Multilevel Models with Stochastic Volatility for Repeated Cross-Sections: an Application to tribal Art Prices

TL;DR

This paper introduces a multilevel model with stochastic volatility tailored for repeated cross-sectional data, applied to tribal art prices, improving volatility modeling, forecasting, and market trend analysis.

Contribution

It develops a novel multilevel stochastic volatility model for repeated cross sections, specifically applied to tribal art prices, enhancing market trend understanding.

Findings

Model captures heteroscedastic and autocorrelated volatility.

Provides superior forecasting performance.

Offers insights into market trends and price predictability.

Abstract

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.