type="main" <title type="main">ABSTRACT</title> <p>We introduce a novel approach to estimating latent oil risk factors and establish their significance in pricing nonoil securities. Our model, which features four factors with simple economic interpretations, is estimated using both derivative prices and oil-related equity...</p>

Purpose - The purpose of this paper is to study performance and market timing ability of equity real estate investment trusts (REITs). Design/methodology/approach - The authors use classical regression-based framework and their multi-index, multifactor, and conditional extensions to jointly...

DeMiguel, Garlappi, and Uppal (2009) report that naïve diversification dominates mean-variance optimization in out-of-sample asset allocation tests. Our analysis suggests that this is largely due to their research design, which focuses on portfolios that are subject to high estimation risk and...

A number of studies investigate whether various stochastic variables explain changes in return volatility by specifying the variables as covariates in a GARCH(1, 1) or EGARCH(1, 1) model. The authors show that these models impose an implicit constraint that can obscure the true role of the...

We use Markov chain methods to develop a flexible class of discrete stochastic autoregressive volatility (DSARV) models. Our approach to formulating the models is straightforward, and readily accommodates features such as volatility asymmetry and time-varying volatility persistence. Moreover, it...

We use state-space methods to investigate the relation between volume, volatility, and ARCH effects within a mixture of distributions hypothesis (MDH) framework. Most recent studies of the MDH fit AR(1) specifications that require the information flow to be highly persistent. Using a more...

We find that trading- versus nontrading-period variance ratios in weather-sensitive markets are lower than those in the equity market and higher than those in the currency market. The variance ratios are also substantially lower during periods of the year when prices are most sensitive to the...

This article shows how rational asset pricing models restrict the regression-based criteria commonly used to measure return predictability. Specifically, it invokes no-arbitrage arguments to show that the intercept, slope coefficients, and R[superscript 2] in predictive regressions must take...

Recent studies show that when a regression model is used to forecast stock and bond returns, the sample R [superscript 2] increases dramatically with the length of the return horizon. These studies argue, therefore, that long-horizon returns are highly predictable. This article presents evidence...