This paper uses an autoregressive approach to test a multi-factor model with time-varying risk premiums. A quasi-differencing approach is used to eliminate the unobservable factors in the model. It is found that the model is capable of capturing the “size effect” and the “dividend yield effect, ” but is incapable of explaining the “book-to-market effect” and the “earnings-price ratio effect.” Thus, it is concluded that a constant-beta multi-factor model will not be able to explain the cross-sectional variation in expected returns.

This paper developes a semiautoregression (SAR) approach to estimate factors of the arbitrage pricing theory (APT) that has the advantage of providing a simple asymptotic variance‐covariance matrix for the factor estimates, which makes it easy to adjust for measurement errors. Using the extracted factors, I confirm the finding that the APT describes asset returns slightly better than the CAPM, although there is still some mispricing in the APT model. I find that not only are the factors “priced” by the market, but the factor premiums move over time in relation to business cycle variables.

In this article we break assets’ betas with common factors into components attributable to news about future cash flows, real interest rates, and excess returns. To achieve this decomposition, we use a vector autoregressive time-series model and an approximate log-linear present value relation. The betas of industry and size portfolios with the market are largely attributed to changing expected returns. Betas with inflation and industrial production reflect opposing cash flow and expected return effects. We also show how asset pricing theory restricts the expected excess return components of betas.

This paper constructs a new data set of repeated sales of artworks and estimates an annual index of art prices for the period 1875-2000. Contrary to earlier studies, we find art outperforms fixed income securities as an investment, though it significantly under-performs stocks in the US. Art is also found to have lower volatility and lower correlation with other assets, making it more attractive for portfolio diversification than discovered in earlier research. There is strong evidence of underperformance of masterpieces, meaning expensive paintings tend to underperform the art market index. The evidence is mixed on whether the law of one price holds in the New York auction market.

This study employs a new data set from art auctions to examine the relationship between auctioneer presale price estimates and the long‐term performance of artworks. We find that the price estimates for expensive paintings have a consistent upward bias over a long period of 30 years. High estimates at the time of purchase are associated with adverse subsequent abnormal returns. Moreover, the estimation error for individual paintings tends to persist over time. These results are consistent with the view that auction house price estimates are affected by agency problems and that some investors are credulous.