Can 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.

This paper extends the benchmark macro-finance (MF) model by introducing, next to the standard macroeconomic factors, additional liquidity-related and return-forecasting factors. Liquidity factors are obtained from a decomposition of the money market spread, while the return-forecasting (risk premium) factor is extracted by imposing a single-factor structure on the 1-period expected excess holding return. The model is estimated on U.S. data using Markov chain Monte Carlo techniques. Two findings stand out. First, the model significantly outperforms most structural and nonstructural MF yield curve models in terms of the cross-sectional fit of the yield curve. Second, financial shocks have a statistically and economically significant impact on the yield curve.