MULTILEVEL MODELS WITH STOCHASTIC VOLATILITY FOR REPEATED CROSS-SECTIONS: AN APPLICATION TO TRIBAL ART PRICES

• 成果类型: Article
• 署名作者: Cagnone, Silvia; Giannerini, Simone; Modugno, Lucia
• 署名单位: University of Bologna
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/17-AOAS1035
• 发表日期: 2017
• 页码: 1040-1062
• 关键词: maximum-likelihood-estimation; time-series; dependence; MARKETS

摘要

In this paper, we introduce a multilevel specification with stochastic volatility for repeated cross-sectional data. Modelling the time dynamics in repeated cross sections requires a suitable adaptation of the multilevel framework where the individuals/items are modelled at the first level whereas the time component appears at the second level. We perform maximum likelihood estimation by means of a nonlinear state space approach combined with Gauss-Legendre quadrature methods to approximate the likelihood function. We apply the model to the first database of tribal art items sold in the most important auction houses worldwide. The model allows to account properly for the heteroscedastic and autocorrelated volatility observed and has superior forecasting performance. Also, it provides valuable information on market trends and on predictability of prices that can be used by art markets stakeholders.