We use realized volatilities based on after hours high frequency returns to predict next day volatility. We extend GARCH and long-memory forecasting models to include additional information: the whole night, the preopen, the postclose realized variance, and the overnight squared return. For four...

We show that characteristics known to predict returns to U.S. stocks also predict returns for a broad sample of nearly 52,000 stocks from fifty-eight non-U.S. countries, and we evaluate the extent to which six prominent corporate events, including initial and secondary stock offerings, stock...

On several occasions technical analysis rules have been shown to have predictive power. The main purpose of this work is to decompose the predictive power of the moving average trading rule and isolate the portion that could be attributed to the possible exploitation of linear and non linear...

We examine the performance of volatility models that incorporate features such as long (short) memory, regime-switching and multifractality along with two competing distributional assumptions of the error component, i.e. Normal vs Student-t. Our precise contribution is twofold. First, we...

This paper calibrates a class of jump-diffusion long-run risks (LRR) models to quantify how well they can jointly explain the equity risk premium and the variance risk premium in the U.S. financial markets, and whether they can generate realistic dynamics of riskneutral and realized...

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the weak form Efficient Market Hypothesis). We do...

This paper proposes a novel approach to extracting option-implied equity premia, and empirically examines the information content of these risk premia for forecasting the stock market return. Our approach does not require specifying the functional form of the pricing kernel, and does not impose...

This paper provides a comprehensive analysis on the stock return predictability in Turkey, January 1997 to July 2011, by employing both portfolio method and cross-sectional regressions. In the risk-related predictors, we found predictive power of beta, total volatility, and idiosyncratic...

This paper examines the information content of two different measures of aggregate equity-market order flow for future macro fundamentals and expected stock market returns. The first measure, the cross-sectional average of individual stock order flows, predicts future growth rates for industrial...

Using a novel and direct measure of investor sentiment, I find that Facebook's Gross National Happiness (GNH) has the ability to predict changes in both daily returns and trading volume in the US stock market. For instance, an increase of one standard deviation in GNH is associated with an...