Long-horizon predictability is not a myth. We propose a new analytical standard error for predictive regressions that does not impose the null hypothesis that returns are unpredictable and exhibits substantial power gains relative to popular tests. Deriving the covariance matrix under the...

Winner stocks have higher risk exposure to Fama and French's (1993) three factors (FF3F) than loser stocks during good economic times, and therefore should earn higher expected returns. Employing the conditional FF3F model to risk adjust returns on winner and loser stocks can reduce the average...

We analyze the puzzling behavior of the volatility of individual stock returns around the turn of the Millennium. There has been much academic interest in this topic, but no convincing explanation has arisen. Our goal is to pull together the many competing explanations currently proposed in the...

We develop a new stochastic volatility model that captures the three most important features of stock index returns: negative correlation between returns and future volatility, excess kurtosis and negative skewness. We estimate the model parameters by maximum likelihood using a numerical...

We evaluate the performance of several specification tests for Markov regime-switching time series models. We consider the Lagrange Multiplier and dynamic specification tests of Hamilton (1994) and Ljung-Box tests based on both the generalized residual and a standard-normal residual constructed...