portfolio uses constraints for asset weights, which require a greater
diversification of investments. The investment portfolio individual asset weights must not
exceed 25%, and must in total amount to 100%.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
42
2.1 Data Sample Selection
The sample consists of 54 unique variables (35 Stock indices; 9 Bond indices; 6
Precious Metal indices; 2 agricultural indices; 2 Wine indices) (See table 1). For all the
indices historical monthly data has been used. For the purpose of collecting historical
monthly financial data Bloomberg terminal was used.
All of these indices are divided among three different sub - samples: "Global",
"Europe1" and "Europe2" constituting to a total of 20 indices in each. Majority of the
variables are unique to their respective sub - samples (i.e. global indices are unique to
"Global" sub - sample), while other variables (i.e. Wine indices are added to all the sub samples) are added to two, or all three sub - samples (See table 1).
"Global" region consists of 13 stock indices, 3 bond indices, 2 agricultural indices, 1
precious metal index and 1 wine index. It also has the longest analyzed period, consisting of
266 monthly observations under the period of 1992/02/29 - 2014/03/31. This region is
composed of varying size global indices, allowing highest diversification opportunities, as the
investment portfolio can be allocated into many different regions.
"EUROPE1" region consists of 10 stock indices, 6 bond indices, 2 precious metal
indices and 2 wine indices. It also has the shortest amount of observations - 136 (monthly),
under the period of 2002/12/31 - 2014/03/31. The region is composed only from varying
sizes of European indices, offering a somewhat lesser opportunity for diversification, as the
investment opportunities lay only within Europe.
"EU2" region consists of 12 stock indices, 3 bond indices, 2 precious metal indices, 2
wine indices and 1 agricultural index. The analyzed period is 2001/08/31 - 2014/03/31,
consisting of 152 monthly observations. The region is composed of only the indices
associated with European Union Countries, therefore, diversification opportunity is
diminished even more, as any choices regarding portfolio allocation must be done in the
WINE AS AN ALTERNATIVE INVESTMENT ASSET
43
constraints of European Union Member countries. And as EU countries are rather
economically interdependent and a financial crisis in one country would instantly roll over to
other members, there isn't too much opportunity of diversification.
Table 1. Indices used in three different sub - samples
GLOBAL
EUROPE1
EUROPE2
Liv-ex Fwin index
Liv-ex Fwin index
Liv-ex Fwin index
Russell 1000 index
Liv-ex 100 index
Liv-ex 100 index
MSCI World index
Euro Stoxx 50 index
FTSE 100 INDEX
MSCI ACWI index
SWISS MARKET index
FTSE MIB INDEX
S&P GLOBAL 100 index
S&P EU Sovereign bond Index
IBEX 35 INDEX
NASDAQ 100 index
EURONEXT 100 index
AEX-Index
BarclaysCpBdGlbl index
S&P EUROPE 350 index
OMX STOCKHOLM 30 INDEX
Euro stoxx index
Europe 600 index
WSE WIG INDEX
S&P GSCI Prec Met index
Euro stoxx index
BEL 20 INDEX
S&P GSCI Agric index
JP Morgan EU gov bond index
AUSTRIAN TRADED ATX INDX
Dow Jones Ind Avg
Dow Jones Prec. Mtl. Index
OMX COPENHAGEN 20 INDEX
S&P 500 index
S&P/TSX Prec. Mtl. Index
OMX HELSINKI INDEX
HANG SENG index
Euro stocks Lrg index
MSCI EUROPE AGRI index
NIKKEI 225 index
Euro stocks Mid index
DAX INDEX
JPM Global Agg Bond
Euro stocks Sml index
CAC 40 INDEX
S&P Gold & Met index
EU enlrg index
S&P GSCI Prec Met index
JPMorgan US Agg Bond
EU bond 5YR
S&P Gold & Met index
STXE 600 Europe index
EU bond 10YR
EU bond 10YR
Dow Jones Global ex-U.S. index
EU bond 20YR
EU bond 30YR
Asia Pacific index
EU bond 30YR
S&P EU Sovereign bond Index
3. Empirical Results
The results will be presented for each sub - sample separately, while providing a
conclusion of all three sample sub - sets after firstly analyzing all of them.
3.1 "Global" Sub - sample analysis
3.1.1 Rate of return and investment risk of financial indices
Each financial index has a calculated rate of return and volatility. These are the two
main index statistics, that describe the yield and risk of the investment. The results of the
analysis show that the highest monthly rates of return are respectively for NASDAQ 100
WINE AS AN ALTERNATIVE INVESTMENT ASSET
44
index (1.162 %), Liv - ex Fwin index (1.048 %) and Hang Seng index (0.854 %). It is worth
noting that the indices with the highest return also have the some highest risk associated with
them. Hang Seng and Nasdaq 100 indices have some of the highest standard deviations
(respectively 7.6 % and 7.39 %). The most risky index is the S&P Gold & Met index, as it
has a standard deviation of 9.92%, while the least risky index is the JPMorgan US aggregate
bond index, with volatility of 1.107 % In further calculations, in order to estimate the Sharpe
- ratio JPMorgan US Aggregate Bond index is used as a proxy of a risk - free asset, as it has
the lowest risk of all the indices this sub - sample.
As the Sharpe - ratio estimates the relationship between risk and return, it is a reliable
estimate of the most viable investment assets. The research results show that wine index - Liv
- ex Fwin has average risk and one of the highest rates of return, as its Sharpe - ratio is 0.118.
Furthermore, Nasdaq 100 index also has quite high Sharpe - ratio - 0.085. Nevertheless,
investing into two different assets, does not yield the highest returns and lowest risk,
therefore, in this research different investment portfolios with varying characteristics
depending of investment strategies and different levels of risk tolerance will be composed.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
45
Table 2. Mean return, volatility and Sharpe-ratios of all the assets in the "Global" sub sample
Liv-ex Fwin index
Russell 1000 index
MSCI World index
MSCI ACWI index
S&P GLOBAL 100 index
NASDAQ 100 index
BarclaysCpBdGlbl index
Euro stoxx index
S&P GSCI Prec Met index
S&P GSCI Agric index
Dow Jones Ind Avg
S&P 500 index
HANG SENG index
NIKKEI 225 index
JPM Global Agg Bond
S&P Gold & Met index
JPMorgan US Agg Bond
STXE 600 Europe index
Dow Jones Global ex-U.S. index
Asia Pacific index
Mean return
1.0488%
0.6838%
0.5412%
0.5396%
0.6145%
1.1627%
0.5099%
0.6567%
0.6461%
0.0999%
0.6998%
0.6628%
0.8546%
0.1405%
0.5260%
0.6947%
0.5117%
0.6010%
0.4609%
0.4004%
Volatility
4.4011%
4.2762%
4.3203%
4.4144%
4.4513%
7.3870%
1.1437%
5.7842%
4.8854%
5.6117%
4.1718%
4.2271%
7.5971%
6.2143%
1.6510%
9.9224%
1.1072%
5.1027%
4.9045%
5.5796%
Sharpe-ratio
0.1180
0.0374
0.0064
0.0059
0.0216
0.0850
-0.0012
0.0251
0.0266
-0.0705
0.0417
0.0331
0.0442
-0.0567
0.0118
0.0188
0.0168
-0.0097
-0.0186
3.1.2 Correlation analysis
Correlation analysis estimated the relationship between the Liv - ex index and the
rates of return of other indices in the sub - sample "Global".
It was estimated that the strongest correlation is between Liv - ex Fwin and STXE 600
Europe indices. A weak correlation is observed between the two assets, as r = 0.244.
Nevertheless, this relationship is statistically significant, because the error term is p = 0.0001
< 0.05. (See figure 1.)
WINE AS AN ALTERNATIVE INVESTMENT ASSET
46
Figure 1. Liv-ex Fwin index and STXE 600 Europe index
LivexFwinindex versus STXE600Europeindex (with least squares fit)
0.3
Y = 0.00902 + 0.244X
0.25
0.2
LivexFwinindex
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
STXE600Europeindex
A correlation with similar strength can be observed between Liv-ex Fwin and
BarclaysCpBdGlbl (Barclays Composite Global Bond index) indices, r = 0.230, p = 0.0001 <
0.05 (See figure 2). Both STXE 600 Europe and BarclaysCpBdGlbl indices are weakly
correlated with the wine index (Liv-ex Fwin), in the long term, the relationship is reliable.
Furthermore, the rates of return are positively correlated, meaning that when returns of wine
index increase, so do the returns of STXE 600 Europe and BarclaysCpBdGlbl indices. The
weakest correlation of the wine index Liv - ex Fwin can be observed between S&P Gold &
Met index and JPM Global Agg Bond indices. The correlations are close to 0 , respectively r
= -0.01, p = 0.873 > 0.05 and r = 0.033, p = 0.596 > 0.05.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
47
Figure 2. Liv-ex Fwin index and BarclaysCpBdGlbl index
LivexFwinindex versus BarclaysCpBdGlblindex (with least squares fit)
0.3
Y = 0.00676 + 0.731X
0.25
0.2
LivexFwinindex
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
-0.02
-0.01
0
0.01
0.02
0.03
0.04
BarclaysCpBdGlblindex
It is also worth noting that the rates of return of all the other assets change
independently from the wine index, as the correlation coefficients are less than 0.2, meaning
that there is a weak or very weak relationship between the variables. This weak correlation
means that there is an opportunity to compose a well diversified investment portfolio.
3.1.3 Granger Causality Test
The main reason to conduct the Granger Causality test is to determnine whether and
how useful is one time series in forecasting another. In other words, to determnine whether
other financial indices are reliable predictors, in forecasting the movements of the wine index
(Liw-ex Fwin).
Granger Causality test was conducted between Liv - ex Fwin index (𝑌𝑖 ) and all the
other indices (𝑋𝑖 ) in the "Global" sub - sample, which were lagged from one to six periods
(months) because test results are sensitive to lagged row selection. Financial asset is
considered the Granger cause of the wine index, if at least for one lag, the value of F =
statistic falls between the null hypothesis (H0: 𝛽𝑖 = 0) rejection region. In other words, if
indicator 𝑋𝑖−𝑡 explains the movements of 𝑌𝑖 then it is capable of predicting the movememtns
of wine index.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
48
The results of Granger Causality test show that all indices in the "Global" sub sample, except for Euro Stoxx index, S&P GSCI Prec Met index and NIKKEI 225 Index are
the Granger Cause of Liw - ex Fwin index. Chi - square significance criteria p < 0.05 for
laggs between 1 to 6 months (See appendix 2).
Based on the obtained results, it can be stated that all indices in the "Global" region,
except for Euro Stoxx index, S&P GSCI Prec Met index and NIKKEI 225 index are capable
of predicting the Liw - ex Fwin wine index. It is however, worth noting that Granger
Causality test shows only a mathematical relationship between the parameters, and not the
interactions based on economic logic.
3.1.4 Cointegration testing
A cointegration test was conducted in order to determine whether the financial indices
in "Global" sub - sample are in the long - term associated with the changes of wine index (Liv
- ex Fwin).
The research proceeds with Engel-Granger cointegration test. Cointegration test is
performed to investigate any long-run equilibrium relationships among mean expected return
of all financial equity and wine indices. Engel-Granger test is designed to test the null
hypothesis that the number of cointegrating vectors is less than or equal to r (rank), where r is
0,1. The null hypothesis is tested against a general alternative.
The reported test statistic for the null hypothesis of no cointegration (H0: r =0) in
"Global" sub -sample, rejects the null hypothesis of no cointegration (r = 0) in favor of the
general alternative r ≥ 1, since p = 0.0005 < 0.05 ( see table 3).
Table 3. Engel-Granger cointegration test results
Const
Rm0
Coefficient
0.00854667
0.335174
Std. Erorr
0.00270271
0.0947203
t - ratio
3.162
3.539
p - value
0.0017 ***
0.0005 ***
*** denote rejection of the null hypothesis at the 1 % significance level
WINE AS AN ALTERNATIVE INVESTMENT ASSET
49
This test concludes that there is cointegrating relationship among mean expected
return of all the financial indices and the wine index. In other words, there is statistical
evidence, that the positive long-run impact of all financial equity to wine index return rate is
observed.
3.1.5 Lagged CAPM
In the next stage of the research, lagged CAPM analysis was performed, aimed at
estimating the effect of financial indices on wine index, with different indicator lags. In the
regression equation the average rate of return of all the indices is used as the independent
variable, and the rate of return of Liv - ex Fwin wine index is used as the dependent variable.
During the regression analysis three different models were formed.
In the first model the independent variable is not lagged. In the second model
independent variable is lagged by one period (one month), and the parameter of one month
ahead is evaluated. In the third model, the independent variable is lagged by up to two time
periods (months) and one - month ahead parameter is evaluated. By doing this, the effects
that alternating lags have on the overall regression model and the variation of returns of wine
index can be evaluated.
Based on the results obtained from the models of regression analysis, it can be stated
that the first model is nothing else, but the evaluation of the correlation between the average
rates of return between financial indices and the rate of return of wine index. The results of
the first model show that the estimate is quite low (𝛽 = 0.335, p < 0.05) thus, the
relationship between financial indices and wine index is weak.
Therefore, the possibility to diversify current assets is plausible. Analyzing the
second model, with the independent variable being lagged by one month, it was found that
the relationship between financial assets and wine index is stronger than it was in the first
WINE AS AN ALTERNATIVE INVESTMENT ASSET
50
model ( β = 0.667 , p < 0.05). This suggests that the effects of the financial assets on the wine
index are stronger when there is a one month lag.
Finally, analyzing the third model suggests that the strongest positive impact of
financial indicators on wine remains with one month lag ( β = 0.308 , p < 0.05) (See table 4)
In addition, the impact of financial indicators on the growth of wine index remains positive
and statistically significant with varying lags (from -2 until +1).
Table 4. The regresion results of the effects of financial indices on wine index during
different time periods
Dependent Variable: Liv-ex
b(-2)
b(-1)
Fwin index
Model 1 (a=0, b =0)
Model 2 (a=1, b =1)
0.355*
Model 3 (a=2, b =1)
0.252* 0.308*
*Parameter estimate is significant with, 𝛼 = 0.05.
b(0)
0.335*
0.246*
0.263*
b(+1)
b
R2
Adj.R2
0.067*
0.025*
0.335*
0.667*
0.849*
0.045
0.094
0.121
0.042
0.084
0.108
3.1.6 Markowitz mean-variance optimization
In the following stage of the research five different investment portfolios are created,
each having different characteristics. The rate of return and risk associated with each
portfolio is evaluated. The portfolios are assigned with different weights of assets, due to the
fact that some assets are more risky than the others. The same classification is used when
evaluating the rate of returns of the assets. The main objective is to create a portfolio which
would have the lowest risk and the highest rate of return, this way confirming or refuting the
benefits of diversification by comparing the portfolio with the results of the other four
portfolios. The latter portfolio should have the highest Sharpe - ratio and meet the weighting
requirements imposed on the portfolio.
The index weightings in the portfolio must be greater than or equal to 0%, meaning
that no short selling is allowed and also the size of one underlying security must not exceed
25% of total portfolio size.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
51
For each analyzed portfolio the constraints on weightings were different depending on
the investment type.
Portfolios:

Portfolio A - Equal weight portfolio

Portfolio B - GMVP Portfolio with lowest volatility

Portfolio C – Portfolio with maximum return rate

Portfolio D - Tangency portfolio (Portfolio with maximal Sharpe Ratio).

Portfolio E - Is the portfolio that maximizes Sharpe Ratio (with no short-selling)
under the constraint that weight of each individual asset class (emotional assets,
equity, bonds, commodities and Real Estate) is lower than 25%.
Based on the results of Markowitz theory of portfolio optimization, in the "Global"
region maximum return on investment is generated by portfolio C. The average return on
investment is 1.16% per month. However, the portfolio risk is also the highest (7.39%) of all
the portfolios analyzed. Moreover, Sharpe - ratio is the lowest (0.157) The Portfolio will suit
investors who are willing to take risks. In this case, the portfolio diversification is not
discussed, because there practically is none, almost all the resources (99.9999%) are allocated
into the NASDAQ 100 index and only a very small fraction (0.00009%) is allocated to wine
index (Table 5).
The portfolio "B" is very similar to the equal weight portfolio "A", just that the
volatility is significantly smaller. This should be expected, as the portfolio is allocated into
smaller amount of different indices.
To continue with, portfolio "D" has the lowest return, but also one of the lowest risks,
and the highest Sharpe - ratio (0.70) (see table 6). This portfolio is designed for investors who
are seeking optimal risk and return ratio. Investments are allocated mainly into U.S. Agg
Bond JPMorgan index and BarclaysCpBdGlbl index (respectively, 49.4% and 35.1% of
WINE AS AN ALTERNATIVE INVESTMENT ASSET
52
resources) and fractions in other assets (see table 5). In addition, 2.8% of total resources of
the portfolio should be invested in the wine Liv-ex Fwin index.
The most diversified portfolio is "E". It has the second best Shape - ratio (0.641). It is
only surpassed by portfolio "D" which is not subject to diversification constraints. It can be
stated that the risk and return ratio is one of the most optimal, as the risk level is lower than
of portfolios "A", "B", "C". Though portfolio "D" must still be considered the best overall
investment choice, as the Sharpe - ratio is the highest. Thus diversification with alternative
assets is only partially justifiable.
Table 5. Weightings of different investment portfolios (%) for sub - sample "Global"
A
Equal
portf. Wt.
B
Max return
portf. Wt.
C
Min std. dev.
portf. Wt.
D
Max SR
portf. Wt.
E
Max SR portf. Wt.
x<0.25, x>0
Liv-ex Fwin index
5.0%
14.5%
0.00009%
2.8%
4.8%
Russell 1000 index
5.0%
0.0%
0.0%
0.0%
0.0%
MSCI World index
5.0%
0.0%
0.0%
0.0%
0.0%
MSCI ACWI index
S&P GLOBAL 100
index
5.0%
0.0%
0.0%
0.0%
0.0%
5.0%
0.0%
0.0%
0.0%
0.0%
NASDAQ 100 index
BarclaysCpBdGlbl
index
5.0%
8.2%
99.9999%
2.5%
2.5%
5.0%
21.3%
0.0%
35.1%
25.0%
Euro stoxx index
S&P GSCI Prec Met
index
S&P GSCI Agric
index
5.0%
3.7%
0.0%
1.7%
3.5%
5.0%
2.4%
0.0%
1.0%
2.5%
5.0%
0.0%
0.0%
0.0%
0.0%
Dow Jones Ind Avg
5.0%
0.0%
0.0%
3.1%
5.8%
S&P 500 index
5.0%
0.0%
0.0%
0.0%
0.0%
HANG SENG index
5.0%
0.0%
0.0%
0.0%
0.0%
NIKKEI 225 index
5.0%
0.0%
0.0%
0.0%
0.0%
JPM Global Agg Bond
S&P Gold & Met
index
JPMorgan US Agg
Bond
STXE 600 Europe
index
Dow Jones Global exU.S. index
5.0%
0.1%
0.0%
0.2%
25.0%
5.0%
0.9%
0.0%
0.0%
0.0%
5.0%
48.3%
0.0%
49.4%
25.0%
5.0%
0.0%
0.0%
0.0%
0.0%
5.0%
0.6%
0.0%
4.2%
5.9%
Asia Pacific index
5.0%
0.0%
0.0%
0.0%
0.0%
Indices /Portfolio
types
WINE AS AN ALTERNATIVE INVESTMENT ASSET
53
Table 6. Rates of return, risk and Sharpe - ratios of different portfolios (Global sub - sample)
Exp.
return
Volatility
Sharpe
ratio
A
Equal portf.
Wt.
B
Max return portf.
Wt.
C
Min std. dev. portf.
Wt.
D
Max SR portf.
Wt.
E
Max SR portf. Wt.
x<0.25, x>0
0.60%
0.65%
1.16%
0.55%
0.57%
2.71%
1.11%
7.39%
0.78%
0.89%
0.222
0.5892
0.1574
0.7035
0.641
3.2 "Europe1" Sub - sample analysis
3.2.1 Rate of return and investment risk of financial indices
Analyzing the results from Europe1 sub - sample it can be observed that the highest
monthly return is of Liv - ex Fwin index (wine index) - 1.044%, followed by EU enlrg index 0.95% (This index provides a broad coverage of companies across the ten member states:
Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia,
Slovenia, Bulgaria and Romania). And finally, Euro stocks Sml index with monthly returns
of 0.824% (This index provides a broad coverage of companies across the ten member states:
Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia,
Slovenia, Bulgaria and Romania). The risk of these three indices is substantially small - when
compared to other indices in the same sub - sample, 5.54% , 7.03% , 5.38% respectively. The
most risky indices are: EU 5 year bond index - 22.12%, followed by the S&P / TSX Precious
Metal index - 11.71% and finally the EU 10 year bond index - 9.42%. By investing into these
indices, riskiness is almost two times higher than with the highest return indices. It is worth
noting that over the period of analysis, the EU bonds of different duration: ten, twenty and
thirty years have quite a level of risk: respectively , 9.42% , 6.44% and 6.2 % (see table 7).
The least risky index is the JP Morgan EU government bond index, having volatility of
1.856%. Due to this reason, JP Morgan EU government bond index will be used a risk - free
proxy in calculating Sharpe - ratios.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
54
Table 7. Mean return, volatility and Sharpe-ratios of all the assets in "Europe1" sub - sample
Liv-ex Fwin index
Liv-ex 100 index
Euro Stoxx 50 index
SWISS MARKET index
S&P EU Sovereign bond Index
EURONEXT 100 index
S&P EUROPE 350 index
Europe 600 index
Euro stoxx index
JP Morgan EU gov bond index
Dow Jones Prec. Mtl. Index
S&P/TSX Prec. Mtl. Index
Euro stocks Lrg index
Euro stocks Mid index
Euro stocks Sml index
EU enlrg index
EU bond 5YR
EU bond 10YR
EU bond 20YR
EU bond 30YR
Mean Return
1.0440%
0.7017%
0.3410%
0.6436%
0.2330%
0.4298%
0.4299%
0.4685%
0.4609%
0.4706%
0.4065%
0.8023%
0.3882%
0.7746%
0.8249%
0.9503%
0.7819%
-0.2848%
-0.3268%
-0.2839%
Volatility
5.5462%
5.1685%
5.1263%
3.6033%
3.4984%
4.7028%
4.2802%
4.3097%
4.9971%
1.8563%
8.7977%
11.7187%
5.0257%
5.1069%
5.3846%
7.0370%
22.1269%
9.4245%
6.2029%
6.4461%
Sharpe-ratio
0.0978
0.0419
-0.0234
0.0434
-0.0608
-0.0080
-0.0086
-0.0004
-0.0018
-0.0073
0.0281
-0.0151
0.0558
0.0619
0.0655
0.0136
-0.0749
-0.1270
-0.1168
3.2.2 Correlation analysis
Correlation analysis estimated the relationship between the Liv - ex index and the
rates of return of other indices in the sub - sample "Europe1".
It was estimated that the strongest correlation is between Liv - ex Fwin and S&P EU
sovereign bond index with r = 0.449 , p = 0.0001 < 0.05 (See figure 3), followed by the Liw ex 100 index, with r = 0.394 , p = 0.0001<0,05 (see figure 4), and finally, Swiss Market index
r = 0.264 , p = 0.0001 < 0.05.
Other signifant correlations can are observed between the wine index (Lie - ex Fwin)
and EU 30 year bond, EU 20 year bond, Euro Stocks Large index and JP morgan EU
government bond index, as for all paired correlations p < 0.05.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
55
When analyzing negative correlations between wine indx (Liv - ex Fwin) and other
indices, the most significant are between Liv - ex Fwin and Euro Stocks sml index, with r = 0.12 , p = 0.051 (see table 8).
Figure 3. Liv-ex Fwin index ir S&P EU Sovereign bond Index
y1 versus x3 (with least squares fit)
0.3
Y = 0.00775 + 0.546X
0.25
0.2
0.15
0.1
y1
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
x3
Figure 4. Liv-ex Fwin index ir Liv-ex 100 index
y1 versus y2 (with least squares fit)
0.3
Y = 0.00694 + 0.407X
0.25
0.2
0.15
0.1
y1
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
-0.2
-0.15
-0.1
-0.05
y2
0
0.05
0.1
0.15
WINE AS AN ALTERNATIVE INVESTMENT ASSET
56
It can be concluded that in the "Europe1" subset a larger amount of indices are
positively correlated with the wine index (Liv - ex Fwin), than in the "Global" sub - set,
therefore it will be relatively harder to compose a well diversified portfolio.
3.2.3 Granger Causality Test
Granger Causality test was conducted between Liv - ex Fwin index (𝑌𝑖 ) and all the
other indices (𝑋𝑖 ) in the "Europe1" sub - sample, which were lagged from one to six periods
(months) because test results are sensitive to lagged row selection. Financial asset is
considered the Granger cause of the wine index, if at least for one lag, the value of F =
statistic falls between the null hypothesis (H0: 𝛽𝑖 = 0) rejection region. In other words, if
indicator 𝑋𝑖−𝑡 explains the movements of 𝑌𝑖 then it is capable of predicting the movememtns
of wine index.
The results of Granger Causality test show that in the sub - sample "Eurozone1",
Europe 600 index, Dow Jones Precious metals index, S&P/TSX Precious Metals index, EU
enlrg index and EU 30 year bond index are the causes of Liv - ex Fwin index, because the
Chi - square significance criteria p < 0.05 with laggs up to 6 months (See Appendix 3).
Based on the obtained results, it can be said that all the previously mentioned indices
are capable of predicting the Liw - ex Fwin index. Though as it was mentioned in the
previous analysis - Granger Causality test shows only a mathematical relationship between
the parameters, and not the interactions based on economic logic.
3.2.4 Cointegration Testing
The research proceeds with Engel-Granger cointegration test. Cointegration test is
performed to investigate any long-run equilibrium relationships among mean expected return
of all financial equity and the Liw - ex Fwin index. Engel-Granger test is designed to test the
null hypothesis that the number of cointegrating vectors is less than or equal to r (rank),
where r is 0, 1. The null hypothesis is tested against a general alternative.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
57
The reported test statistic for the null hypothesis of no cointegration (H0: r =0) in
Europe 1 region, fails to reject the null hypothesis of no cointegration (r =0), since p = 0.18 >
0.05 (See table 8).
Table 8. Engel Granger cointegration test results
Const
Rm0
Coefficient
0.00906029
0.118450
Std. Erorr
0.00302512
0.0889728
t - ratio
2.995
1.331
p - value
0.0030 ***
0.1842
*** denote rejection of the null hypothesis at the 1 % significance level
This test, concludes that there is no cointegrating relationship among mean expected
return of all financial indices and wine index i.e. there is no statistical evidence, that the mean
expected return of all financial indices have long-run significant impact on wine index return
rate.
3.2.5 Lagged CAPM
In the next stage of the research, lagged CAPM analysis was performed, aimed at
estimating the effect of financial indices on wine index in the "Europe1" sub - sample, with
different indicator lags.
In the regression equation the average rate of return of all the indices is used as the
independent variable, and the rate of return of Liv - ex Fwin wine index is used as the
dependent variable. During the regression analysis three different models were formed.
In the first model the independent variable is not lagged. In the second model
independent variable is lagged by one period (one month), and the parameter of one month
ahead is evaluated. In the third model, the independent variable is lagged by up to two time
periods (months) and one - month ahead parameter is evaluated. By doing this, the effects
that alternating lags have on the overall regression model and the variation of returns of wine
index can be evaluated.
Based on the results obtained from the first model, it can be stated that as in the
previous sub - sample, the relationship between financial indices and the wine index is weak
WINE AS AN ALTERNATIVE INVESTMENT ASSET
58
and statistically insignificant - 𝛽 = 0.118, p > 0.05. Therefore, the potentiality to diversify is
justified.
When analyzing the results of the second model, with a lag of one time period
(month), it can be asserted that the relationship between financial indices and the wine index
is weak and statistically insignificant - 𝛽 = 0.245, p > 0.05.
To continue with, the same conclusion is made with the third model, when the returns
of financial indices are lagged from -2 up to +1 periods (months) - 𝛽 = 0.364, p > 0.05.
To summarize, the results obtained from the three models, allow the assumption that
the returns of financial assets do not have any statistically significant impact on the returns of
Liw - ex Fwin index, as all the models the assumption of independence between the financial
indices and Liw - ex Fwin index (See table 9).
Table 9. The regresion results of the effects of financial indices on wine index during
different time periods
Dependent Variable: Liv-ex Fwin index b(-2) b(-1) b(0)
Model 1 (a=0, b =0)
0.118
Model 2 (a=1, b =1)
0.089 0.079
Model 3 (a=2, b =1)
0.153 0.065 0.088
b(+1)
0.077
0.058
b
0.118
0.245
0.364
R2
0.007
0.012
0.024
Adj.R2
0.003
0.001
0.009
3.2.6 Markowitz mean-variance optimization
In the following stage of the research five different investment portfolios for the
"Europe1" sub - sample are created, each having different characteristics.
Portfolios:

Portfolio A - Equal weight portfolio

Portfolio B - GMVP Portfolio with lowest volatility

Portfolio C – Portfolio with maximum return rate

Portfolio D - Tangency portfolio (Portfolio with maximal Sharpe Ratio).

Portfolio E - Is the portfolio that maximizes Sharpe Ratio (with no short-selling)
under the constraint that weight of each individual asset class (emotional assets,
equity, bonds, commodities and Real Estate) is lower than 25%.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
59
Based on the results from Markowitz mean - variance optimization, is was
found that the highest monthly rate of return on investment is generated by portfolios
"C" - 0.2376% and "D" - 0.2369%. Both of the portfolios exhibit very similar levels
of risk, 4.874% for portfolio "C" and 4.86% for portfolio "D" (See table 11).
However, it is worth noting that these two portfolios are almost completely
undiversified. Portfolio "C" is allocated completely in Liv - ex Fwin index, while
portfolio "D" is allocated mostly into the same index with only a little fraction being
in S&P Europe 350 stock index.
For risk - averse investors it would be suggested to choose the portfolio "B",
due to it having the lowest risk - 2.09%. The portfolio is rather poorly diversified with
significant part of its assets - 35%, in S&P Europe 350 index, 27.2% in S&P EU
sovereign bond index, 6.2% in Liv - ex Fwin index, and the remainder scattered
among six other indices (See table 10).
The most diversified portfolio - "E" has a Sharpe - ratio of 0.038, and is third
by ranking, left after the portfolios "C" and "D". This portfolio also has average risk
and return, 2,54% and 0,09% respectively. The main advantage of this portfolio is the
allocation of risk and returns. This portfolio would be suggested to investors who are
not willing to accept too much risk and receive relatively low compensation.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
60
Table 10. Weightings of different investment portfolios (%) for sub - sample "Europe1"
A
Equal portf.
Wt.
5.0%
B
Max return
portf. Wt.
6.2%
C
Min std. dev.
portf. Wt.
100.0%
D
Max SR portf.
Wt.
99.622%
E
Max SR portf. Wt.
x<0.25, x>0
25.0%
Liv-ex 100 index
5.0%
0.0%
0.0%
0%
8.6%
Euro Stoxx 50 index
5.0%
0.0%
0.0%
0%
0.0%
SWISS MARKET index
S&P EU Sovereign bond
Index
5.0%
0.0%
0.0%
0%
0.0%
5.0%
27.2%
0.0%
0%
25.0%
EURONEXT 100 index
S&P EUROPE 350
index
5.0%
0.0%
0.0%
0%
0.0%
5.0%
31.9%
0.0%
0.127%
20.8%
Europe 600 index
5.0%
1.2%
0.0%
0%
2.8%
Euro stoxx index
JP Morgan EU gov
bond index
Dow Jones Prec. Mtl.
Index
S&P/TSX Prec. Mtl.
Index
5.0%
0.0%
0.0%
0%
0.0%
5.0%
8.5%
0.0%
0%
4.9%
5.0%
0.7%
0.0%
0%
0.0%
5.0%
0.0%
0.0%
0%
0.0%
Euro stocks Lrg index
5.0%
0.0%
0.0%
0%
0.0%
Euro stocks Mid index
5.0%
0.0%
0.0%
0%
0.0%
Euro stocks Sml index
5.0%
10.0%
0.0%
0%
0.0%
EU enlrg index
5.0%
0.0%
0.0%
0%
0.0%
EU bond 5YR
5.0%
2.1%
0.0%
0%
0.1%
EU bond 10YR
5.0%
0.0%
0.0%
0%
0.0%
EU bond 20YR
5.0%
12.2%
0.0%
0%
12.8%
EU bond 30YR
5.0%
0.0%
0.0%
0%
0.0%
Indeces /Types of
Portfolios
Liv-ex Fwin index
Table 11. Rates of return, risk and Sharpe - ratios of different portfolios (Euro1 sub - sample)
Exp.
return
Volatility
Sharpe
ratio
A
Equal portf.
Wt.
B
Max return portf.
Wt.
C
Min std. dev. portf.
Wt.
D
Max SR portf.
Wt.
E
Max SR portf. Wt.
x<0.25, x>0
0.0285%
0.0440%
0.2376%
0.2369%
0.0983%
3.1417%
2.0983%
4.8745%
4.8607%
2.5431%
0.0091
0.0210
0.048745
0.048745
0.0387
3.3 "Europe2" Sub - sample analysis
3.3.1 Rate of return and investment risk of financial indices
The process of calculating the rates of return and volatility for "Europe2" sub sample, shows that the highest monthly rate of return is exhibited by WSE WIG index, which
WINE AS AN ALTERNATIVE INVESTMENT ASSET
61
is 1,229%. Second highest being the monthly return of S&P GSCI Precious Metals index 0.84%. Finally, S&P Gold & Metal index is ranked third with 0.788% monthly rate of return.
It is worth mentioning that the two indices with the highest monthly returns exhibit some of
the highest risks. S&P Gold & Metal index - 10.82%, while WSE WIG index - 8.96%. The
lowest risk is associated with S&P EU Sovereign bond index, which is 3.38% (See table 12).
Due to the reason of being the least risky in the "Europe2" sub - sample, the S&P EU
sovereign bond index will be used as a risk - free proxy for the purpose of calculating the
Sharpe - ratios.
Table 12. Mean return, volatility and Sharpe-ratios of all the assets in "Europe2" sub - sample
Liv-ex Fwin index
Liv-ex 100 index
FTSE 100 INDEX
FTSE MIB INDEX
IBEX 35 INDEX
AEX-Index
OMX STOCKHOLM 30 INDEX
WSE WIG INDEX
BEL 20 INDEX
AUSTRIAN TRADED ATX INDX
OMX COPENHAGEN 20 INDEX
OMX HELSINKI INDEX
MSCI EUROPE AGRI index
DAX INDEX
CAC 40 INDEX
S&P GSCI Prec Met index
S&P Gold & Met index
EU bond 10YR
EU bond 30YR
S&P EU Sovereign bond Index
Mean Return
0.5524%
0.6262%
0.0555%
-0.1008%
0.3398%
0.0262%
0.6153%
1.2296%
0.1910%
0.7185%
0.7641%
0.3331%
0.4763%
0.6276%
0.1104%
0.8403%
0.7887%
-0.4550%
-0.3921%
0.2028%
Volatility
3.5993%
5.0306%
4.2930%
6.3117%
6.1252%
6.0970%
6.4213%
8.2308%
5.1571%
6.4525%
5.6749%
6.7266%
3.5126%
6.4689%
5.3845%
5.2870%
10.8291%
8.9657%
6.2871%
3.3865%
Sharpe-ratio
0.0815
0.0958
-0.0270
-0.0377
0.0173
-0.0238
0.0538
0.1077
-0.0017
0.0626
0.0822
0.0163
0.0558
0.0537
-0.0131
0.1088
0.0584
-0.0649
-0.0766
-
The Sharpe - ratio calculations reveal that two indices with the highest monthly rates
of return, also have the highest Sharpe - ratios: S&P GSCI Precious Metal index has the
WINE AS AN ALTERNATIVE INVESTMENT ASSET
62
highest Sharpe - ratio of 0.108, and WSE WIG index one of - 0.107. Finally, Liv - Ex 100
ranks third, with a Sharpe - ratio of 0.095.
3.3.2 Correlation analysis
Correlation analysis estimated the relationship between the Liv - ex index and the
rates of return of other indices in the sub - sample "Europe2".
The results suggest that strongest positive correlation is between Liv - ex Fwin index
and Liv - ex 100 index ( r = 0.568, p = 0.0001 < 0.05) (See figure 5). The second strongest
positive relationship is between the Liw - ex Fwin and FTSE 100 indices ( r = 0.367 , p =
0.0001 < 0.05) (See figure 6).
Finally, the third strongest positive correlation is between the Liv - ex Fwin index and
the OMX Copenhagen 20 index ( r = 0.311, p = 0.0001 < 0.05) (See Appendix 1). These
correlations are statistically significant and it can be stated that when the rate of return of Liw
- ex Fwin index is rising so do the ones of FTSE 100 and OMX Copenhagen.
Despite the fact that the relationship might be between the Liv - ex Fwin index and
other financial indices may be weak, they are statistically significant.
Figure 5. Liv-ex Fwin index ir Liv-ex 100 index
x15 versus x14 (with least squares fit)
0.1
Y = 0.00298 + 0.406X
0.05
x15
0
-0.05
-0.1
-0.15
-0.2
-0.2
-0.15
-0.1
-0.05
x14
0
0.05
0.1
0.15
WINE AS AN ALTERNATIVE INVESTMENT ASSET
63
Figure 6. Liv-ex Fwin index ir FTSE 100 INDEX
x15 versus x3 (with least squares fit)
0.1
Y = 0.00535 + 0.308X
0.05
x15
0
-0.05
-0.1
-0.15
-0.2
-0.1
-0.05
0
0.05
0.1
x3
Similarly to the situation in the "Europe1" sub - sample, the correlations among
financial indices and Liv - ex Fwin wine index are stronger in this sub - sample ("Europe2")
than the ones observed in the "Global" sub - sample, thus to some extent, mitigating the
diversification opportunities.
3.3.3 Granger Causality Test
Just as in the case of the two previous sub - samples ("Global" and "Europe1")
Granger Causality test was conducted between Liv - ex Fwin index (𝑌𝑖 ) and all the other
indices (𝑋𝑖 ) in the "Europe2" sub - sample, which were lagged from one to six periods
(months) because test results are sensitive to lagged row selection. Financial asset is
considered the Granger cause of the wine index, if at least for one lag, the value of F =
statistic falls between the null hypothesis (H0: 𝛽𝑖 = 0) rejection region. In other words, if
indicator 𝑋𝑖−𝑡 explains the movements of 𝑌𝑖 then it is capable of predicting the movememtns
of wine index.
The results suggest that in the "Europe2" sub - sample all the financial assets with the
exception of FTSE 100 Index, are the Granger cause of Liv - ex Fwin index. Chi - square
significance criteria p<0.05 with lags from 1 to 6 periods (months) (See Appendix 4).
WINE AS AN ALTERNATIVE INVESTMENT ASSET
64
Finally, based on the obtained results, a statement can be made that all the indices in
sub - sample "Europe2" are capable of predicting the Liw - ex Fwin index. Once again,
Granger Causality test shows only a mathematical relationship between the parameters, and
not the interactions based on economic logic.
3.3.4 Cointegration Testing
The research proceeds with Engel-Granger cointegration test. Cointegration test is
performed to investigate any long-run equilibrium relationships among mean expected return
of all financial indices in the "Europe2" sub - sample and Liw - ex Fwin index.
Engel-Granger test tests the null hypothesis that the number of cointegrating vectors
is less than or equal to r (rank), where r is 0, 1. The null hypothesis is tested against a general
alternative.
The reported test statistic for the null hypothesis of no cointegration (H0: r =0) in
"Europe2" sub - sample, rejects the null hypothesis of no cointegration (r =0) in favor of the
general alternative r ≥ 1, since p = 0.0001 < 0.05 (See table 13).
Table 13. Engel Granger cointegration test results
Const
Rm0
Coefficient
0.000408585
1.55434
Std. Erorr
0.00260093
0.0712223
t - ratio
0.1571
21.82
p - value
0.8754
2.94e-048 ***
*** denote rejection of the null hypothesis at the 1 % significance level
This test, concludes that there is cointegrating relationship among mean expected
return of all the financial indices and Liw - ex Fwin wine index in the "Europe2" sub sample. In other words, there is statistical evidence, that the positive long-run impact of all
financial equity to wine index rate of return is observed.
3.3.5 Lagged CAPM
In the next stage of the research, lagged CAPM analysis was performed, aimed at
estimating the effect of financial indices on wine index in the "Europe2" sub - sample, with
different indicator lags.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
65
Similarly as in the previous sub - samples, the average rate of return of all the indices
is used as the independent variable, and the rate of return of Liv - ex Fwin wine index is used
as the dependent variable. During the regression analysis three different models were formed.
In the first model the independent variable is not lagged. In the second model
independent variable is lagged by one period (one month), and the parameter of one month
ahead is evaluated. In the third model, the independent variable is lagged by up to two time
periods (months) and one - month ahead parameter is evaluated. By doing this, the effects
that alternating lags have on the overall regression model and the variation of returns of wine
index can be evaluated.
Based on the results obtained from the first model, it can be stated that as in the
previous sub - samples, the relationship between financial indices and the wine index is weak,
but statistically significant (𝛽 = 0.309, p < 0.05).
When analyzing the results of the second model, with a lag of one time period
(month), it can be asserted that the relationship between financial indices and the wine index
is weak, but again, statistically significant (𝛽 = 0.276, p < 0.05).
To continue with, the same conclusion is made with the third model, when the returns
of financial indices are lagged from -2 up to +1 periods (months) (𝛽 = 0.268, p < 0.05).
To summarize, the results obtained from the three models, allow the assumption that
the returns of financial assets though have a weak impact on the returns of Liw - ex Fwin
index it is statistically significant (See table 13).
Table 14. The regresion results of the effects of financial indices on wine index during
different time periods
Dependent Variable: Liv-ex
Fwin index
Model 1 (a=0, b =0)
Model 2 (a=1, b =1)
Model 3 (a=2, b =1)
b(-2)
0.259*
b(-1)
b(0)
0.219*
0.178*
0.309*
0.276*
0.268*
b(+1)
b
R2
Adj.R2
0.022
-0.028
0.309*
0.517*
0.677*
0.097
0.153
0.217
0.091
0.135
0.195
WINE AS AN ALTERNATIVE INVESTMENT ASSET
66
3.3.6 Markowitz Mean-Variance Optimization
In the following stage of the research five different investment portfolios for the
"Europe2" sub - sample are created, each having different characteristics.
Portfolios:

Portfolio A - Equal weight portfolio

Portfolio B - GMVP Portfolio with lowest volatility

Portfolio C – Portfolio with maximum return rate

Portfolio D - Tangency portfolio (Portfolio with maximal Sharpe Ratio).

Portfolio E - Is the portfolio that maximizes Sharpe Ratio (with no short-selling)
under the constraint that weight of each individual asset class (emotional assets,
equity, bonds, commodities and Real Estate) is lower than 25%.
Based on the results of Markowitz mean - variance optimization, it can be seen that
the highest monthly rates of return are generated by portfolio "C" and "D", both portfolios are
identical as they are invested into the same index. These portfolios have the rate of return of
0.421% per month and identical riskiness of 6.49% (See table 15 and 16).
When considering the portfolio characteristics of "A" and "B" it can be seen that they
both yield almost the same level of monthly returns 0.206% and 0.211% respectively and
exhibit similar level of risk 3.64% for portfolio "A" and 3.39% for portfolio "B". Thus
obtaining an insignificant edge, when investing in portfolio "B".
Finally, the diversified portfolio "E" yields the best results as its Sharpe - ratio is
almost mirroring the ratio of the maximized Sharpe - ratio portfolio "D". When taking into
account the risk associated with each portfolio, it is obvious that the portfolio "E" is the best
choice (See table 15).
WINE AS AN ALTERNATIVE INVESTMENT ASSET
67
Table 15. Rates of return, risk and Sharpe - ratios of different portfolios (Euro2 sub - sample)
A
Equal
portf. Wt.
B
Max return portf.
Wt.
C
Min std. dev. portf.
Wt.
D
Exp. return
0.206%
0.211%
0.421%
0.421%
0.283%
Volatility
3.64%
3.39%
6.49%
6.49%
4.52%
Sharpe ratio
0.0566
0.0624
0.0649
0.0649
0.0626
Max SR portf. Wt.
E
Max SR portf. Wt.
x<0.25, x>0
Table 16. Weightings of different investment portfolios (%) for sub - sample "Europe2"
A
Equal
portf. Wt.
5.0%
B
Max return
portf. Wt.
40.3%
C
Min std. dev.
portf. Wt.
100%
D
Max SR
portf. Wt.
100%
E
Max SR portf. Wt.
x<0.25, x>0
25.0%
CAC 40 INDEX
5.0%
0.0%
0%
0%
25.0%
FTSE 100 INDEX
5.0%
0.0%
0%
0%
0.0%
FTSE MIB INDEX
5.0%
2.6%
0%
0%
6.5%
IBEX 35 INDEX
5.0%
5.5%
0%
0%
2.9%
AEX-Index
OMX STOCKHOLM
30 INDEX
5.0%
0.0%
0%
0%
7.1%
5.0%
0.0%
0%
0%
9.5%
WSE WIG INDEX
5.0%
0.0%
0%
0%
0.2%
BEL 20 INDEX
AUSTRIAN TRADED
ATX INDX
OMX COPENHAGEN
20 INDEX
OMX HELSINKI
INDEX
MSCI EUROPE AGRI
index
5.0%
3.1%
0%
0%
2.9%
5.0%
0.0%
0%
0%
0.0%
5.0%
0.0%
0%
0%
0.0%
5.0%
0.0%
0%
0%
0.0%
5.0%
8.7%
0%
0%
1.0%
Liv-ex 100 index
5.0%
0.0%
0%
0%
1.8%
Liv-ex Fwin index
S&P GSCI Prec Met
index
5.0%
5.8%
0%
0%
3.4%
5.0%
5.9%
0%
0%
3.3%
S&P Gold & Met index
5.0%
0.0%
0%
0%
0.0%
EU bond 10YR
5.0%
2.2%
0%
0%
0.0%
EU bond 30YR
S&P EU Sovereign
bond Index
5.0%
5.9%
0%
0%
3.4%
5.0%
19.8%
0%
0%
8.0%
Index /Types of the
Portfolios
DAX INDEX
In summary, based on correlation analysis results of all three sub - samples, it can be
seen that the relationship between the rate of retunr of Liv - Ex Fwin and the other financial
assets of the "Global" sub - sample, have a weak correlation. Pearson correlation coefficient
stays under 0.3. On the other hand in the other two sub - samples ("Europe1" and "Europe2")
this relationship is stronger. Part of the correlations go above the 0.3 mark.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
68
Though the correlation between the returns of financial assets and wine index are
significant, they are weakly correlated, in some cases even when lagged, meaning that wine
indeed can be used as an alternative investment and offer diversification from traditional
assets, such as bonds and stocks.
Moreover, in all of the regions a weak, statistically significant long - term correlation
between the returns of wine index and the returns of other financial indices can be observed.
To continue with, the results from the CAPM models show that in the sub - sample
"Europe2" the return rate of the indices has a larger impact on the rate of return of the wine
index, that in sub - sample "Europe1", but smaller when compared to the "Global" sub sample.
For each of the three different sub - samples, five portfolios with different constraints
and objectives were composed. When comparing the most diversified portfolios among these
sub - samples, the highest return and the lowest risk is associated with the "Global" sub sample, as the monthly rate of return of the portfolio is 0.57% with a risk of 0.89%. In
contrast, the worst portfolio is from "Europe1" Sub - sample, with 0.10% monthly rate of
return and 2.54% of risk.
When evaluating overall portfolios among the three sub - samples, it can stated that
the portfolio with diversification constraints (no more than 25% of each asset) and no short
selling restriction, performs on par with portfolios which are designed to maximize the
Sharpe - ratio. From the perspective of risk and rates of returns, these portfolios are some of
the best.
Furthermore, the research showed that in the sub - samples "Global" and "Europe2",
majority of the financial indices are the Granger cause of Liv - Ex Fwin index. While in the
"Europe1" sub - sample, only five indices in total (the Europe 600 index, Dow Jones precious
metals index, S&P/TSX precious metals index, EU enlarged index and the EU 30 year bond
WINE AS AN ALTERNATIVE INVESTMENT ASSET
69
index) are the Granger cause of Liw - Ex Fwin index. Thus, wine index returns cannot be
forecasted as good as in the other two sub - samples.
Also, when calculating mean - variance maximization, the results show that though
some portfolios are concentrating on different stock and bond indices, some varying fractions
of wine index are still included in each and every portfolio. Suggesting that wine indeed has
some of the best risk and return relationships.
4. CONCLUSIONS
The thesis was aimed at verifying the hypothesis of the plausibility of wine as an
alternative investment asset and checking for any implications of including it into a
conventional investment portfolio, composed of stocks, bonds and other more traditional
alternative investments.
In order to answer the research question different types of wine investments were
analyzed (Hard assets, En Primeur, Wine indices) and in turn compared to other more
traditional alternative investments, such as real estate, REITs, hedge funds, venture capital,
commodities, managed futures, private equity, buyout funds, distressed debt investments and
even to other collectibles, such as art and antiquity. Furthermore, the characteristics of risk
and return of wine indices were tested and compared to other indices, including stocks,
bonds, precious metals and agricultural goods.
Firstly, when reviewing existing literature, special emphasis was put on the economic
perspective and financial applications of wine.
The attention for wine as an alternative asset has increased in recent decades, as
investors are searching for more ways to diversify their capital. Wine market has developed,
and now offers different types of exposure to wine markets. These include: Investing directly,
En Primeur, and investments into wine indices. The most plausible way of investment was
found to be through wine index investments.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
70
To begin with, wine indices are not subject to the same factors as direct investment
into wine. To begin with, wine requires particular storage conditions in order for it not to lose
value. These seasonal storage costs, coupled with insurance fees, discourage most investors
from considering wine as an alternative investment asset. Not to mention the direct cost of
purchasing a single bottle, which may vary from only a few hundred to a couple of thousand
euros. Moreover, unless an investor chooses to acquire the most sought after brands of wine,
the investor will have to deal with the illiquidity issues, which are natural to less popular
brands of wine. It may take from up to a few weeks to a couple of months to liquidate an
average sized collection of wine. While wine index is not subject to any of the mentioned
drawbacks.
Another type of exposure to wine markets, is by investing into wine which is sold
before bottling, and referred to as En Primeur. En Primeur is sold at a discount price, and
investor can take a gamble of the wine appreciating after bottling, though is not always the
case. Furthermore, an investor must have specific knowledge about wine to take advantage of
this type of investment and not simply blindly invest.
To continue, wine indices are not subject to smoothing of measured historical data,
due to infrequent transactions, as is the case with real estate. Moreover, illiquidity smoothing
or delayed data incorporation into the index from sales of hard assets are of no concern when
investing into wine indices, unlike when investing into REITs.
According to the results of empirical research, wine exhibits long - term correlation
with traditional assets. Though the relationship is statistically significant, it is very weak, thus
wine can be used to capture diversification benefit against traditional assets. Furthermore,
when lagged by one or two periods (months), majority of the observations do not exhibit
correlations with traditional asset returns.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
71
Granger causality test revealed that in two sub - samples out of three, majority of
financial indices are the Granger cause of wine index and can be used in forecasting the
movements of the index. Caution should be exercised as Granger Causality test shows only a
mathematical relationship between the parameters, and not the interactions based on
economic logic.
Finally, the most significant finding is that including wine index into a conventional
portfolio does improve the risk and return relationship of the whole portfolio. This can be
observed in the portfolios which are constructed during Markowitz mean - variance portfolio
optimization process.
In total there are fifteen investment portfolios divided into three sub - samples. All
have varying allocations of capital in the wine index, meaning that it is has a good risk and
return characteristic.
In total there are six portfolios which are constructed to maximize Share - ratio. Three
of them constrained by the weightings of how much of a single index, percentage wise, can
be added to a portfolio. The constraint is - no more than 25% of single index in the
investment portfolio. And another constraint being no short selling. Other three portfolios are
left to maximize the Sharpe - ratio without any restrictions on the weightings of a single
index which can be added.
Based on the results, portfolios which seek to maximize the Sharpe - ratio and are
subject to constrains, on average achieve similar Sharpe - ratio as the three unrestrained
(Sharpe - ratio maximizing) portfolios. The constrained portfolios provide lesser rates of
returns, but at the same time, have a smaller volatility.
Recommendations for any future working papers on this subject, would be as follows.
Firstly, in the future, as markets of other collectibles, such as art, stamps, antiquity and other
emotional assets become more established, include some of those assets into a portfolio
WINE AS AN ALTERNATIVE INVESTMENT ASSET
72
together with wine. Whether by adding indices or returns data of single assets. This could not
be done at the time that this thesis is written, due to the low level of development of these
alternative asset markets.
Another suggestion, could be investing in an already existing portfolio, instead of
creating one yourself, and analyzing if the return and risk characteristics undergo a positive
change. By analyzing a real life example, much more insights can be gained on how wine
investments interact with different types of portfolios.
This could not be done at the moment of writing this portfolio, due to information
costs and limitations in accessing information about different types of investment portfolios.
The results from Markowitz mean - variance portfolio optimization from the three sub
- samples, show that investments into wine offer some of the best relationships between risk
and the rate of return. Portfolios which were diversified with constraints of no more than 25%
of one asset in total compositions and no short selling, are in general heavily allocated into
the wine index and offer some of the best rates of return for an acceptable rate of risk, while
being on par with other investment portfolios which were designed to maximize the Sharpe ratio.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
73
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evidence from Australia. Journal of Wine Economics , 141-161 .
WINE AS AN ALTERNATIVE INVESTMENT ASSET
78
APPENDICES
Appendix 1. Correlation matrix of Liw - Ex Fwin and other financial indices
Liv-ex Fwin index
Global
sub-sample
Russell 1000
index
MSCI World index
Pearson
Correlation
Sig.
(2tailed)
.129*
.035
.220**
.000
.221**
.000
MSCI ACWI index
S&P GLOBAL
100 index
NASDAQ 100
index
.187**
.002
.024
.703
BarclaysCpBdGlbl
index
.190**
.002
Liv-ex Fwin index
Europe1
sub-sample
Liv-ex 100
index
Euro Stoxx
50 index
SWISS
MARKET
index
S&P EU
Sovereign
bond Index
EURONEXT
100 index
Pearson
Correlation
Sig.
(2tailed)
.394**
.000
.116
.060
.264**
.000
.449**
.000
.096
.121
S&P
EUROPE
350 index
.007
.912
Europe 600
index
.026
.672
S&P GSCI Prec
Met index
.150*
.014
.067
.277
S&P GSCI Agric
index
.152*
.013
.153*
.013
Dow Jones Ind
Avg
.108
.079
Euro stoxx
index
JP Morgan
EU gov
bond index
Dow Jones
Prec. Mtl.
Index
S&P/TSX
Prec. Mtl.
Index
-.030
.629
.034
.120
.052
.141*
Asia Pacific index
.000
.124
.131
.089
.278
FTSE 100
INDEX
FTSE MIB
INDEX
.367**
.000
.020
.805
-.011
.894
AEX-Index
OMX
STOCKHOLM
30 INDEX
.185*
.023
.164*
.044
WSE WIG
INDEX
.191*
.019
BEL 20 INDEX
AUSTRIAN
TRADED ATX
INDX
OMX
COPENHAGEN
20 INDEX
.144
.078
.174*
.033
.311**
.000
.146
Euro stocks
Lrg index
.159**
.009
.022
Euro stocks
Mid index
.084
.174
OMX HELSINKI
INDEX
.144
.077
.035
.572
-.120
.051
.146
.879
-.088
.155
.166*
.041
-.012
.845
-.143*
.020
MSCI EUROPE
AGRI index
S&P GSCI Prec
Met index
S&P Gold &
Met index
.119
-.009
.131
.109
.283**
.000
.075
.224
.119
.147
.211**
.001
Euro stocks
Sml index
EU enlrg
index
EU bond
5YR
EU bond
10YR
EU bond
20YR
.194**
.001
.050
.545
.127*
.039
.212**
.001
.246**
.002
NIKKEI 225 index
JPM Global Agg
Bond
S&P Gold & Met
index
JPMorgan US
Agg Bond
STXE 600 Europe
index
Dow Jones Global
ex-U.S. index
.568**
-.090
S&P 500 index
HANG SENG
index
DAX INDEX
Sig.
(2tailed)
IBEX 35 INDEX
.729
.131*
Liv-ex 100
index
Pearson
Correlation
CAC 40 INDEX
.021
Euro stoxx index
Liv-ex Fwin index
Europe2
Sub-sample
EU bond
30YR
EU bond 10YR
EU bond 30YR
S&P EU
Sovereign bond
Index
* - 10 % significance level; ** - 5 % significance level; *** - 1% significance level.
WINE AS AN ALTERNATIVE INVESTMENT ASSET
79
Appendix 2. Granger causality test for Liv - Ex index GLOBAL sub - sample
GRANGER CAUSALITY TEST FOR Liv-ex Fwin index UP TO 6 LAGS
CAUSAL CAUSAL CAUSAL
DRIVERS
LAG
CHISQF-STAT
FLAG
STAT
RUSSELL 1000 INDEX
MSCI WORLD INDEX
MSCI ACWI INDEX
SAMPPGLOBAL100INDEX
NASDAQ100INDEX
BARCLAYSCPBDGLBLINDE
EUROSTOXXINDEX
SAMPPGSCIPRECMETINDE
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
7.9904
4.3728
2.9106
2.253
1.8357
1.5177
5.939
4.2649
3.1498
2.3535
1.9748
1.6754
6.7127
4.6941
3.4473
2.5817
2.1357
1.8301
6.1953
4.0932
2.7577
2.0721
1.7755
1.5499
6.5126
3.5708
2.6691
2.0219
1.7977
1.588
0.8983
1.9484
2.2855
1.689
1.4346
1.275
0
0.7969
0.4991
0.8584
0.6884
0.5724
0.015
0.1269
0.322
0.3723
0.5638
0.93
8.0819
8.9138
8.9688
9.3287
9.5761
9.5758
6.007
8.6938
9.7057
9.7452
10.3014
10.5709
6.7896
9.5687
10.6224
10.69
11.1409
11.5471
6.2662
8.3438
8.4977
8.5799
9.2619
9.779
6.5872
7.2789
8.2246
8.3721
9.3778
10.0198
0.9086
3.9717
7.0425
6.9935
7.4837
8.0447
0
1.6245
1.538
3.5544
3.5912
3.6115
0.0152
0.2587
0.9922
1.5417
2.9408
5.8681
***
**
**
*
**
**
**
*
*
**
***
**
**
*
*
**
**
**
*
**
**
**
*
*
WINE AS AN ALTERNATIVE INVESTMENT ASSET
SAMPPGSCIAGRICINDEX
DOWJONESINDAVG
SAMPP500INDEX
HANGSENGINDEX
NIKKEI225INDEX
JPMGLOBALAGGBOND
SAMPPGOLDAMPMETINDEX
JPMORGANUSAGGBOND
STXE600EUROPEINDEX
DOWJONESGLOBALEXU
80
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
3.0211
4.2646
2.9732
2.3991
2.1648
2.5591
5.6456
3.7816
2.3742
1.8876
1.5671
1.3106
7.7331
4.2437
2.812
2.1738
1.7608
1.4506
6.7408
3.3212
2.0795
1.4849
1.1826
1.5101
1.3697
1.217
1.2578
1.063
0.9293
0.8008
25.1937
14.7796
8.9554
7.3318
5.8659
4.9082
3.442
4.4546
3.2895
2.7583
2.3865
2.3017
1.2268
1.4125
1.3534
2.1214
1.7692
2.2706
5.8079
4.932
3.6932
2.7081
2.5504
2.1516
4.9593
3.0557
8.6932
9.1615
9.9337
11.2928
16.1467
5.7103
7.7087
7.3157
7.816
8.1749
8.2693
7.8216
8.6507
8.6649
9.001
9.1855
9.1527
6.8179
6.7701
6.4079
6.1484
6.1692
9.528
1.3854
2.4808
3.8759
4.4015
4.8479
5.0525
25.4822
30.1276
27.5951
30.3582
30.5999
30.9682
3.4814
9.0806
10.1363
11.4211
12.449
14.5227
1.2409
2.8793
4.1704
8.7837
9.2293
14.3261
5.8744
10.0536
11.3803
11.2134
13.3043
13.5753
5.0161
*
**
**
*
*
**
**
**
*
***
**
**
*
***
**
***
***
***
***
***
***
*
**
**
**
**
**
*
**
**
***
**
**
**
**
**
WINE AS AN ALTERNATIVE INVESTMENT ASSET
81
2
3
4
5
6
1
2
3
4
5
6
Asia Pacific index
2.5525
1.6162
1.3821
1.129
1.2354
1.9803
2.5966
2.2949
1.7651
1.4151
1.3554
5.2032
4.9803
5.7227
5.8896
7.795
2.0029
5.2932
7.0714
7.3085
7.3817
8.5517
*
*
*
* - 10 % significance level; ** - 5 % significance level; *** - 1% significance level.
Appendix 3. Granger causality test for Liv - Ex index Europe1 sub - sample
GRANGER CAUSALITY TEST FOR Liv-ex Fwin index UPTO 6 LAGS
CAUSAL
CAUSAL
CAUSAL
DRIVERS
LAG
F-STAT CHISQ-STAT
FLAG
Euro Stoxx 50 index
SWISS MARKET index
S&P EU Sovereign bond Index
EURONEXT 100 index
S&P EUROPE 350 index
Europe 600 index
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
2.323
1.3045
0.7721
0.6737
0.7199
1.451
1.9854
1.3021
1.1715
0.984
0.8459
0.976
0.2575
0.177
0.267
0.6551
0.6068
0.5701
2.514
1.3235
0.956
0.7936
0.7534
1.0966
2.1124
0.775
1.0504
0.7816
0.5448
1.845
0.2994
0.3326
0.8181
0.8438
2.3496
2.6592
2.3792
2.7894
3.7554
9.1554
2.0081
2.6542
3.6097
4.0744
4.4129
6.1578
0.2604
0.3607
0.8227
2.7123
3.1653
3.5968
2.5428
2.698
2.9457
3.2862
3.9302
6.9192
2.1366
1.5798
3.2367
3.2362
2.8421
11.6413
0.3028
0.6779
2.5209
3.4939
*
WINE AS AN ALTERNATIVE INVESTMENT ASSET
Euro stoxx index
JP Morgan EU gov bond index
Dow Jones Prec. Mtl. Index
S&P/TSX Prec. Mtl. Index
Euro stocks Lrg index
Euro stocks Mid index
Euro stocks Sml index
EU enlrg index
EU bond 5YR
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
82
0.7919
1.0002
0.3636
0.6748
0.7185
0.7242
0.6652
1.2671
0.8844
1.4691
1.1626
1.1125
1.3883
1.2132
0.344
1.8848
0.7735
2.3469
1.7534
1.7512
0.1937
2.2782
1.7437
2.7425
2.1646
2.3161
1.5427
0.9679
0.572
0.5476
0.498
0.9602
0.3877
0.6177
0.5245
0.4579
0.5857
0.8566
0.0209
0.1257
0.7632
0.6209
0.6869
1.1554
4.3872
3.5293
5.5578
4.1684
3.5135
4.23
1.0368
0.5263
0.2684
0.3342
0.2716
4.1312
6.3108
0.3678
1.3756
2.2139
2.9987
3.4703
7.9949
0.8946
2.9948
3.5825
4.6066
7.2423
7.655
0.3479
3.8421
2.3834
9.7177
9.1467
11.049
0.1959
4.6439
5.3729
11.3555
11.2919
14.6133
1.5603
1.973
1.7627
2.2672
2.5976
6.0586
0.3921
1.2591
1.6161
1.8959
3.0551
5.4047
0.0212
0.2563
2.3517
2.5708
3.5833
7.2901
4.4374
7.1943
17.1257
17.2596
18.3284
26.6891
1.0487
1.0729
0.8269
1.3838
1.4166
*
**
*
**
**
**
***
***
***
***
WINE AS AN ALTERNATIVE INVESTMENT ASSET
EU bond 10YR
EU bond 20YR
EU bond 30YR
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
83
0.3665
2.2432
1.1575
0.9175
1.0161
0.8122
0.7816
0.4065
2.0364
0.8831
0.5893
0.5044
0.665
0.3632
3.2313
2.6177
1.9211
1.6369
1.5089
2.3127
2.2689
2.3596
2.8272
4.2074
4.237
4.9316
0.4111
4.1511
2.7213
2.4399
2.631
4.1956
0.3674
6.5869
8.0663
7.9545
8.5388
9.5206
**
*
* - 10 % significance level; ** - 5 % significance level; *** - 1% significance level.
Appendix 4. Granger causality test for Liv - Ex index Europe2 sub - sample
GRANGER CAUSALITY TEST FOR Liv-ex Fwin index UPTO 6 LAGS
CAUSAL
CAUSAL
CAUSAL
DRIVERS
LAG
F-STAT CHISQ-STAT
FLAG
DAX INDEX
CAC 40 INDEX
FTSE 100 INDEX
FTSE MIB INDEX
IBEX 35 INDEX
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
8.4792
8.7282
5.5992
4.4281
3.4885
2.9333
8.8465
7.6115
4.7705
3.8735
3.1182
2.6125
1.734
1.7521
1.4759
1.1992
0.9554
0.7942
8.874
6.5413
3.8885
3.3543
2.689
2.271
5.9585
4.8459
3.0196
2.8902
8.6511
18.0543
17.6142
18.8352
18.8131
19.258
9.0258
15.7444
15.0072
16.4758
16.816
17.1516
1.7692
3.6242
4.643
5.1008
5.1521
5.2142
9.0539
13.5306
12.2327
14.2677
14.5013
14.9094
6.0793
10.0236
9.499
12.2936
***
***
***
***
***
**
***
***
***
***
**
**
***
***
**
**
**
**
**
***
**
**
WINE AS AN ALTERNATIVE INVESTMENT ASSET
AEX-Index
OMX STOCKHOLM 30 INDEX
WSE WIG INDEX
BEL 20 INDEX
AUSTRIAN TRADED ATX INDX
OMX COPENHAGEN 20 INDEX
OMX HELSINKI INDEX
MSCI EUROPE AGRI index
S&P GSCI Prec Met index
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
84
2.372
1.982
6.9188
6.9739
4.5626
3.5317
3.0514
2.5316
5.7951
9.5406
6.2193
4.7386
3.8074
3.5093
4.691
6.2257
3.641
3.0478
2.6888
3.4621
16.9267
9.5321
6.0304
4.7938
4.0379
3.4123
16.6066
10.0718
6.4518
4.7276
3.7659
3.1729
3.8173
5.7822
4.31
3.3266
2.6964
2.3018
7.0775
5.7002
4.1311
3.2933
2.6714
2.2896
1.6488
3.7868
2.2455
1.9359
2.1314
1.9316
5.3594
3.3537
2.4488
1.7897
2.1501
12.792
13.0122
7.0591
14.4254
14.3533
15.022
16.4556
16.6208
5.9125
19.7348
19.5649
20.1555
20.533
23.0392
4.7861
12.8778
11.4539
12.9638
14.5002
22.7293
17.2698
19.7171
18.9706
20.3906
21.7756
22.4026
16.9432
20.8335
20.2963
20.1091
20.309
20.8306
3.8947
11.9604
13.5585
14.1497
14.5415
15.112
7.221
11.7908
12.9958
14.0081
14.4067
15.032
1.6822
7.833
7.0639
8.2346
11.4943
12.6815
5.4681
6.9372
7.7036
7.6124
11.595
**
*
***
***
***
***
**
**
**
***
***
***
***
***
**
***
**
**
**
***
***
***
***
***
***
***
***
***
***
***
***
***
*
***
***
**
**
**
***
***
***
**
**
**
**
*
*
*
**
**
*
*
WINE AS AN ALTERNATIVE INVESTMENT ASSET
S&P Gold & Met index
EU bond 10YR
EU bond 30YR
S&P EU Sovereign bond Index
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
1
2
3
4
5
6
85
2.2978
4.2079
1.6159
7.6981
5.7485
4.6254
4.4409
5.7386
3.0262
2.5312
1.88
2.3343
2.253
0.9666
6.6369
4.7664
3.6687
2.9114
2.6466
31.4466
21.8147
16.671
13.9549
10.9949
9.1765
15.0857
4.2932
3.3425
24.2168
24.4513
24.9441
29.1553
5.8549
6.2597
7.9627
7.9965
12.5888
14.7914
0.9862
13.7285
14.9943
15.6047
15.7008
17.3754
32.0841
45.1237
52.4443
59.3577
59.2937
60.2454
**
**
***
***
***
***
**
*
*
**
**
***
***
***
**
**
***
***
***
***
***
***
* - 10 % significance level; ** - 5 % significance level; *** - 1% significance level.