10-5-1--119-131

Journal of Wine Economics, Volume 5, Number 1, 2010, Pages 119-131

Wine Investment andPortfolio Diversification Gains*

James J. Fogarty"

Abstract

The existing literature on the return to wine is mixed. Some studies have found wine to be anunattractive investment option and others have found wine to be an investment class that providesexcess risk adjusted returns. However, provided the return to wine does not have a strong posi-tive correlation with standard financial assets, even if the return to wine is low, it is possible thatincluding wine in an investment portfolio will provide a diversification benefit. Here the repeatsales regression methodology is used to estimate the return to Australian wine, and the return isshown to be lower than for standard financial assets. Several measures are then used to show thatdespite the return to Australian wine being lower than the return to standard financial assets, winedoes provide a modest diversification benefit. (JEL Classification: Gl 1, G12)

I. Introduction

Although there is now a reasonably established literature that investigates the return to stor-ing fine wine, the potential for wine to provide a diversification gain to an already welldiversified investment portfolio is an issue that has not yet been fully investigated. Thepotential for wine to provide a diversification gain is explored in the remainder of thispaper, and the structure of the discussion is as follows. Section II presents a brief overviewof the return to wine literature. Section III describes the data, explains the method used toestimate the return to wine, and presents estimates of the return to storing Australian finewine. Section IV demonstrates that there is a diversification gain from holding fine wine,and concluding comments are presented in Section V.

* The author would like to thank Gin Parameswaran and an anonymous referee for several suggestions that helpedimprove the paper.* School of Agricultural and Resource Economics, University of Western Australia, 35 Stirling Hwy, Crawley6009, email: [email protected]

© The American Association of Wine Economists, 2010

120 Wine Investment and Portfolio Diversification Gains

II. Literature Review

The first published study of the return to wine was Krasker (1979). To test whether thereturn to wine was greater than the return to Treasury bills Krasker considered post 1950vintages of California Cabernet Sauvignon and Red Bordeaux sold at auction over theperiod 1973 to 1977. There were 137 observations for the study, although, as the regressionapproach used requires there to be observations for wine / in adjacent periods, many salesobservations would have been excluded from the sample. The estimate of the premium towine over Treasury bills was not statistically different from zero, and the conclusion drawnwas that wine is not an especially good investment. Krasker did however note that the effectof taxes on investment income makes storing wine a viable proposition for those consum-ers faced with a high marginal tax rate and purchasing only wine they intend to drink.

Jaeger (1981) takes as her starting point the Krasker (1979) data set and approach, andthen extends the time series back to 1969 so that there are 199 observations. Using anapproach similar to Krasker where storage costs are estimated from the data, the estimatedrisk premium to wine over Treasury bills for the period 1969 to 1977 was 8.5 percent, andwas statistically significant. However, Jaeger argues the estimate for storage costs gener-ated by the approach ($16.60 per case) is implausibly high, and that the model specificationis inappropriate. As an alternative, Jaeger hypothesises that the return to wine can bedecomposed into two parts; a return that accrues to all wine regardless of price, and a returnthat is a function of price. Reformulating the model to allow for returns to vary with price,Jaeger finds that the risk premium over Treasury bills for an average bottle of wine in thesample (price $410) was 12.4 percent. Jaeger concludes by using price to separate thesample into thirds and shows that the return to wine in the bottom third of the sample isgreater than the return to wine in the top third of the sample. As the risk measure for expen-sive wine is lower, and statistically different to the risk measure for less expensive wine, theidea that the higher returns to cheaper wine represent compensation for higher risk appearsreasonable. Whether the return to wine was greater than the return to other risky financialassets was not considered as part of the study.

Rather than estimating a period by period return to wine calculated from aggregate salesdata, Weil (1993) considers the case of an actual wine investor. The 68 transactions evalu-ated cover a 16 year period, where the earliest wine purchases were in 1976 and the mostrecent wine sales were in 1992. Wines were held for varying lengths of time, and the sizeof each investment varied. As such, the actual return the investor achieved was determinedvia an internal rate of return calculation. For comparison purposes the return to an equityportfolio was also calculated. So that the return to equities would be directly comparablewith the return to wine, it was assumed that whenever a wine buy or sell action was initi-ated by the investor a matching investment or disinvestment in equities was made. Forwine, the actual pre-transaction and storage cost annual return achieved was ten percent. Asthe return to the matching equities portfolio was 15 percent, the conclusion drawn was thatthe investor would have been better off holding equities.

The regression approach used by Krasker (1979) and Jaeger (1981) is generalised inBurton and Jacobsen (2001) where the semi-annual return to wine for the period 1986 to

James J. Fogarty 121

1996 is calculated using the repeat sales regression price index methodology due to Baileyet al. (1963). The advantage of the Bailey et al. method over the approach of Krasker andJaeger is that it is not necessary to have adjacent period repeat sales. As long as there is arepeat sale, regardless of the amount of time that has passed between the first sale and thesecond sale, the information can be used in the analysis. The approach therefore permits asubstantial increase in the proportion of total wine sales information that can be used toestimate returns. The data used by Burton and Jacobsen to estimate the semi-annual returnto wine was restricted to post 1960 vintages of leading Red Bordeaux wines and included10,558 observations. In the study the return to wine and the return to wine sub-marketportfolios of the best wines (first growths), and select high quality vintages (1961 and1982), were compared to the return of equities and one year Treasury bills. The resultsshowed that the risk-return profile of wine is such that it is dominated by equities in termsof risky investments, and an unattractive alternative to low risk debt.

The adjacent period hedonic price regression approach was used by Fogarty (2006)to estimate the return to post 1965 vintages of Australian premium wine for the period 1989to 2000. The hedonic literature is substantial, but modern applications generally attributethe approach to Rosen (1974). The quarterly return to premium Australian wine was esti-mated from 14,102 observations and compared to the return of Australian equities andthree month Treasury bills. The return to wine was found to be lower than equities, butwine was also found to be slightly less risky than equities. Given previous findings thatwine returns vary with price, the sample was then split into approximately half and thereturn to more expensive wine compared to the return to less expensive wine. Unlike thefindings of Jaeger (1981), the quarterly return to the most expensive Australian wine(3.17 percent) was higher than the return to less expensive wine (1.92 percent), and some-what surprisingly, the risk to the most expensive wine (S.D. 4.74 percent) was lower thanthe risk to the less expensive wine (S.D. 5.35 percent).

Using data for the period 1996 to 2003 Sanning et al. (2008) use the repeat sales method-ology to estimate the return to holding red Bordeaux. Rather than pooling the data to esti-mate a market return, Sanning et al. (2008) estimate monthly returns for individualvintage-producer combinations, and individual vintage-classification combinations, whereclassification refers to the five classifications recognised in the classic Bordeaux wine rank-ing, plus all unclassified wines as a sixth group. There are 276 unique vintage-producercombinations and so 276 vintage-producer repeat sales regressions. Averaging across allmonths and all 276 regressions, the monthly return to wine was found to be .51 percent, withstandard deviation 6.05 percent. Restricting the sample to just the producers recognised inthe classic Bordeaux classification the average monthly return was found to be .78 percent,with standard deviation 7.20 percent. There are 83 unique vintage-classification combina-tions and the average monthly return across all months and all 83 vintage-classificationrepeat sales regressions was .88 percent, with standard deviation 7.08 percent. Restrictingthe sample to just the producers recognised in the classic Bordeaux classification the averagemonthly return was found to be 1.03 percent, with standard deviation 7.29 percent.

The Capital Asset Pricing Model (CAPM) and the Fama-French Three Factor Model(TFM), which are both standard tools used to study equity returns, are then used to further


investigate the return to wine. For the vintage-producer analysis, although average excessreturns are found to be positive, most estimates are not statistically significant. The resultis somewhat stronger for the vintage-classification regressions. Average excess returns areshown to be positive, with approximately one quarter of the excess return estimates beingstatistically significant, and of the statistically significant excess returns most are positive.The finding of excess risk adjusted returns is important; however, arguable the most impor-tant finding presented in Sanning et al. (2008) is that models that do a good job of explain-ing the return to equities, such as the CAPM and TFM, do an extremely poor job ofexplaining the return to wine. The finding is important because if wine returns are notexplained by equity market risk factors, wine may be able to play a risk diversification rolein a balanced investment portfolio.

Masset and Henderson (2010) use an approach that is conceptually similar to theapproach used to construct a stock market index to show that for the period 1996 to 2007the cumulative return to red Bordeaux wine (145 percent) was greater than the cumulativereturn to the Dow Jones (127 percent). The risk to holding wine, measured as the annual-ised standard deviation of returns, was also shown to be lower (8 percent) than the riskassociated with holding equities (15 percent). The data set used included 77,014 wine saleobservations, and so the authors were also able to estimate a variety of sub indices basedon vintage and wine classification. The results show that in general vintages of higher qual-ity, along with first growth and second growth wines, provide the highest returns, and alsohave a risk profile at least as favourable as wines from lesser vintages and lower classifica-tions. The mean-variance framework is then used to show that including art and wine in anequity portfolio reduces portfolio risk, and a polynomial goal programming model is usedto show that the optimal allocation to wine and art varies if investors care about the skew-ness and kurtosis of their investment portfolio as well as risk.

The ability of wine to provide a diversification gain to an investment portfolio during adownturn is considered in Masset and Weisskopf (2010). The authors use data covering theperiod 1996 to January 2009 and the repeat sales methodology to estimate a fine wine priceindex, as well as individual price indices for wines from Bordeaux, Burgundy, RhoneValley, Italy, and the US. Wine is shown to outperform equities during a downturn, andincluding wine in a diverse mix of investment portfolios, from conservative to aggressive,is shown to reduce portfolio risk. The CAPM and the conditional CAPM are then used toshow that although wine returns are not related to systematic market risk, returns areimpacted by general economic conditions.

III. Data and Wine Return Estimates

To investigate the potential for there to be a diversification gain from holding wine requiresrisk and return information for several asset classes. For standard financial assets it is possibleto download data from a commercial service provider such as datastream. For wine it is nec-essary to obtain raw auction data and then estimate returns. For the current study wine salesdata were obtained from the Langton's auction house for sales in Melbourne and Sydney, and


the data allow returns to be estimated for the period 1990Q1 to 2000Q4. For each saleLangton's recorded only the highest and lowest sale price for each item, and information onthe quantity associated with each lot sold was unavailable. If several lots of an identical winewere presented for sale at a particular auction, the mean of the highest and lowest hammerprice has been used as the sale price. The sample of wines considered was restricted to thepost 1965 vintages of the wine brands listed in the Caillard and Langton (2001) classificationof Australian investment quality wines and included 12,180 observations.

Observations were recorded for 84 individual wine brands, and while most of the obser-vations were for either Shiraz based or Cabernet based wines, this was not exclusively thecase. The specific distribution of observations by dominant grape variety was: Cabernet 43percent, Shiraz 37 percent, Chardonnay ten percent, Pinot Noir four percent, Riesling twopercent, and Semillon, Merlot, and Botrytis affected wines approximately one percenteach. Not all wine brands appeared in the sample with equal frequency, and the most tradedwine, Penfold's Grange Shiraz, accounted for almost nine percent of the sample. Details onthe top eight wine brands by sales volume - along with the price range recorded for salesacross all vintages at any point in 2000 - are shown in Table 1, and as can be seen, the topeight wine brands account for more than 36 percent of the total number of observations.Reflecting the development of the Australian wine investment market, sales observationsincrease in frequency steadily through time. Specifically, there were 624 sales observationsin 1990, followed by 639 sales observations in 1991, then 689 sales observations in 1992,..., and 2,483 sales observations in 2000.

Table 1Summary Details for the Top Eight Wine Brands

by Volume of Sales

Wine Brand (1)

Penfold's Bin 95Grange Shiraz

Penfold's St HenriShiraz-Cabernet

Penfold's Bin 707Cabernet Sauvignon

Wynn's CabernetSauvignon

Penfold's Bin 389Shiraz

Henschke Hill ofGrace Shiraz

Mount Mary QuintetCabernet Blend

Lake's Folly WhiteLabel Cabernets

Observations1990-2000(no.) (2)

1,071

652

481

467

461

453

421

386

Vintage rangesold in 2000(years) (3)

1965-95

1965-96

1965-97

1965-97

1965-95

1966-95

1976-95

1967-98

Distinctvintages sold

in 2000(no.) (4)

31

31

23

31

31

23

18

27

Max price allvintagesin 2000($)(4)

426

100

151

86

85

281

226

60

Min price allvintagesin 2000($)(5)

159

29

72

19

20

100

95

20


When considering wines from a specific region it has been shown that it is possible toforecast the vintages that outperform the average by modelling the weather during the grow-ing season (Ashenfelter et al., 1995; Byron and Ashenfelter, 1995; Wood and Anderson, 2006;Lecocq and Visser, 2006; Ashenfelter, 2008). However, in the Australian context, whereinvestment grade wines are drawn from across the country rather than from a single region,such an approach is difficult to implement. As such, for the Australian context it could beargued that an index based on the return achieved from holding wines from all vintages isthe most appropriate index to consider. It is however also true that climatic variation in mostAustralian premium wine growing regions is less pronounced than in Bordeaux, and so thevariation in price across wines from different vintages is also less pronounced.

The most commonly used approach to estimating the return to wine has been the repeatsales regression approach, and that is the approach used here. The original application ofthe repeat sales price index methodology was in the area of house price index construction,and there is now a vast literature that has used the approach to estimate the return to realestate. The approach is however well suited to estimating returns in any market character-ised by infrequently traded heterogeneous assets. For example, the approach has also beenused extensively to estimate the return to art, and in their review of the art auction process,Ashenfelter and Graddy (2003, p. 769) list nine studies that have used the repeat salesregression approach to estimate the return to art.

The exposition of the repeat sales regressions approach presented in Bailey et al. (1963)is timeless, and as such the following outline of the approach draws heavily on the originalpresentation. Let weW=[l,...,W] be the set of all observed wine sales, and let/er={0,l , . . . , r} be the set of time periods under consideration. Now, separate the set Winto the subset of wines that sell only once during the sample period, i el- {1,...,/} c= W andthe subset of wines that transact more than once over the sample period, j e J- {l,...,y)cff.The repeat sales approach considers only the observations in the set J. In some applicationsthe proportion of the data discarded can be significant, and this is a criticism of the approach.For Australian wine, individual wine brand-vintage combinations generally trade morethan once in a relatively short period of time and so the requirement for there to be a repeatsale is not especially onerous. Now, let the second sale for wine) occur in time period t andlet the first sale occur in time period s, where t>s. Let the sale observations be denoted Pjand Pj, respectively, with the price relative (Pj/Pj) denoted Rf. Following Bailey et al.(1963, p. 934) and using B' and Bs to denote the true but unknown price index numbers forperiods t and s, the repeat sales model can be written as Rf - (B'/Bs) x Uf, or if lower caseletters are used to denote logs, as rf -b' -bs + uf, where in log form the errors have zeromean and constant variance. If xT takes the value minus one when r=s, the value one whenT=f, and the value zero otherwise, the regression model can then be expressed asrf - £[=1 B

Tx] + uf, where the return to wine is found by taking first differences of the leastsquares estimates of the P coefficients.

The actual regression point estimates, with robust standard errors, associated quarterlypercentage returns, and regression R2 value are reported in Table 2. Although the R2 value


Table 2Return to Wine Estimates

Period (1)1990Q11990Q21990Q31990Q41991Q11991Q21991Q31991Q41992Q11992Q21992Q31992Q41993Q11993Q21993Q31993Q41994Q11994Q21994Q31994Q41995Q11995Q21995Q31995Q41996Q11996Q21996Q31996Q41997Q11997Q21997Q31997Q41998Q11998Q21998Q31998Q41999Q11999Q21999Q31999Q42000Q12000Q22000Q32000Q4ObservationsR2

Estimate (2)

-.045*-.031-.024-.034-.042-.057-.026

.008

.000

.011-.011

.043

.036

.096**

.111**

.106**

.201**

.220**

.196**

.177**

.229**

.322**

.355**

.372**

.491**

.541**

.512**

.540**

.615**

.699**

.704**

.752**

.814**

.834**

.859**

.823**

.862**

.836**

.874**

.885**

.891**

.886**

.873**

.861**

All Wine Vintages

S.E. (3)

(.025)(.031)(.032)(.031)(.035)(.035)(.037)(.035)(.036)(.038)(.039)(.041)(.038)(.038)(.039)(.040)(.039)(.039)(.039)(.040)(.042)(.043)(-041)(.042)(.042)(.043)(.042)(.043)(043)(.044)(.044)(.044)(•045)(.045)(.045)(.045)(.046)(.046)(.046)(.046)(.047)(.047)(.047)(.048)12,180

.192

Return (4)

^t.3971.4490.660

-1.009-0.749-1.499

3.1023.503

-0.7831.069

-2.1795.537

-0.7236.2211.515

-0.5409.9901.946

-2.394-1.8995.4099.6643.3651.747

12.6205.163

-2.8972.9077.7788.7570.4494.9476.4191.9562.528

-3.5284.015

-2.5273.8591.1090.612

-0.524-1.341-1.206

* Significant at the 10 percent level. ** Significant at the 5 percent level or greater.


for the regression is low, it is consistent with that reported in Burton and Jabcosen (2001,p. 343) of .17 for all wine, and not that far below the values reported in Jaeger (1981, p. 589)of between .29 and .41 for a vastly smaller data set. The maximum quarterly return over theperiod was 12.62 percent, and the minimum quarterly return was -4.40 percent. The averagequarterly return was 2.05 percent, and the standard deviation of returns was 3.93 percent.

IV. Diversification Gains

To place the return information in context, Table 3 provides summary details on the returnto Australian wine, Australian shares, Australian bonds, and unhedged Australian dollarreturns to US shares and US bonds from the first quarter of 1990 to the fourth quarter of2000. The second column of Table 3 provides total return information, and as can be seen,the return to Australian wine is lower than the return to each of the other four asset classes.There are several measures of risk, and many financial applications focus on systematicrisk. Here, details are reported for total risk, measured as the standard deviation of totalreturns to each asset class, and as can be seen by considering the information in columnthree of Table 3, higher returns are generally associated with higher risk. When using totalrisk rather than systematic risk, a standard measure that can be used to compare the riskadjusted performance of each asset class is the Sharpe ratio. The Sharpe ratio is a measureof excess return per unit of risk and is calculated as the asset return minus the risk freereturn divided by the asset standard deviation.1 Here the 90-day Treasury bill return hasbeen used as the risk free return, and by considering the detail in column four of Table 3 itcan be seen that of the five asset classes wine has the lowest Sharpe ratio.

Table 3Individual Asset Summary Return Information: 1990Q1-2000Q4

Asset class (1)

Australian Wine

Australian Shares

Australian Bonds

US Shares

US Bonds

QuarterlyReturn (%)(2)

2.05

2.67

2.84

4.79

2.91

StandardDeviation (%) (3)

3.93

5.80

3.15

8.14

6.07

Sharperatio (3)

.061

.150

.327

.366

.181

Correlationcoefficient (4)

1.00

.136

-.106

.131

.003

S. ratio xCorr. coef. (5)

.061

.020

-.034

.048

.001

Diversificationgain (6)

-

Yes

Yes

Yes

Yes

In an investment portfolio context both asset returns and asset correlations are impor-tant, and as can be seen by considering the correlation coefficient information in the fifthcolumn of Table 3, the return to Australian wine is not strongly correlated with the returnto other assets. So, despite the relatively poor performance of wine in both risk adjustedterms and raw return terms, wine could still be a valuable addition to an investment portfo-lio. The potential for there to be a diversification gain from holding wine can be tested

' If using systematic risk rather than total risk the comparable metric is the Treynor measure.


using several different approaches. The first approach considered is based on the ideasoutlined in slightly different ways in both Blume (1984) and Elton et al. (1987) that therewould be an incremental diversification benefit from adding wine to an existing asset if theSharpe ratio for wine is greater than the Sharpe ratio of the existing asset multiplied by thecorrelation coefficient between wine and the existing asset. As can be seen by consideringthe final column of Table 3, based on the Blume/Elton et al. measure, there is a diversifica-tion gain from adding wine to each of the four broad asset classes considered.

One way to visualise the diversification gain from adding wine to an existing asset is tofollow Polwitoon and Tawatnuntachai (2008) and form a sequence of portfolios consistingof the benchmark asset and the asset wine, where the allocation to wine starts at zero andincreases in small steps; and then plot the change in the Sharpe ratio. As for the sampleperiod the return to Australian bonds was higher than the return to Australian wine and therisk lower, portfolio combinations comprising these two assets provide a good example tofocus on when studying diversification gains in a two asset portfolio setting.

Figure 1 plots excess portfolio return, portfolio risk, and the net gain in the Sharperatio compared to a pure Australian bond portfolio, where a sequence of Australian wineand Australian bond portfolios are constructed with the weight to wine gradually increas-ing from zero percent to 100 percent. In the figure risk and excess return information isread off the left axis, and as can be seen, excess portfolio return falls from 1.03 percentwith zero allocation to wine, down to .24 percent with 100 percent allocation to wine. Itcan also be seen from Figure 1 that portfolio risk traces a quadratic. The rate of decreasein portfolio risk is equal to the rate of decrease in portfolio return when the allocation towine is 14 percent, and it is this point that defines the maximum net gain in the Sharperatio from adding Australian wine to an Australian bond portfolio. The plots for pair-wise

Figure 1

Australian Wine and Bond Portfolio Combinations

Portfolio risk andexcess return

5.0 T

2.0

1.0

0.0

• Gain in Sharpe ratio (RH axis)

• Portfolio Return (LH axis)

Change inPortfolio Risk (LH axis) Sharpe ratio

-rO.l

Maximum gain in Sharpe ratio(allocation to wine 14 percent)

Minimum risk portfolio(allocation to wine 35 percent)

Falling return as weight to wine increases

30 40 50 60 70Percentage allocation to wine

80 90 100-0.4


asset portfolios formed using wine and the other asset classes shown in Table 3 lookbroadly similar to the result shown in Figure 1.

It is however true that an investor is likely to hold a diversified portfolio of assets ratherthan a single asset class. As such, it is necessary to consider a more general test of thepotential for there to be a diversification gain from holding wine. Mean-variance spanningtests can be used to answer the question of whether or not an investor that currently holdsK assets in their portfolio would benefit by adding another asset, or set of assets, to theirexisting investment portfolio. The Huberman and Kandel (1987) regression based spanningtest is a joint test that the intercept equals zero, and that the sum of the /? coefficients equalone, in the linear regression r, = a + X£, Alv + M<> where rt denotes the return to the testasset at time t, r.f denotes the return to the benchmark assets at time t, and ut is a constantvariance zero mean error term. If the Huberman and Kandel spanning test restrictions hold,it means the return to the test asset can be written as a linear combination of the existingbenchmark assets, plus a zero mean error term. In the current application this would implythat adding wine to the portfolio of existing assets could not raise expected portfolio return,but could only add to portfolio risk. As such, if the restrictions oc=O and Zjl, p = 1 hold, theinvestor would not add wine to their investment portfolio, and it can be said that wine isdominated, or spanned, by the test assets.

The Huberman and Kandel mean-variance spanning regression was estimated usingquarterly return information for the period 1990 to 2000 where Australian wine was the testasset and the benchmark assets were Australian shares, Australian bonds, US shares, andUS bonds. The Wald test statistic for the joint restriction that a = 0 and Zf=i P = 1 was 14.3,and strongly significant. The null hypothesis of spanning is therefore rejected. Although theabove result suggests wine provides a diversification benefit, before reaching a definitiveconclusion there is one further aspect to consider. Unlike standard financial assets, thereturn to wine has to be estimated, and there is uncertainty surrounding these estimates.The situation is illustrated in Figure 2 where the actual estimated index values for 2000Q3and 2000Q4 are shown, along with estimates of the maximum and minimum plausiblerange for the change in the index value.

Although using estimated returns is unlikely to have a significant impact on the esti-mated average return, it does mean that the volatility of the return to wine is likely to beunderstated. Given this issue, as a final test of the robustness of the result that wine provides

a diversification benefit, new wine return estimates were generated as /, =/ , + (S£(/,)x(px(-l)') where /, denotes the least squares estimate of the wine price index at time t,SE(I,) denotes the associated standard error, and qjis set at the maximum value consistentwith rejecting the hypothesis of mean-variance spanning. The null hypothesis of spanningcan be rejected at the 95 percent confidence level for (p=A5, and with <p=A5 the return towine is approximately the same as shown in Table 3, but the implied level of risk is 35 per-cent higher (5.29 percent) than reported in Table 3. On balance, it therefore appears reason-able to conclude that in an Australian context, adding wine to an already well diversifiedinvestment portfolio provides a further diversification benefit. This finding is consistentwith that reported in Masset and Weisskopf (2010) for the US market.


Figure 2

Uncertainty Surrounding Wine Return Estimates

Log Index

i

8727-

8606-

L

Point estimate

VT *

Return Iestimate j

used I

i

>• 95 percent confidence interval

! Minimumj plausibleirange

f •9-

95 percent confidence interval •< ^ . ^

1

rMaximumplausiblerange

iPoint estimate

1 ^

2000 Q3 2000 Q4

To make tractable the diversification gain attributable to wine where the benchmarkportfolio contains several assets, it is helpful to consider only portfolios on the mean-variance efficient frontier. Approaches to finding the mean-variance efficient frontier haveevolved substantially since the concept was proposed by Markowitz (1952), but here theclassic approach that uses the covariance matrix and mean return vector with asset weightsrestricted to non-negative values has been used.2 The efficient frontier and individual assetrisk-return details are shown in Figure 3. The reference portfolio used to calculate thepotential diversification gain available from holding wine is the portfolio that generated thehighest level of return per unit of risk when wine was not allowed as an investment option.This measure was selected as it is a utility free measure of an optimal portfolio. When winewas excluded from the set of investment options the portfolio that provided the highestreturn per unit of risk had a quarterly return of 3.10 percent and risk of 3.15 percent. Theinclusion of wine in the portfolio allowed this same level of return to be achieved with arisk of only 2.94 percent. So, despite having a relatively low return, including wine as aninvestment option shifts the efficient frontier to the left.3

2 Alternative approaches include replacing the estimated return vector or covariance matrix with a weighted aver-age of two estimates, where the weights are determined by the data. See Jorion (1985) for an example of theapproach applied to expected returns, and Ledoit and Wolf (2003) for a general approach to finding a shrinkagecovariance matrix.3 Transaction and storage costs when trading wine are much higher than when trading standard financial assets.However, in Australia the profits on the sale of wine are generally tax free. These two effects largely cancel eachother out so that when considering after tax and transaction cost return information for the above assets the diver-sification gain from holding wine is of approximately the same order of magnitude.


Figure 3

Mean-Variance Efficient Frontier with Wine as an Investment Option

PortfolioReturn (%)

5.0!

4.0

3.0-

2.0-

1.0

0.0

US Shares

Efficient portfolios

US Bonds

Australian Shares

Australian Wine

Inefficient portfolios

0.0 1.5 3.0 4.5 6.0Portfolio Risk (%)

7.5 9.0

V. Conclusion

Over the sample period both the return to Australian wine and the risk adjusted excessreturn to Australian wine were lower than for standard financial assets. It had previouslybeen proposed that for the investor that wished to drink the wine they store, even when thereturn to wine is lower than the return to standard financial assets, tax impacts may be suchthat wine investment remains an attractive option. Given the return information shown inTable 3, and provided the investor has other income such that they face a positive marginaltax rate, this proposition is also true in Australia. Additionally, for the investor that does notintend to drink the wine they hold, the mean-variance spanning test results show that wineinvestment can provide a diversification benefit that means a positive allocation to wine isworthwhile even if they already have a well diversified investment portfolio. Using thereturn from the portfolio that provided the highest return per unit of total risk when winewas not allowed as an investment possibility as the reference level of return, a positiveallocation to wine was shown to reduce portfolio risk by approximately 6.7 percent.

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Ashenfelter, O., Ashmore, D. and Lalonde, R. (1995). Bordeaux wine vintage quality and the weather.Chance, 8, 7-14.

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