Forecasting Art Prices with Bayesian Models

Vandana -, Deepmala -, Krzysztof Drachal, Lakshmi Narayan Mishra

Abstract

In this paper several potential art price determinants are considered. For example, stock market indices, other commodity prices, exchange rates, GDP, disposable income, consumption, interest rates, etc. The analysis is based on quarterly data starting in 1998 and ending in 2015. The methodology is based on BMA (Bayesian Model Averaging) and DMA (Dynamic Model Averaging), which is applicable in case of the uncertainty about the suitable predictors. Prices of various type of art goods are analysed. The results suggest that art market is quite a complex one and even in case of including many predictors it is hard to model. However, it is found that DMA outperforms BMA.