Fine Wine Prices Research Articles

Message in a bottle: Forecasting wine prices

AbstractCan we predict fine wine and alcohol prices? Yes, but it depends on the forecasting horizon. We make this point by considering the Liv-ex Fine Wine 100 and 50 Indices, the retail and wholesale alcohol prices in the United States for the period going from January 1992 to March 2022. We use rich and diverse datasets of economic, survey, and financial variables as potential price drivers and adopt several combination/dimension reduction techniques to extract the most relevant determinants. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol categories. We show that it is possible to predict fine wine prices for the 2-year horizon and retail/wholesale alcohol prices at horizons ranging from 1 month to 2 years. Our findings stress the importance of including consumer survey data and macroeconomic factors, such as international economic factors and developed markets equity risk factors, to enhance the precision of predictions of retail/wholesale (fine wine) prices.

Persistence in the passion investment market

Research on the passion investment market is still limited, especially on the issue of persistence. This paper is the first to investigate it using both R/S analysis and fractional integration in the case of 3 fine wine price indices, 10 diamond price indices, 15 art price indices, and 1 stamp price index at the daily, monthly and quarterly frequency. The results can be summarised as follows: wine prices are found to be highly persistent, whilst stamp prices appear to be only weakly persistent, though they can still be characterised as a long-memory process; as for diamond prices, they can be persistent (Diamonds & Gems), anti-persistent (Diamonds Carat indices) or even random (Polished Prices Diamond Index). The dynamic R/S analysis also shows that persistence is time-varying and tends to fluctuate around the average. These findings can be explained by the different degree of liquidity of the assets examined and have implications for both academics investigating market efficiency and practitioners interested in forecasting prices.

The Influence of Economic Policy Uncertainty and Business Cycles on Fine Wine Prices.

This study investigates the impact of both economic policy uncertainty (EPU) and business cycles on the fine wine market. We use a nonlinear autoregressive distributed lag model to measure the influence of these two variables on three major Liv-ex indices over the period 2005M01–2020M12. Our results are multiple. First, fine wine prices are relatively unaffected asymmetrically by EPU, while the economic cycle has a more pronounced asymmetric effect, especially in the short run. Second, uncertainty in Europe and the USA affect fine wine prices more than in China. Third, in the short term, fine wine prices react more strongly to changes in business cycles than to uncertainty. Finally, prices of the five first growths of Bordeaux are asymmetrically influenced by EPU, unlike of the rest of the most prestigious Bordeaux wines. The study also has implications for investment. We argue that a strong and professional strategic intelligence watch would help stakeholders in the secondary wine market to improve their returns, especially when European and US wines are involved. While short-runners should focus on information relative to changes in the business cycle, long-term investors would find it more interesting to closely monitor policy decisions liable to have long-term effects on wine prices (such as taxation, monetary measures…).

Economic policy uncertainty and fine wine prices

PurposeThe paper aims to investigate the impact of economic policy uncertainty (EPU) (i.e. uncertainty due to government policies) on fine wine prices.Design/methodology/approachThe paper uses the Baker et al. (2016) monthly news-based measure of EPU for the leading wine markets: the USA, the UK, France, Germany and China in conjunction with monthly fine wine pricing data from the London International Vintners Exchange (Liv-ex). The wine sub-indices used are the Liv-ex 500 (Bordeaux), Burgundy 150, Champagne 50, Rhone 100, Italy 100, California 50, Port 50 and Rest of the World 50. The Prais–Winsten and Cochrane–Orcutt regressions are used for our analyses to correct for effects of serial correlation. Time lags are chosen based on the appropriate information criterion.FindingsChanges in EPU levels negatively impact changes in the Liv-ex 500 index for all our leading wine markets except France, the Champagne 50 index for the UK and the Burgundy 150 and the Rhone 100 indices for Germany, with the effects being significant for at least up to a quarter before EPU is detected. The authors did not find significant results for the EPU of France.Practical implicationsThe paper aims to provide insights into whether EPU creates opportunities or threats for investors and wineries.Originality/valueA forward-looking news-based EPU measure is used to gain insights into how the different Liv-ex sub-indices react to increases in uncertainty centered around government policies across a sample of different countries.

Heterogeneity and fine wine prices: application of the quantile regression approach

ABSTRACT This study addresses the price heterogeneity of the five first growths of Bordeaux. We apply the quantile regression (QR) approach with market segmentation based on wine bottle price quantiles. We compute the hedonic price of wine attributes for various price segments in the market. This approach is applied to a major dataset comprising approximately 50,000 transactions over the 2003–2017 period. The findings indicate that the relative hedonic prices of several wine attributes differ significantly among deciles. The implications of our results are manifold. Vintage and Parker grades have a strong impact on the variation in wine prices, and there is a hierarchy among the five first growths of Bordeaux. There is also a premium commanded by the reputation and experience of an auction house. Since the financial crisis of 2012–2013, investors have considered that the five first growths are overrated, save for the most expensive wines; for those most expensive ones, investors prefer scarcity to liquidity. These results are of import to several actors in the fine wine market: investors, for example, could use the findings herein to better diversify their wine portfolio, while auction houses could better anticipate their future sales based on consumers’ expectation.

The long-term financial drivers of fine wine prices: The role of emerging markets

We analyze the long-term relationship between the fine wine market and stock markets, from world level to specific countries, by applying a cointegration approach on a 21-year monthly database. Our database includes the Liv-ex Fine Wine Investables Index, 6 Liv-ex sub-indices, 25 MSCI Indexes and 10 national stock market indexes. Results suggest significant cointegration between emerging markets, especially Asia, and fine wine markets, and also causality from the former to the latter. As a result, the slowdown of economic growth in emerging countries can be a risk to the fine wine market. Furthermore, Mainland China appears to be one of the main drivers of fine wine markets, and more precisely, the leading Bordeaux wines. We suggest that along with single-index analysis, our approach can be taken into consideration in fine wine price forecasting and portfolio diversification strategy.

Machine Learning in Fine Wine Price Prediction

AbstractAdvanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)

A Barrel of Oil or a Bottle of Wine: How Do Global Growth Dynamics Affect Commodity Prices?

AbstractThis paper explores empirically the causes of extreme fluctuations in commodity prices from January 1990 to June 2010 and seeks to identify the relative contribution of advanced and emerging market economies to the changes in commodity prices. Our assumption is that analyzing two very distinct goods—crude oil and fine wine—helps to identify common determinants of commodity prices. We find that the growth rate of global aggregate demand is the key macroeconomic determinant of the fluctuations in both crude oil and fine wine prices over the sample period. While advanced economies account for more than half of global consumption, emerging market and developing economies make up the bulk of the incremental change in demand, thereby having a greater weight in commodity price formation. The coefficient of emerging market industrial output growth is about three times as high as that of advanced economies in oil price regressions and almost five times as powerful in fine wine price regressions. The results also show that the shift in the composition of aggregate commodity demand is a recent phenomenon. (JEL Classifications: Q11, Q39, Q41, Q43)

Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries

This study provides the first attempt to examine the ability of the price of fine wine to forecast the Gross Domestic Product (GDP) for the major developed countries. Considering the limitation of a linear Granger causality test in detecting nonlinear causal relationships, a nonlinear Granger causality test is also employed. The results from our nonlinear causality test show that this new variable contains useful information to forecast GDP for the US, the UK, and Australia, suggesting that we may include it as a forecasting variable in GDP forecasting models, especially nonlinear models, for these three countries.

Wine price risk management: International diversification and derivative instruments

Variations in fine wine prices can be prominent and have widespread economic and financial implications. Although fine wine investments are dominated by French wines, we demonstrate that significant international diversification benefits exist for investors in Italian, Australian and Portuguese fine wines. This is important since we also find that diversification across varieties of French wine is not likely to be that effective. We propose the development of futures and options contracts on standardized wine price indices in order to enhance market completeness and to address the risk management needs of all market participants. Several popular continuous time processes are used to approximate empirically the dynamics of four fine wine price indices. On the basis of our results, we recommend appropriate equilibrium models for pricing fine wine futures and option contracts.

Wine Price Risk Management: International Diversification and Derivative Instruments

Variations in fine wine prices can be prominent and have widespread economic and financial implications. Although fine wine investments are dominated by French wines, we demonstrate that significant international diversification benefits exist for investors in Italian, Australian and Portuguese fine wines. This is important since we also find that diversification across varieties of French wine is not likely to be that effective. We propose the development of futures and options contracts on standardized wine price indices in order to enhance market completeness and to address the risk management needs of all market participants. Several popular continuous time processes are used to approximate empirically the dynamics of four fine wine price indices. On the basis of our results, we recommend appropriate equilibrium models for pricing fine wine futures and option contracts.