Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
417527 | Computational Statistics & Data Analysis | 2012 | 14 Pages |
Abstract
A new heteroskedastic hedonic regression model is suggested which takes into account time-varying volatility and is applied to a blue chips art market. A nonparametric local likelihood estimator is proposed, and this is more precise than the often used dummy variables method. The empirical analysis reveals that errors are considerably non-Gaussian, and that a Student distribution with time-varying scale and degrees of freedom does well in explaining deviations of prices from their expectation. The art price index is a smooth function of time and has a variability that is comparable to the volatility of stock indices.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Fabian Y.R.P. Bocart, Christian M. Hafner,