Econometric analysis of volatile art markets

Fabian Y. R. P. Bocart and Christian Hafner

No 2011-071, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: volatility; art markets; hedonic regression; semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C14 C43 Z11 (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/56719/1/671186922.pdf (application/pdf)

Related works:
Journal Article: Econometric analysis of volatile art markets (2012)
Working Paper: Econometric analysis of volatile art markets (2012)
Working Paper: Econometric analysis of volatile art markets (2011)
Working Paper: Econometric analysis of volatile art markets (2011)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2011-071

Access Statistics for this paper

More papers in SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().