We merge the literature on downside return risk and liquidity risk and introduce the concept of extreme downside … same time when the market liquidity (return) is lowest. This effect is not driven by linear or downside liquidity risk or … extreme downside return risk and is mainly driven by more recent years. There is no premium for stocks whose liquidity is …

We propose a consistent and computationally efficient 2-step methodology for the estimation of multidimensional non … immune to estimation dimensionality problems. Simulations show good finite sample properties and significant efficiency gains …. This method is especially relevant for risk management purposes such as, for example, the computation of portfolio Value at …

-horizon returns and the negligible impacts of estimation errors on the expected returns. This study uses the innovative simulation … return distribution has the slowest rate of convergence to normality among groups of assets. Estimation errors of the … imprecisions persist over the investment horizons, the estimation errors of the monthly return have a strong effect on the …

risk for the market portfolio is consistent with theory. The granular residual is volatile and less informative about real … activity than our adjusted index, potentially rationalizing lower/zero risk compensation …

investing within the well-known risk-return paradigm. From the viewpoint of ex-ante equity risk premium (ERP), the five factor …-related systematic risk, ii) the exposure to ESG-related systematic risk is significantly priced in the market, and iii) equity funds …

In this paper, we build estimation error in mean returns into the mean-variance (MV) portfolio theory under the … of the MV portfolio along with its mean and risk return when the sample covariance matrix is equal to a constant matrix …. We use the mean squared error (MSE) to characterize the effects of estimation error in mean returns on the joint sampling …