Price formation in parallel trading systems: Evidence from the fine wine market

Abstract

What drives the prices of fine wines is not easy to discern, in view of a multitude of confounding factors characterising the transactions across several markets. At the same time, understanding the quantitative relationships and mechanisms that determine the price level is important for policy making (e.g. predicting the outcomes of regulations) and methodological purposes (which elements to consider in modelling these markets). We examine the price formation of fine wines simultaneously across three markets: an automated electronic exchange (Liv-ex), intermediated auctions, and over-the-counter (OTC). We use a unique dataset consisting of 99,769 price data points for Premier Cru Bordeaux fine wines and we examine the price determinants with Bayesian modelling. We ascertain the mean price ranking (OTC market being the most expensive and Livex the least, differing by about 4.5% and-0.8% from the auctions). We also find a slight price decrease for larger transactions (approx. 0.3% reduction for a 10% volume increase) and some platykurtosis in price distribution (greatest in Liv-ex), and observe the most stochastic noise in auctions. In an agent-based simulation, we discover that it is necessary to include trading mechanisms, commissions, and OTC market heterogeneity to reproduce the observed characteristics. Our results indicate which elements should be included in future fine wine markets models.