Working Papern°: 2015-58-01

Dynamics of fine wine and asset prices: evidence from short and long-run co-movements

Benoît Faye a, Eric Le Fur b, Stéphanie Prat c

a INSEEC Business School, H19, Quai de Bacalan, CS60083, 33070 Bordeaux France; Bordeaux Wine Economists, LAREFI University of Bordeauxb INSEEC Business School, H19, Quai de Bacalan, CS60083, 33070 Bordeaux France; Bordeaux Wine Economists, LAREFI University of Bordeauxc INSEEC Business School, H19, Quai de Bacalan, CS60083, 33070 Bordeaux France; Bordeaux Wine Economists, LAREFI University of Bordeaux

January 2015

An ulterior version of this article appeared in Applied Economics, 2015It can be purchased at: http://dx.doi.org/10.1080/00036846.2015.1011321

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Dynamics of fine wine and asset prices: evidence from short and long-run co-movements

Benoit Faye, INSEEC Business School, Bordeaux Wine Economists, LAREFI Bordeaux University1

Stéphanie Prat, INSEEC Business School, Bordeaux Wine Economists, LAREFI Bordeaux University2

Eric Le Fur, INSEEC Business School, Bordeaux Wine Economists, LAREFI Bordeaux University3

This version: June 2014 – do not quote

Abstract. This paper examines short and long-term price linkages among the majority of fine wine and equity markets over the period 2003-2012. We do not consider price index (LIV-EX 100 or 500) as is usual in previous studies but rather auction price series of the world’s most traded wine-vintage pairs (5 Bordeaux first growth, 8 Bordeaux second growth, 5 Burgundy, 3 Rhone, 4 Italian, 5 Californian, 1 Australian and 1 Portuguese). A global equity index is also included using MSCI World. Cointegration procedures, the Granger non-causality test, and ECM are used to analyze short and long-run relationships among these markets. The results indicate a high impact of financial markets on wine prices and short-term causality for some wines. Moreover, the findings show short-run causality between wines themselves, revealing leader (exogenous) and follower (endogenous) status of some fine wines in price dynamics, and also long-run causality for endogenous wines. This approach is relevant for portfolio diversification strategies and allows price movements to be anticipated more accurately than an index approach.

Keywords: Wine, Price, finance, causality, ECM

JEL classification : C32, D12, G11

1. Introduction

1 bfaye@inseec.com2 sprat@inseec.com3 elefur@inseec.com

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Over the past three decades there has been increasing interest on the part of investors for alternative assets, particularly real estate, art, and recently wines and stamps (Walgreen, 2010). With regard to fine wines, the low correlation with traditional assets (Masset and Henderson, 2010), the exceptional quality of vintages 2009 and 2010, and the rise of Asian demand are the main reasons for this interest. For the past 15 years, this asset class has grown and become globalized. Fine wine sales (see section 2) at major auction houses have risen from $3.7 million (2003) to $181.9 million (2011) and since 2009 the volumes purchased by Chinese buyers have exceeded European volumes.

A growing literature has examined the characteristics, prices and the volatility of returns of this asset class (Storchmann, 2011). More recently, various papers have tried to provide meaningful forecasts of wine price dynamics by focusing on evidence of linkages between equity returns and wine returns and between wine returns themselves. These studies were highly relevant during the period when the number of investment funds and auction prices were rising sharply.

On the strength of this growing interest, a number of price indices have been created, thereby improving transparency and market liquidity (Liv-ex for example). These indices provide market trends, but limited information. Indeed the methodology employed is not always transparent. Prices are recorded from both stores and auction houses. Generally, the wines used in the index are fixed, but only the last ten vintages are recorded. Most studies on linkages between equity markets and wine markets and between wine returns themselves have used these indices. However, though investors cannot buy indices (in the absence of derivatives on the wine market), they can buy real pairs of wine-vintages. With investors operating in auction markets in particular, study of the volumes traded in these markets (Cardebat and al., 2013) shows that investors first buy the most liquid wines, which are not always included in these indices. For example, the 1982, 1986, 1990, 1996 and 2000 Bordeaux first growths account for the bulk of transactions, but are not included in Liv-ex 50, 100, or 500.

This paper focuses on the linkages and speed of adjustment between returns on the stock market and returns on the market for fine wines and between wines themselves using traditional cointegration methodology. However, our approach takes into account the above remarks. First, we do not consider wine indices, but certain wine-vintage pairs for which the traded volumes are the greatest. The wine selection was made from a very large initial database including Bordeaux first and second growths, Burgundy, Rhone, Italian, Californian, Australian and Portuguese wines. We assume that these wines give a price direction to other wines. Second, we use a global database of auction prices. Third, we consider monthly price series that are much longer than those used in previous studies (2003-2012, including two periods of financial crisis).

The remainder of the paper is organized as follows. Section 2 briefly surveys the literature on the level and movement of wine returns. Section 3 presents our database and the selection principles of the sample. The methodology is explained and results are discussed in section 3. The paper ends with an interpretation of results.

2. Literature review

The growing interest in wine economics prompted us to test the link between wine and financial assets in terms of risk and returns. The literature reveals the exceptional returns on wine

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investments (Sokolin, 1998; Burton and Jacobsen, 2001; Masset et al; 2010; Masset and Weisskopf, 2010), but there are some conflicting findings.

While some papers suggest that certain wines outperform U.S. Treasuries, specifically French wines (Jaeger, 1981; Burton and Jacobsen, 2001; Jones and Storchmann 2001; Hadj Ali and Nauges, 2003; Sanning et al, 2007, 2008), Australian wines (Byron and Ashenfelter, 1995), and Californian wines (Jaeger, 1981; Storchmann and Haeger, 2006), others suggest lower returns (Krasker, 1979; Ashenfelter et al, 1995; Wood and Anderson, 2006). If wine returns are compared to the stock index, the same confusion appears. Most of the literature indicates the outperformance of wines, with the notable exception of certain studies (Duthy, 1986; Byron and Ashenfelter, 1995; Masset et al, 2010; Masset and Weisskopf, 2010, 2011). Even on similar wine appellations, results may differ. For Bordeaux wines, Hadj Ali and Nauges (2003) and Masset and Henderson (2010) find strong returns, but Fogarty (2006a, 2006b, 2006c), and Cardebat and Figuet (2010) or Beijer (2012) consider the returns to be much lower.

However, few wine studies are concerned with volatility and the transmission of returns between wine and financial assets, and between wine products themselves. Recently, the findings of Kourtis et al. (2012) in relation to wine portfolio international diversification strategies show that some wines (Italian, Australian and Portuguese) are not correlated with others (Bordeaux, Bourgogne and Californian) and with financial assets. This finding is relevant both for portfolio diversification and in relation to price dynamics. On the other hand, it does not provide any information on the link and the reaction time of price series between wines and financial assets.

On these topics, research is more advanced with regard to other alternative assets. Since the 1990s, the question of price transmission (returns and volatility) in the art market has been discussed using the cointegration method or ECM models, which allow short and long-run co-movements to be identified (Higgs and Worthington 2004; Ginsburgh and Jeanfils 1994; Flores, Ginsburgh and Jeanfils, 1999; Higgs et Worthington, 2003). These issues have been addressed in the area of commodities (Brooks and Prokopczuk, 2013) and specifically of wine economics (Fogarty, 2007; Sanning et al., 2008; Masset and Henderson, 2009; Baldi et al., 2013). In this last study the authors used price indices drawn from data providers (e.g. Live-ex or wineprices.com). However, the use of indices generates a number of well-known issues, as discussed previously. Given these circumstances, it would be more relevant to analyze short and long-run relationships between price series of real wine/vintage pairs. This approach is more informative for investors who wish to buy and sell real wines in accordance with a portfolio strategy, and to forecast real prices (see section 3).

In this paper, we adopt a similar methodological approach to Worthington and Higgs (2003) and Masset and Henderson (2009), but use real prices rather than index prices. With this approach, we need a very large global price database from which we can identify the most traded wine/vintage pairs. Indeed, these highly liquid pairs are in great demand by investors and are probably trendsetters for other wines. The database is described in the following section.

3. Database, and selection and explanation of samples

Two sources of data are used, one for wine prices and the other for financial indices. We draw our initial database from the WinePrices.com website covering 215,883 wine auctions (or 1,598,177 standard 750ml bottles ) sold between 2003 and 2012. This dataset contains the most representative

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wines for portfolio diversification (Kourtis et al., 2012), including Bordeaux (first and second “grand crus”), Burgundy, Rhone, Italian, Californian, Australian, and Portuguese, for vintages from 1945 to 2008.

As discussed in the previous section, most studies use Liv-ex indices. More precisely, the Liv-ex 50, 100 and 500 indices cover the last ten vintages for Bordeaux wines only, whereas the Liv-ex Fine Wine Investables Index tracks the most “investable” wines in the market –around 200 wines from 24 top Bordeaux chateaux – dating back to the 1982 vintage. Only Liv-ex 1000, which tracks 1000 wines from around the world, offers sufficient diversification potential. It is comprised of seven sub-indices including the Bordeaux Legends 50 index which is a selection of 50 Bordeaux wines from exceptional older vintages (from 1982). The relevance of Bordeaux Legends 50 in terms of trading frequency is, however, reduced by its low weight in the Liv-ex 1000. But trading frequency by vintages in our initial database shows that auctions include a limited number of wines and vintages (Fig.1 and Fig. 2), which do not reflect the composition of wine indices.

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Fig. 1: Number of auctions by Vintage

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Considering the issue of information quality from an investor standpoint, it seems more appropriate to analyze short-run and long-run causalities between the most traded products rather than between wine indices. However, in this context the number of cointegrating relations and causal links becomes very high and compromises the interpretation of the results. We therefore select the most traded wine-vintage pairs in order to produce information that is useful to investors, and we assume that there is a correlation between these leading products and followers, defined as the least traded wines from the same region, or the least traded vintages of the same wine. Thus, in the initial database, we selected only the most traded wine-vintage pairs in each region . This choice is also consistent with the liquidity constraint to which investors are exposed. Our sub-sample (Table 1) contains 18,904 fine wine auctions (143,931 750ml bottles) amounting to $130,286,101. Sales are distributed (Fig. 3) among 33 auction houses4 in six countries (USA, China, United Kingdom, Switzerland, France, Netherlands). For each auction we have the prices realized for each uniform wine lot (same wine and vintage). The price realized is the sum of the hammer price and the buyer's premium specific to each auction house, excluding sales tax and VAT. Buyer's premium rates do not vary much from one auction house to another and are largely compensated by tax spreads between places. The impact of transaction costs is relatively neutral on changes in realized prices.

Monthly price series (Fig. 6) show mean (realized) prices between 2003 and 2012. Missing data were estimated by linear interpolation, except for summer prices (July and August), which were estimated by the nearest neighbor method. For our selected products, note that the shapes of monthly price series are consistent with the evolution of the Liv-ex 500 index5 (Fig. 5), except Australian and Portuguese wines, and some Californian wines which are known to be non-correlated with Liv-ex series (Kourtis et al., 2012).

In this sub-sample of 32 most traded wines from the initial database, a small number of vintages account for the bulk of trade, usually between five and seven vintages (out of 85 vintages in the initial database) over a total of 60,334 auctions. Spearman correlation tests (see Annex 1 to 4) show strong price dependency between the most traded vintages of the same wine. Accordingly choosing only the most traded vintage for each wine selected seems reasonable6. Finally, our study of short-run and long-run causalities focuses on 15 vintage-wine pairs (13,201 auctions) that are integrated of order 1 (see ADF tests in Table 2) and considered to be the market core of fine wine auctions.

For the financial indicator required by the model, we use the MSCI World index (Fig. 5). This choice, widely shared in the literature, is consistent with a presumed global wine market that is strongly linked to equity markets.

4 Acker Merrall & Condit (New York, Hong Kong), Christie’s (New York, London, Hong Kong, Los Angeles, Paris, Amsterdam, Bordeaux, Chicago, Geneva, South Kensington), Sotheby’s (New York, London, Hong Kong), Zachy’s (New York, Hong Kong, Los Angeles, Las Vegas), Bonhams (London, Hong Kong), Bonhams & Butterfields (San Francisco), Morells (New York), Hart Davis Hart (Chicago), ERI (Chicago, San Francisco), Bloomsbury/Sokolin (New York), WineGavel (San Francisco), Spectrum Wine Auctions (Dana Point, Los Angeles)5 Liv-ex 500 comprises 30-40% great Burgundy, Rhone and Italian wines, with the remainder Bordeaux wines.6 All the coefficients are significant and very high for Bordeaux first and second growths and Burgundy, whereas Californian coefficients are relatively low, probably because their history as financial assets may be more recent.

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Table 1: Descriptive statistics of sub-sample

Categories Wines VintagesSelected

Number of

auctions

Total auction value

Numberof

bottles

Average price

BORDEAUX (First growth)

Château Lafite Rothschild 1982 1862 36 810 793.2 12671 2656.49Château Mouton Rothschild 1982 2034 16 836 229.6 15013 1026.88Château Haut Brion 1989 1451 13 853 640.2 11746 1083.28Château Margaux 1996 1312 8 647 584.33 13150 617.05Château Latour 1990 1186 7 568 861.39 9766 726.17

BORDEAUX (Second growth)

Château Cos d’Estournel 1982 615 1 529 676.77 5224 276.28Château Ducru Beaucaillou 1982 583 1 182 444.64 4915 225.31Château Gruaud Larose 1982 657 1 566 556.13 5792 255.07Château Léoville Barton 2000 587 880 029.71 6258 138.40Château Léoville Las Case 1982 775 3 664 653.02 8089 426.53Château Montrose 1990 729 2 664 859.71 5986 419.94Château Pichon-Longueville Baron 1990 534 1 024 253.21 4758 203.34Château Pichon-Longueville Comtesse de Lalande

1982 7933 159 497.62 6151 486.61

BOURGOGNE

Domaine de la Romanée-Conti La Tâche 1990 408 6 161 512.22 1606 3431.74Domaine de la Romanée-Conti Richebourg

1990 2051 482 299.09 786 1684.04

Domaine de la Romanée-Conti Romanée Saint Vivant (Marey-Monge)

1990 180843 577.18 811 953.86

Domaine de la Romanée-Conti , Romanée-Conti

1990 2328 717 625.45 650 10987.05

Vogüé-Musigny Vieilles Vignes 1990 367 1 148 545.89 2078 521.48

RHONE

Château de Beaucastel Chateauneuf-du-Pape

1989 383564 652.71 2680 247.42

Domaine Chave-Hermitage 1990 225 726 773.34 1387 494.79Domaine Jaboulet Aîné Hermitage La Chapelle

1990 2841 815 367.24 3874 432.03

ITALIE

Tenuda San Guido – Sassicaia Bolgheri 1985 268 1 476 497.13 995 1322.69Tenuda dell’ Ornellaia Ornellaia Bolgheri

1997 228348 538.03 1699 192.47

Marchesi Antinori Tenuda Tignanello 1997 249 244 815.56 1804 130.03Marchesi Antinori Solaia 1997 243 495 386.01 1554 309.59

AUSTRALIE Penfolds Grange 1998 369 832 168.32 2448 331.23CALIFORNIE Harlan Estate 1997 540 2 541 869.13 2403 993.51

Screaming Eagle 1999 317 1 047 047.49 718 1387.20Opus One 1997 272 422 918.93 1918 214.72Dominus Estate 1994 532 835 216.89 3602 215.60Bryant Family 1997 238 900 388.25 962 848.50

PORTUGAL Taylor’s Port Wine 1994 246 291 822.76 2437 117.93Total 18 904 130 286 101 143 931

Figures 3 and 4. Auction distributions by place and auction house

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Figure 5. Wine and financial indices over the period 2003-2012

Liv-ex 1000 MSCI World US (price index)

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2003-1 2004-5 2005-9 2007-1 2008-5 2009-9 2011-1 2012-50

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Figure 6. Monthly price series by region

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4. Methodology

We employ Vector Autoregressive (VAR) and Vector Error Correction (VEC) models to explore the co-movements between wine prices and equity markets for the period 2003M1-2012M10. First, the VAR methodology allows us to analyze the short-run dynamics of the wine price formation process, both among wine categories and between them, linked to the market dynamics of asset prices. Next, a cointegration analysis applying the Johansen and Juselius (1990) procedure is used to investigate the long-run causal relationships between price series.

We analyze time series properties by running tests for unit roots in all wine and asset price series (defined as logs). Indeed, for variables that are not stationary, conventional assumptions for asymptotic analysis do not hold. Augmented Dickey-Fuller (ADF) tests are applied to the full data sample to test the stationarity of the variables. The model estimated is as follows:

∆ y t=α+βt+ϕ y t−1+∑i=1

p

γ iΔ y t−i+ut (1)

Where y tis the log of price series at time t ; ut is the contemporaneous error term and is assumed to be independent and identically distributed with zero mean and finite variance; α is a constant and β is the coefficient of a time-trend. The lag length p in all the tests was selected according to the Akaike Information Criteria (AIC). The null hypothesis is H 0 :ϕ=0 i.e. the series has a unit root or the series is non-stationary. The tests are conducted in terms both of level and of first-difference.

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Empirical evidence of stationarity in first-difference for a set of wine price and asset series justifies the cointegration analysis proposed by Johansen (1988) and Johansen and Juselius (1990). The use of a Vector Error Correction Model (VECM) then allows us to examine causal – uni- or bi-directional – relationships between the stock market price and wine prices and to take into account short-run deviations from long-run equilibrium relations. In the VECM, variables adjust to their long-run relationship.

The empirical specification of the VEC Model is defined as follows:

ΔY t=α0+Π ×Y t−1+∑i=1

p−1

Γ i×∆Y t−i+εt (2)

Where Y is the vector of endogenous variables (wine price series that are I (1 ) and the asset price series).Where Π=α×β '; α∧β are n×r matrices with n the number of variables in Y and r the rank of the matrix Π .β×Y t−1 represents the long-run relationships – or cointegrating relationships – between price series; β are the cointegrating vectors.α is a loading matrix giving the rate of adjustment of the variables in Y to the long-run equilibrium defined by the cointegrating relationships between endogenous variables. Short-run movements are

defined by ∑i=1

p−1

Γi×∆Y t−i with Γ i the matrix of coefficients of the lagged difference terms. We also

include a restricted constant and a time-trend in the cointegrating relationships.

The first step in the empirical analysis is to employ the auxiliary VAR methodology developed by Sims (1980) to determine the optimal lag length of the model. The choice of order p of the vector autoregression is based on standard information criteria that simultaneously avoids the problem of serial correlation of the error terms. The choice of the number of lags is a crucial input for the cointegration test (Endrész, 2011).

The second step is to determine the number of cointegrating vectors, i.e. the order of cointegration between our price series following Johansen’s methodology. The procedure involves estimating the matrix Π from the unrestricted VAR and determining its rank r . If the matrix Π is full-rank (r=n¿ then the model is a stationary VAR. This case can be excluded here as some series are found to be stationary in first difference, i.e. order 1 integrated. If the rank r=0 all variables of the model are I (1 ) but not related by a long-run relationship, i.e. they are not cointegrated. Finally, if the coefficient matrix Π has reduced rank r<n then r indicates the number of linearly independent combinations of non-stationary variables that are stationary. In this case, rrepresents the number of cointegrating relations.

Johansen derives two tests to determine the rank of Π, the first being the trace test (3) and the second the maximum Eigenvalue test (4). Both tests are based on the maximum likelihood ratio and are constructed as follows:

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Trace (r /n)=−T¿ ∑i=r+1

n

log (1− λ̂ i) (3)

Eigenvalue(r /n+1)=−T ¿ log (1− λ̂) (4)

where the λ̂ i is the estimated value for the ith ordered eigenvalues from the matrix Π . The trace statistic tests the null hypothesis of numbers of cointegrating vectors less than or equal to r , against the alternative that there are more than r . The maximum Eigenvalue statistic tests the null hypothesis of r against the alternative of r+1 cointegrating relations. Critical values for these two tests are provided by MacKinnon, Haug and Michelis (1999). When the null hypothesis fails to be rejected, indicating that a long-run relationship has been detected between series, then VECM can be applied to evaluate the short-run dynamics and long-run equilibrium of the cointegrated series (Engle and Granger, 1987).Finally, an empirical analysis of causality between variables is conducted using the Toda and Yamamoto (1995) procedure. Indeed, when series are cointegrated, the standard Granger non-causality test (Granger, 1969), based on an F-statistic, is not valid, because the test statistics do not have a standard distribution. This is due to the error correction term in the model, which implies several pre-tests of unit root and cointegration bias, as the causality test is sensitive to model specification and the number of lags (Phillips and Toda, 1993; Gujarati, 1995). The procedure proposed by Toda and Yamamoto (1995) is based on the estimation of an augmented VAR in which the asymptotic distribution of the Wald statistic, i.e. an asymptotic χ2 distribution, is assured. The procedure is robust to the integration order and cointegration properties of the economic series. The Toda and Yamamoto test method is based on the following VAR model where series are considered at level:

Y t=α+ ∑i=1

k+dmax

β iY t−i+εt

Where k is the optimal lag length of Y t determined using the standard information criteria, and dmax is the maximum order of integration of the series determined by unit root tests.Where ε t is the error terms that are assumed to be white noise with zero mean and constant variance. The procedure involves estimating the VAR (p) model where p=k+dmax and imposing restrictions

on parameters. The null non-causality can be expressed as H 0 :∑i=i

k

β i=0 and tested by a standard

Wald test. If the p-value of the Chi-Square statistic is less than 5%, then the null hypothesis of non-causality in the Granger sense should be rejected.

5. Interpretation of results

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The null hypothesis of non-stationarity is tested using Augmented Dickey-Fuller methodology. Results indicate that we cannot reject the null hypothesis of a unit root for 16 series, including the asset price series (Table 2)7.

Table 2: Unit Root TestsCategory Series Vintage ADF

Level First Difference

Burgundy

Domaine de la Romanée-Conti La Tâche 1990 1.323 -11.795***Domaine de la Romanée-Conti Richebourg 1990 -4.328*** -17.780***Domaine de la Romanée-Conti Romanée Saint Vivant 1990 -4.823*** -11.136***Domaine de la Romanée-Conti , Romanée-Conti 1990 -5.247*** -13.582***Vogüé-Musigny (Vieilles Vignes) 1990 0.607 -10.165***

Bordeauxfirst growth

Lafite Rothschild 1982 1.957 -13.499***Mouton Rothschild 1982 -2.212 -14.941***Haut-Brion 1989 -2.808 -16.818***Margaux 1996 1.066 -15.712***Latour 1990 -2.216 -15.868***

RhôneBaucastel-Chateauneuf du Pape 1989 -8.903*** -9.214***Chave-Hermitage 1990 -5.095*** -14.367***Jaboulet Ainé-Hermitage La Chapelle 1990 -4.657*** -8.714***

Italian

Tenuda San Guido – Sassicaia Bolgheri 1985 -3.988*** -12.838***Tenuda dell’ Ornellaia Ornellaia Bolgheri 1997 -6.536*** -11.574***Marchesi Antinori Tenuda Tignanello 1997 -7.252*** -9.384***Marchesi Antinori Solaia 1997 -6.912*** -7.771***

Australian Penfolds Grange 1998 -8.952*** -10.060***

Californian

Harlan estate 1997 -2.641 -17.670***Screaming Eagle 1999 -2.635 -15.575***Opus One 1997 -6.331*** -10.162***Dominus estate 1994 -2.482 -12.557***Bryant Family 1997 0.534 -12.621***

Bordeauxsecond growth

Cos d'Estournel 1982 -3.385** -12.006***Ducru Beaucaillou 1982 -6.161*** -9.927***Gruaud Larose 1982 1.38 -9.751***Leoville Barton 2000 -4.350*** -10.332***Leoville Las Cases 1982 -2.428 -9.196***Montrose 1990 -1.714 -10.178***Pichon Baron 1990 -3.766*** -9.742***Pichon Lalande 1982 -2.184 -12.441***

Portuguese Taylors's 1994 -5.578*** -13.284***MSCI WORLD U$ MCSI World -2.736 -8.241***Notes: ***, ** denotes significant at 1% and 5% level respectively using the t-stat approachAll series are in logs. In all cases, unit root tests have been applied using both the model with constant and the model with constant and a linear trend. The t-stats in the table are obtained with the model with constant and a linear trend for those cases in which the linear trend was significant. The lag length was selected in accordance with the Schwartz Information Criteria.

All these series are integrated of order 1, indicated as I (1). This test is more than a methodology step because stationarity in level or in first difference can be seen as a way to discriminate between collectible wines and financial assets respectively. Price series of the first generally follow a trend according to their valuation as collectibles (or drinkables), while the second seem to be more sensitive to financial shocks and are the focus of our paper.

7 Series that are found to be I (1) are: Domaine de la Romanée-Conti La Tâche, Vogüé-Musigny (Vieilles Vignes), Lafite Rothschild, Mouton Rothschild, Haut-Brion, Margaux, Latour, Harlan estate, Screaming Eagle, Dominus estate, Bryant Family, Gruaud Larose, Leoville Las Cases, Montrose, Pichon Lalande and MSCI index.

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The second step of our analysis is to determine the presence of long-run relationships between price series in our sample using the Johansen and Juselius multivariate cointegration procedure.

The optimal lag length of the model is determined using a VAR model and is selected so as to avoid any serial correlation in the error terms using multivariate LM tests. The null hypothesis assumes no serial correlation and fails to be rejected for four lags (see Annex 5). Thus the specification of the model takes on four lags. The Johansen test shows mixed results as to the number of cointegrating relations between price series. The null hypothesis for the trace test fails to be rejected for ten cointegrating relations. whereas the null hypothesis for the maximum Eigenvalue test fails to be rejected for six cointegrating relations – both at the 5% level. In the case of different results, the choice of a parsimony criterion allows us to retain the test based on the maximum Eigenvalue statistics. Table 3 reports the results of the Johansen and Juselius cointegration test.

Table 3Sample (adjusted): 2003M05 2012M10Included observations: 114 after adjustmentsTrend assumption: Linear deterministic trend (restricted)Series: LBO2_GRUAUD LBO2_LEOLASCASE LBO2_MONTROSE LBO2_PICHONLALANDE LB_LATACHE LB_VOGUES LCA_BRYANT LCA_DOMINUS LCA_HARLAN LCA_SREAMING LBO1_HBRION LBO1_LAFIROTHS LBO1_LATOUR LBO1_MARGAUX LBO1_MOUTROTHS LEI_MSCI_WORLDLags interval (in first differences): 1 to 3

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None  0.786705  176.1389  NA  NAAt most 1  0.676124  128.5229  NA  NAAt most 2  0.637772  115.7650  NA  NAAt most 3  0.606321  106.2730  NA  NA

At most 4 *  0.583869  99.95003  80.87025  0.0005At most 5 *  0.531476  86.43110  74.83748  0.0032At most 6  0.393340  56.97561  68.81206  0.3965At most 7  0.341781  47.67679  62.75215  0.6031At most 8  0.311890  42.61403  56.70519  0.5782At most 9  0.290713  39.15851  50.59985  0.4506

At most 10  0.260948  34.47217  44.49720  0.3964At most 11  0.219187  28.20586  38.33101  0.4411At most 12  0.160259  19.91142  32.11832  0.6591At most 13  0.109138  13.17455  25.82321  0.7901At most 14  0.094706  11.34250  19.38704  0.4787At most 15  0.048010  5.608926  12.51798  0.5115

 **MacKinnon-Haug-Michelis (1999) p-values

In addition, all characteristic roots lie inside the unit circle (see Annex 6). The finding obtained from the Johansen cointegration test is that stationary long-run relationships exist between wine and equity markets. That is, some price series are characterized by a long-run equilibrium, despite deviations in the short run. A VECM can then be applied.

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As previously mentioned, the next step of our analysis is to determine short-run causalities between price series using the Toda and Yamamoto (1995) procedure. Results are given in Table 4.

Some general comments may be advanced, given the short-run causality structure. Among the 16 markets of interest, 84 significant causal links are found at the 10% level and 65 at the 5% level. The equity market (MSCI world) is the most influential market in the Granger sense in the short run. All wine price series are (very) significantly influenced by the MSCI World index except Bryant Family, Château Montrose and Château Margaux. These exceptions may be explained by small changes in prices series of these wines over the period (even if they are I (1)), giving them a more collectible wine profile than a financial asset status. Among the wine markets, the most influential are Haut-Brion 1989 which significantly Granger-causes seven other wine markets, Screaming Eagle 1999 and La Tâche 1990 which both Granger-cause six others wine markets at 5%.

15

Table 4: short-run causalitiesGruaud Larose

Leoville las

cases

Montrose Pichon Lalande

DRC La Tâche

Vogue Musigny

Bryant Family

Dominus Harlan Screaming Eagle

Haut-Brion

Lafite Rothschild

Latour Margaux Mouton Rothschild

MSCI Index

is caused by

Gruaud Larose - 8.616 17.673 8.408 8.4600.035 0.001 0.038 0.037

Leoville las cases

- 9.184 8.685

0.027 0.034Montrose - 15.645

0.001Pichon Lalande - 7.953

0.047DRC La Tâche 10.030 - 8.162 10.297 11.566 20.538 8.110 7.976

0.018 0.043 0.016 0.009 0.000 0.044 0.047Vogue Musigny 10.473 12.887 7.926 - 10.014

0.015 0.005 0.048 0.018Bryant Family 14.563 8.365 - 9.055 14.089

0.002 0.039 0.029 0.003Dominus causes 10.072 7.963 - 10.186 10.670

0.018 0.047 0.017 0.014Harlan 9.610 - 11.720 10.611 7.625

0.022 0.008 0.014 0.054Screaming Eagle 10.320 25.859 - 7.932 10.922 14.458 12.037

0.016 0.000 0.047 0.012 0.002 0.007Haut-Brion 14.248 7.887 9.210 9.759 8.383 26.200 - 16.104

0.003 0.048 0.027 0.021 0.039 0.000 0.001Lafite Rothschild 8.479 -

0.037Latour 13.461 9.546 10.823 -

0.004 0.023 0.013Margaux 12.152 8.032 11.089 14.370 -

0.007 0.045 0.011 0.002Mouton Rothschild

10.997 13.946 -

0.012 0.003MSCI Index 36.200 8.589 9.069 14.356 13.138 14.031 9.772 14.587 17.670 23.009 13.775 8.995 -

0.000 0.035 0.028 0.003 0.004 0.003 0.021 0.002 0.001 0.000 0.003 0.029Notes: Figures in table are Wald statistics for Granger non-causality tests. Figures in italics are -the associated p-values that are less than 5%. Figures with an associated p-value that are more than 5% are not shown here as it means non-causality in the Granger-sense between series. Tests indicate Granger causality by row to column and Granger-caused by column to row.

16

17

More specifically, we can then observe linkages between products for each wine group. In Bordeaux first growths, the causality framework shows that Haut-Brion, Mouton Rothschild and Margaux Granger-cause Lafite Rothschild, and Lafite Rothschild Granger-causes Latour. Thus Haut-Brion and Mouton Rothschild emerge as the most exogenous variables and Lafite and Latour as the most endogenous. These results seem quite surprising for market operators, who often consider Lafite Rothshild as a very speculative and leader wine, while it appears here as the most endogenous. In the Californian group, Dominus and Screaming Eagle Granger-cause Bryant Family 1997 (at the 5% level) and Bryant Family Granger-causes Harlan Estate (at the 5% level) revealing a surprising Californian wine structure, which is very different from their price hierarchy (Fig 3). In the Burgundy group, La Tâche causes Vogüé-Musigny at a very significant level. Finally for the Bordeaux second growths, the most exogenous variables Montrose and Pichon Lalande Granger-cause Gruaud Larose which causes Léoville Las Case, the most endogenous wine of this group.

Moreover, between these wine groups the findings reveal causality linkages. Firstly, one should note the influence of Bordeaux first growths on second growths through the causalities of Haut-Brion, Margaux and Latour on Gruaud Larose, and the causalities of Haut-Brion and Mouton Rothschild on Pichon Lalande. Conversely, Léoville Las case causes Lafite Rothschild. These linkages between first and second growths of Bordeaux wines are consistent with their internal framework. Lafite Rothschild and Gruaud Larose are the most endogenous variables, and Haut-Brion and Mouton Rothschild the most exogenous. Château Montrose seems very independent of first growth wines. Secondly, analyses of the causalities between Bordeaux second growths and Burgundy wines appear very consistent, given their internal hierarchy. The most exogenous Burgundy (La Tâche) causes the most endogenous Bordeaux second growth (Léoville Las Case) and one of the most exogenous Bordeaux second growth (Pichon Lalande) causes the most dependent Burgundy (Vogüé-Musigny).Thirdly, we find the same consistency between Californian wines and Bordeaux second growths. The most exogenous second growth (Montrose) Granger-causes one of the most dependent Californian wines (Bryant Family), while the latter causes one of the most endogenous second growths (Gruand Larose). We find the same consistency between Burgundy and Californian wines in which the very dependent Vogüé Musigny is caused by Screaming Eagle, one of the most exogenous Californian wines. At the same time, the most independent Burgundy (La Tâche) causes one of the most exogenous Californian wines (Dominus), revealing the interdependence between these two categories of wines.

In contrast, relationships between first Bordeaux wines and the wines of California reveal a complex structure with little consistent causality. On one hand, the most independent Bordeaux wines (Haut-Brion and Mouton Rothschild) seem to cause the least endogenous Californian wines (Screaming Eagle and Bryant Family) and the most dependent wines of California cause the most endogenous Bordeaux (Lafite Rotschild and Latour). At this stage the inter-relationships are consistent, but the influence of the most endogenous wines of California (Harlan and Bryant Family) on the most exogenous Bordeaux (Haut Brion, Mouton Rothschild and Margaux) creates a circular relationship. Insofar as all the relationships are significant, there are probably successive cross-linkages during the observed period. In particular, the rise of the New York auction market could give an influence to Californian wines that has hitherto characterized Bordeaux wines.

Finally, this short-run causality framework reveals the existence of strong exogenous wines both in their category and between categories. La Tâche, Haut-Brion, Montrose and Screaming Eagle appear

18

as leaders of auction wine markets, as they Granger-cause both the other wines in their category and the endogenous wines of other categories. Between them, a hierarchy appears in the short run, where La Tâche causes Screaming Eagle, Haut-Brion and La Tâche have a bi-directional causality, and Montrose remains independent of the others. This short-run causality framework is largely consistent with long-run causality relationships.

Indeed as revealed by the Johansen tests, our price series have a long-run relationship between them, justifying the use of VECM for analyzing short-run dynamics. The cointegrating equations and the loading parameters are presented in Table 5. In the top panel of Table 5, coefficients that have been normalized to 1 in the maximum likelihood procedure concern the following price series: Château Gruaud-Larose, Château Leoville Las Case, Château Montrose, Château Pichon-Lalande, Château La Tâche and Château Vogue-Musigny. In the absence of underlying economic theories, the choice of the six endogenous variables for which long-run relationships have been assumed relies on (previously presented) short-run causalities. In fact, the variables selected for three of them are the most endogenous within wine groups – Château Gruaud-Larose, Château Leoville Las Case and Château Vogue-Musigny – and for the last three of them the most endogenous among the different wine groups – Château Montrose, Château Pichon-Lalande and Château La Tâche.The bottom panel of Table 5 shows loading coefficients associated with the six normalized restricted cointegrating vectors. Those coefficients of the error-correction terms can be interpreted as speed of adjustment of the variables towards their long-run equilibrium. Results show that the adjustment coefficients are negative and statistically significant at the10% level for Château Gruaud-Larose and at the 1% level for all others series except Château Montrose, which has the expected sign but is not statistically significant. When deviations from the long-run equilibrium occur, equilibrium-correcting mechanisms allow price series to adjust and revert back to their own equilibrium.

Generally in terms of portfolio diversification, whether or not the short-run causality framework suggests some expected gains for investors, in the long-run asset prices adjust to their long-run trends.

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Table 5 Vector Error Correction Estimates Sample (adjusted): 2003M05 2012M10 Included observations: 114 after adjustments Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq: CointEq1 CointEq2 CointEq3 CointEq4 CointEq5 CointEq6

LBO2_GRUAUD(-1) 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000

LBO2_LEOLASCASE(-1) 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000

LBO2_MONTROSE(-1) 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000

LBO2_PICHONLALANDE(-1) 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000

LB_LATACHE(-1) 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000

LB_VOGUES(-1) 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000

LCA_BRYANT(-1) 0.298026 -1.253318 -0.808337 0.538008 0.583262 -0.071215[ 2.10671] [-7.20113] [-5.62662] [ 2.76356] [ 3.37793] [-0.62010]

LCA_DOMINUS(-1) -0.709525 1.432578 1.147701 -1.390684 -0.875627 0.175595[-3.78867] [ 6.21764] [ 6.03464] [-5.39606] [-3.83066] [ 1.15497]

LCA_HARLAN(-1) -0.594152 0.294588 0.089074 -0.825829 -0.696971 0.591507[-3.75889] [ 1.51483] [ 0.55490] [-3.79648] [-3.61253] [ 4.60957]

LCA_SREAMING(-1) -0.410485 -0.214278 -0.173711 -0.001029 -0.774359 -1.476855[-2.09764] [-0.89002] [-0.87411] [-0.00382] [-3.24198] [-9.29628]

LBO1_HBRION(-1) 1.794319 0.380438 1.059425 0.089997 1.514533 1.173279[ 5.91483] [ 1.01933] [ 3.43887] [ 0.21558] [ 4.09031] [ 4.76412]

LBO1_LAFIROTHS(-1) 0.031568 0.491084 -0.377898 0.137494 -0.238402 0.014249[ 0.30337] [ 3.83593] [-3.57606] [ 0.96015] [-1.87703] [ 0.16867]

LBO1_LATOUR(-1) -1.093474 0.037495 -0.516586 0.728396 -2.800777 0.551339[-3.23319] [ 0.09011] [-1.50407] [ 1.56502] [-6.78479] [ 2.00808]

LBO1_MARGAUX(-1) -1.123116 0.634579 1.096384 -1.242402 -0.763938 0.073970[-5.87070] [ 2.69612] [ 5.64327] [-4.71907] [-3.27158] [ 0.47628]

LBO1_MOUTROTHS(-1) 0.964787 -2.914243 -1.822227 1.099146 2.467711 -1.531764[ 3.42472] [-8.40828] [-6.36941] [ 2.83516] [ 7.17667] [-6.69767]

LEI_MSCI_WORLD(-1) 0.049902 0.632817 -0.323666 -0.297662 0.109758 -0.267559[ 0.30585] [ 3.15251] [-1.95339] [-1.32569] [ 0.55114] [-2.01999]

@TREND(03M01) -0.009435 -0.005046 -0.006127 -0.000528 -0.010283 -0.003543[-6.16289] [-2.67898] [-3.94099] [-0.25041] [-5.50292] [-2.85059]

C -1.459152 -0.488385 1.097453 -0.586650 0.565959 0.330717

Error Correction: D(LBO2_GRUAUD) D(LBO2_LEOLASCASE)

D(LBO2_MONTROSE) D(LBO2_PICHONLALANDE)

D(LB_LATACHE) D(LB_VOGUES)

CointEq1 -0.488433[-1.88176]

CointEq2 -0.721230[-3.45996]

CointEq3 -0.178861[-0.83780]

CointEq4 -0.463371[-2.34580]

CointEq5 -0.790268[-2.79847]

CointEq6 -0.647221[-4.22328]

Adj. R-squared 0.599779 0.377656 0.469294 0.250368 0.375535 0.487471

Notes: figures in the f irst part of the table are the estimated coefficients associated to the long-run relationships (figures in [ ] are the t-statistics). In the second part of the table, f igures represents the speed of adjustement of the system tow ards long-run equilibrium. t-stats are in []

20

6. Concluding remarks

This paper focuses on long-run and short-run relationships among 32 wine markets and the global equity market (MSCI Word Index) over the period 2003-2012 using traditional cointegration methodology. This work appears to be relevant in the current literature as it covers a large database of wine auctions which allows causation to be analyzed between real wines rather than between wine price indices. Firstly, our findings differentiate the most sensitive wines to financial markets (alternative assets) from those that should be considered more as collectibles or drinkables. Causation in the short term with the MSCI World tends to indicate (as in other collectibles markets, such as art or stamps) that the profits generated in financial markets are reinvested on wine markets. Secondly, the causation framework among wines markets reveals strong significant short- and long-run relationships, in which some wine-vintage pairs appear to be either leaders or followers. On the one hand, this framework suggests that the choices in a portfolio diversification strategy may not lead to returns that are as large as expected. On the other hand, opportunities for diversification may still exist owing to the exogenous characteristics (Haut-Brion 1989, La Tâche 1990, Screaming Eagle 1999, Montrose 1990) or stationary trends (e.g. Rhone, Italian or Australian wines) of some wine price series. Nevertheless, the medium explanatory power (with R²adj around 50%) of long-run equations points to the relevance of omitted variables in expected wine prices. Moreover, even though our methodological choice concerning wine-vintage pairs rather than indices is relevant for investors, work may be required on more significant wine indices, which are able to produce a broader explanatory scope by aggregating the most traded vintages of the same wine.

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23

Annex 1. Correlation matrix (Spearman) beween the most traded vintages (1982, 1986, 1989, 1990, 1995, 1996, 2000) of each Bordeaux first growth, Château Haut-Brion (HB), Château Lafite Rothschild (LR), Château Latour (LT), Château Margaux (MG), Château Mouton Rothschild (MR)

24

Château Haut-BrionVariables HB2000 HB1996 HB1995 HB1990 HB1989 HB1986 HB1982

HB2000 1 0.92003321 0.93935538 0.93822399 0.94495519 0.85434886 0.91903694HB1996 0.92003321 1 0.94320039 0.92057223 0.92699718 0.8866917 0.87796126HB1995 0.93935538 0.94320039 1 0.93803191 0.96140414 0.90527154 0.89446139HB1990 0.93822399 0.92057223 0.93803191 1 0.94605002 0.8829979 0.92418452HB1989 0.94495519 0.92699718 0.96140414 0.94605002 1 0.91701232 0.93041715HB1986 0.85434886 0.8866917 0.90527154 0.8829979 0.91701232 1 0.85112279HB1982 0.91903694 0.87796126 0.89446139 0.92418452 0.93041715 0.85112279 1

Château Lafite RothschildVariables LR2000 LR1996 LR1995 LR1990 LR1989 LR1986 LR1982

LR2000 1 0.96741828 0.97848435 0.97424749 0.9698399 0.97184265 0.98092053LR1996 0.96741828 1 0.97865182 0.98359926 0.97470178 0.98126183 0.97876939LR1995 0.97848435 0.97865182 1 0.98749536 0.98354986 0.97535209 0.9791053LR1990 0.97424749 0.98359926 0.98749536 1 0.98907269 0.98310477 0.98364191LR1989 0.9698399 0.97470178 0.98354986 0.98907269 1 0.98005207 0.98300326LR1986 0.97184265 0.98126183 0.97535209 0.98310477 0.98005207 1 0.97846336LR1982 0.98092053 0.97876939 0.9791053 0.98364191 0.98300326 0.97846336 1

Château LatourVariables LT2000 LT1996 LT1995 LT1990 LT1989 LT1986 LT1982

LT2000 1 0.96014323 0.95769025 0.91343787 0.89142884 0.88940786 0.93656633LT1996 0.96014323 1 0.96603364 0.93635377 0.91135746 0.90308966 0.94277299LT1995 0.95769025 0.96603364 1 0.9132414 0.93768197 0.92841157 0.9358826LT1990 0.91343787 0.93635377 0.9132414 1 0.89592665 0.86407842 0.92596701LT1989 0.89142884 0.91135746 0.93768197 0.89592665 1 0.92868573 0.8973919LT1986 0.88940786 0.90308966 0.92841157 0.86407842 0.92868573 1 0.90928492LT1982 0.93656633 0.94277299 0.9358826 0.92596701 0.8973919 0.90928492 1

Château MargauxVariables MG2000 MG1996 MG1995 MG1990 MG1989 MG1986 MG1982

MG2000 1 0.93557057 0.92281491 0.9277592 0.84167885 0.88830533 0.85352095MG1996 0.93557057 1 0.95353135 0.93341367 0.87511441 0.94036604 0.9155111MG1995 0.92281491 0.95353135 1 0.93389701 0.86186715 0.92507112 0.90596051MG1990 0.9277592 0.93341367 0.93389701 1 0.86791118 0.93347735 0.9079462MG1989 0.84167885 0.87511441 0.86186715 0.86791118 1 0.87319532 0.84961332MG1986 0.88830533 0.94036604 0.92507112 0.93347735 0.87319532 1 0.93197399MG1982 0.85352095 0.9155111 0.90596051 0.9079462 0.84961332 0.93197399 1

Château Mouton RothschildVariables MR2000 MR1996 MR1995 MR1990 MR1989 MR1986 MR1982

MR2000 1 0.94088746 0.93058511 0.88681863 0.86167413 0.87196249 0.87666848MR1996 0.94088746 1 0.95816358 0.9274844 0.920048 0.92056333 0.92609202MR1995 0.93058511 0.95816358 1 0.9424032 0.92982066 0.92423206 0.93765377MR1990 0.88681863 0.9274844 0.9424032 1 0.94707011 0.89898264 0.89748575MR1989 0.86167413 0.920048 0.92982066 0.94707011 1 0.90753247 0.90744751MR1986 0.87196249 0.92056333 0.92423206 0.89898264 0.90753247 1 0.97179963MR1982 0.87666848 0.92609202 0.93765377 0.89748575 0.90744751 0.97179963 1Values in bold are different from 0 with a significance level alpha=0. .05

Annex 2. Correlation matrix (Spearman) beween the most traded vintages (1982, 1986, 1989, 1990, 1995, 1996, 2000) of each Bordeaux second growth, Château Léoville Las Case (LV), Château Montrose (MT), Château Pichon Lalande (PL), Château Gruaud Larose (GL)

25

Château Léoville Las CaseVariables LV2000 LV 1996 LV 1995 LV 1990 LV 1989 LV 1986 LV 1982LV2000 1 0.798075 0.78128493 0.80753386 0.76192318 0.70845796 0.75630385LV 1996 0.798075 1 0.83498468 0.82581833 0.81460421 0.73445693 0.73544508LV 1995 0.78128493 0.83498468 1 0.79650186 0.77625941 0.66872022 0.72180283LV 1990 0.80753386 0.82581833 0.79650186 1 0.82316024 0.76636701 0.81479195LV 1989 0.76192318 0.81460421 0.77625941 0.82316024 1 0.75097069 0.74508318LV 1986 0.70845796 0.73445693 0.66872022 0.76636701 0.75097069 1 0.7821155LV 1982 0.75630385 0.73544508 0.72180283 0.81479195 0.74508318 0.7821155 1

Château MontroseVariables MT 2000 MT 1996 MT 1995 MT 1990 MT 1989 MT 1986 MT 1982MT 2000 1 0.87145392 0.81274109 0.85884304 0.83018455 0.46552036 0.70474973MT 1996 0.87145392 1 0.87111519 0.84722384 0.85646068 0.45762206 0.81143111MT 1995 0.81274109 0.87111519 1 0.8421575 0.82413004 0.28420417 0.77339901MT 1990 0.85884304 0.84722384 0.8421575 1 0.89624189 0.54406472 0.83758347MT 1989 0.83018455 0.85646068 0.82413004 0.89624189 1 0.46307522 0.78118378MT 1986 0.46552036 0.45762206 0.28420417 0.54406472 0.46307522 1 0.54139685MT 1982 0.70474973 0.81143111 0.77339901 0.83758347 0.78118378 0.54139685 1

Château Pichon LalandeVariables PL 2000 PL 1996 PL 1995 PL 1990 PL 1989 PL 1986 PL1982PL 2000 1 0.78286929 0.80901083 0.61725844 0.77973654 0.70892967 0.77411856PL 1996 0.78286929 1 0.86759137 0.63479888 0.79530975 0.75378084 0.77714121PL 1995 0.80901083 0.86759137 1 0.72037278 0.82890365 0.79773274 0.82099961PL1990 0.61725844 0.63479888 0.72037278 1 0.65112019 0.61276433 0.69242578PL 1989 0.77973654 0.79530975 0.82890365 0.65112019 1 0.70226422 0.79095362PL 1986 0.70892967 0.75378084 0.79773274 0.61276433 0.70226422 1 0.78107979PL1982 0.77411856 0.77714121 0.82099961 0.69242578 0.79095362 0.78107979 1

Château Gruaud LaroseVariables GL 2000 GL 1996 GL 1995 GL 1990 GL 1989 GL 1986 GL 1982GL2000 1 0.72728955 0.60405187 0.73686521 0.7897016 0.7414913 0.76076252GL 1996 0.72728955 1 0.68972332 0.81507824 0.70222854 0.75278365 0.83977798GL 1995 0.60405187 0.68972332 1 0.73899756 0.65892421 0.68081251 0.77747289GL 1990 0.73686521 0.81507824 0.73899756 1 0.70517268 0.77505275 0.80325174GL 1989 0.7897016 0.70222854 0.65892421 0.70517268 1 0.74637827 0.77464789GL 1986 0.7414913 0.75278365 0.68081251 0.77505275 0.74637827 1 0.8382586GL 1982 0.76076252 0.83977798 0.77747289 0.80325174 0.77464789 0.8382586 1

Annex 3. Correlation matrix (Spearman) beween the most traded vintages of each selected Burgundy, Vogüé-Musigny Vieilles Vignes (VM) and Domaine de la Romanée-Conti La Tâche (TA)

Vogüé-Musigny Vieilles VignesVariables VM 2002 VM 1999 VM 1996 VM 1995 VM 1990 VM 1988 VM 1985

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VM 2002 1 0.55805758 0.44679559 0.71239758 0.51448737 0.61832061 0.16799513VM 1999 0.55805758 1 0.49054858 0.78858663 0.65943945 0.72019064 0.6039865VM 1996 0.44679559 0.49054858 1 0.6643645 0.50952487 0.58430842 0.24664889VM 1995 0.71239758 0.78858663 0.6643645 1 0.63785142 0.62988097 0.47987616VM 1990 0.51448737 0.65943945 0.50952487 0.63785142 1 0.49143733 0.54843267VM 1988 0.61832061 0.72019064 0.58430842 0.62988097 0.49143733 1 0.6969697VM 1985 0.16799513 0.6039865 0.24664889 0.47987616 0.54843267 0.6969697 1

Domaine de la Romanée-Conti La TâcheVariables TA 1999 TA 1996 TA 1995 TA 1990 TA 1988 TA 1985TA 1999 1 0.889 0.896 0.836 0.905 0.658TA 1996 0.889 1 0.932 0.893 0.938 0.782TA 1995 0.896 0.932 1 0.89 0.922 0.692TA 1990 0.836 0.893 0.89 1 0.915 0.825TA 1988 0.905 0.938 0.922 0.915 1 0.774TA 1985 0.658 0.782 0.692 0.825 0.774 1Values in bold are different from 0 with a significance level alpha=0.05

Annex 4. Correlation matrix (Spearman) beween the most traded vintages of each selected Californians, Harlan (HL), Dominus (DO), Screaming Eagle (SE), Bryant Family (BF)

HarlanVariables HL 2001 HL 2000 HL 1999 HL 1997 HL 1994HL 2001 1 0.64395604 0.41388164 0.66102989 0.67698364HL 2000 0.64395604 1 0.62108339 0.59778729 0.64820399HL 1999 0.41388164 0.62108339 1 0.61220314 0.52025854HL 1997 0.66102989 0.59778729 0.61220314 1 0.75875602HL 1994 0.67698364 0.64820399 0.52025854 0.75875602

DominusVariables DO 1997 DO 1996 DO 1994 DO 1992 DO 1991DO 1997 1 0.6169401 0.56675084 0.32011565 0.56483993DO 1996 0.6169401 1 0.59364234 0.44120768 0.54079874DO 1994 0.56675084 0.59364234 1 0.56637435 0.74585338DO 1992 0.32011565 0.44120768 0.56637435 1 0.60100071DO 1991 0.56483993 0.54079874 0.74585338 0.60100071 1

Screaming eagleVariables SE 2003 SE 2002 SE 2001 SE 1999 SE 1997SE 2003 1 0.56431989 0.52682081 0.69683258 0.51404422SE 2002 0.56431989 1 0.75356127 0.59316189 0.65340372SE 2001 0.52682081 0.75356127 1 0.66090006 0.61094456SE 1999 0.69683258 0.59316189 0.66090006 1 0.6077394SE 1997 0.51404422 0.65340372 0.61094456 0.6077394 1

Bryant FamilyVariables BF 2001 BF 1997 BF 1996 BF 1995 BF 1994BF 2001 1 0.17729543 0.21166705 0.19071774 0.29417621BF 1997 0.17729543 1 0.64058922 0.48275205 0.38957081BF 1996 0.21166705 0.64058922 1 0.78472972 0.48503761BF 1995 0.19071774 0.48275205 0.78472972 1 0.56457431BF 1994 0.29417621 0.38957081 0.48503761 0.56457431 1

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Annex 5: VAR Residual Serial Correlation LM TestsNull Hypothesis: no serial correlation at lag order hDate: 01/27/14 Time: 17:20Sample: 2003M01 2012M10Included observations: 115

Lags LM-Stat Prob

1 204.7778 0.03152 235.3692 0.00063 223.9060 0.00304 152.4908 0.8139

Probs from chi-square with 169 df.

Annex 6

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

Inverse Roots of AR Characteristic Polynomial

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