We present empirical evidence that stocks with low volatility earn high risk-adjusted returns. The annual alpha spread of global low versus high volatility decile portfolios amounts to 12% over the 1986-2006 period. We also observe this volatility effect within the US, European and Japanese...

The mean-semivariance CAPM strongly outperforms the traditional mean-variance CAPM in terms of its ability to explain the cross-section of US stock returns. If regular beta is replaced by downside beta, the traditional risk-return relationship is restored. The downside betas of low-beta stocks...

The relationship between prices of paintings at public auctions and their attributes has received much attention in recent years. However, the effects of color have been mostly absent from these studies. The present study explored the relationship between price and color in Rothko’s post 1950...

The quest for parsimonious models has been a key objective in asset pricing. However, there appears to be no consensus on the most successful asset pricing strategy in the literature, especially for the South African Market. Using financial statements from January 2000 to December 2015, this...

Subordination is an often used stochastic process in modeling asset prices. Subordinated Levy price processes and local volatility price processes are now the main tools in modern dynamic asset pricing theory. In this paper, we introduce the theory of multiple internally embedded financial...

We propose a novel class of models in which the crash hazard rate is determined by a function of a non-local estimation of mispricing. Rooted in behavioral finance, the non-local estimation embodies in particular the characteristic of "anchoring" on past price levels and the "probability...

We generalize the seminal Gibbons-Ross-Shanken test to the empirically relevant case where the number of test assets far exceeds the number of observations. In such a setting, one needs to use a regularized estimator of the covariance matrix of test assets, which leads to biases in the original...