financial crises caused by the Lehman bankruptcy and the European sovereign debt problems have increased the interest

Abstract 

We provide an innovative methodological contribution to the measurement of returns on infrequently traded assets using a novel approach to repeat sales regression estimation. The model for price indices we propose allows for correlation with other markets, typically with higher liquidity and high-frequency trading. Using the new econometric approach, we propose a monthly art market index, as well as sub- indices for Impressionist, Modern, Post-War, and Contemporary paintings based on repeated sales at a monthly frequency. The correlations enable us to update the art index via observed transactions in other markets that have a link with the art market. In terms of the Sharpe ratio, we find that Contemporary art appears to perform almost as well as the S&P 500.   Nevertheless,  Art and Luxury goods companies show better performance numbers than any of the art indices. Interestingly, real estate is not as attractive as Contemporary and Post War art in terms of Sharpe ratios. None of the art index returns load significantly on momentum or liquidity factors, let alone the Fama-French factors. The most remarkable result pertains to the Contemporary art market index. In a sample up to the financial crisis the alpha and beta of the index feature the performance of a respectable hedge fund.

 Keywords: art index, repeated sales, correlation JEL classification: C14, C43, Z11 

Introduction

The recent financial crises caused by the Lehman bankruptcy and the European sovereign debt problems have increased the interest in safe-haven investments. Standard safe haven investments having either no protection against inflation (in the case of bonds) or high volatility (such as gold), investors may have been attracted by alternative assets such as real estate, fine art, or wine. The development of the fine art funds confirms that investors view artworks as just another asset class of investment. This growth was fueled by many factors, including a search by investors for higher yields, entrance into the market of Chinese and emerging markets collectors who are diversifying their acquisitions and investing in art.

The evaluation of these markets on a higher frequency time scale such as monthly is hampered by the heterogeneity of goods and illiquidity caused by periods of few if any transactions. Nonetheless, a reliable high frequency evaluation is important for optimal investment allocation, risk management, and the understanding of correlation and spill- overs from and to other markets. The purpose of this paper is to introduce a new approach to the construction of monthly art indices. So far, the literature addressed mostly the heterogeneity issue. Two estimation methods are commonly used to construct indices:

(1) repeat-sales regression (RSR) and (2) the hedonic regression (HR).

RSR uses prices of individual objects traded at two distinct moments in time. If the characteristics of an object do not change (which is usually the case for collectibles), the heterogeneity issue is bypassed. Goetzmann (1993) constructs a decennial repeated sales index, using 2,809 artworks re-sold at auction from Gerald Reitlinger and Enrique Mayer databases over a period covering 1715 to 1986.  Mei and Moses (2002) construct a repeated-sales data set based on auction art price records at the New York Public Library as well as the Watson Library at the Metropolitan Museum of Art with a total of 4,896 price pairs covering the period 1875-2000.  They construct an annual art index to study the risk-return characteristics of paintings which they find compare favorably to those  of  traditional  financial  assets,  such  as  stocks  and  bonds.   Korteweg,  Kr¨aussl,  and Verwijmeren (2016) consider repeat-sales as endogenous by including a hazard model for the probability of a sale. They construct an annual index using 32,928 transactions over the period 1960 to 2013.

The basic idea of the HR method is to regress prices on various attributes of objects(dimensions, artist, subject matter, etc.) and to use time dummies in the regression to obtain “characteristic-free” prices to compute a price index. See e.g. Ginsburgh, Mei, and Moses (2006) for an extensive description of hedonic regressions and their application to the art market.

The main advantage of RSR, compared to HR, is that the estimation of the returns does not require the inclusion of explanatory variables in the model. The main disad- vantage of the repeated-sales methodology is the low frequency of available resales pairs, whereas the HR typically allows for more frequent observations thanks to the better avail- ability of data.

We provide an innovative methodological contribution to the measurement of returns on infrequently traded assets using a novel approach to repeat-sales regression estimation. Using the new econometric approach, we propose a monthly art market index, as well as sub-indices for Impressionist, Modern, Post-War, and Contemporary paintings based on repeated sales at a monthly frequency. Our starting point is a model proposed by Bocart and Hafner (2015). We address the question by extending a recently proposed dynamic state space model - inspired by Aruoba, Diebold, and Scotti (2009) - for price indices of heterogeneous goods to allow for correlation with other markets, typically with higher liquidity and high frequency trading. Ignoring correlation would lead to flat indices in times of no transactions, as is common in the art markets due to strong biannual cycle of auctions. A precise estimation of correlation enables us to update the art index via observed transactions in other markets that have a link with the art market. In statistical terms, this improves the efficiency of estimated price indices.

In particular, the construction of the monthly index exploits links of art with other assets available at higher frequencies such as liquid Exchange Traded Funds focusing on consumer goods or real estate, baskets of art-related companies (Sotheby’s, artnet, artprice, etc.) or furniture companies, and safe haven assets like gold or U.S. Treasuries. The paper is organized as follows. In section 2 we present the econometric model specification and estimation. Empirical findings for the five price indices: Impressionist art, Modern art, Post-War art and Contemporary art and finally a Global art market index are reported in section 3 In section 4 we study art as an asset class and report standard asset pricing model estimates for the various art market indices. A final section concludes the paper.