Price indices for heterogenous goods such as real estate or fine art constitute crucial information for institutional or private investors considering alternative investments in times of financial markets turmoil. Classical mean-variance analysis of alternative investments has been hampered by the lack of a systematic treatment of volatility in these markets. This may seem surprising as derivatives on subsets of the traded goods require a precise modelling and estimation of the underlying volatility. For example, in art markets, auction houses often give price guarantees to the seller that resemble put options. In this paper we propose a hedonic regression framework which explicitly defines an underlying stochastic process for the price index, allowing to treat the volatility parameter as the object of interest. The model can be estimated using maximum likelihood in combination with the Kalman filter. We derive theoretical properties of the volatility estimator and show that it outperforms the standard estimator. We show that extensions to allow for time-varying volatility are straightforward using a local-likelihood approach. In an application to a large data set of international blue chip artists, we show that volatility of the art market, although generally lower than that of financial markets, has risen over the last years and, in particular, during the recent European debt crisis.
Volatility heterogenous goods hedonic regression random effects