Extracting and forecasting the volatility of financial markets is an important empirical problem. Time series of realized volatility or other volatility proxies, such as squared returns, display long range dependence. Exponential smoothing (ES) is a very popular and successful forecasting and...

We estimate MIDAS regressions with various (bi)power variations to predict future volatility measured via increments in quadratic variation. Instead of pre-determining the (bi)power variation we parameterize it and estimate the intra-daily return power transformation that optimally predicts...

This paper systematically investigates the sources of differential out-of-sample predictive accuracy of heuristic frameworks based on internet search frequencies and a large set of econometric models. The volume of internet searches helps gauge the degree of investors' time-varying interest in...

This paper draws upon several distinct contributions to improve the out-of- sample forecasting performance of realized volatility models. More specifically, we retain the rolling-sample idea of Andreou and Ghysels (2002) to propose a new approach we call the Rolling Realized Volatility (RRV ),...

We adjust the dividend-price ratio for share repurchases and investigate whether predictive power can be improved when constructing forecasts of UK and French equity premia. Regulations in the two largest European stock markets allow us to employ actual repurchase data in our predictive...

We use machine learning methods to predict stock return volatility. Our out-of-sample prediction of realised volatility for a large cross-section of US stocks over the sample period from 1992 to 2016 is on average 44.1% against the actual realised volatility of 43.8% with an R2 being as high as...

This paper proposes a two-state predictive regression model and shows that stock market 12-month return (TMR), the time-series momentum predictor of Moskowitz, Ooi, and Pedersen (2012), forecasts the aggregate stock market negatively in good times and positively in bad times. The out-of-sample...

This study investigates the impact of investor sentiment on excess equity return forecasting. A high (low) investor sentiment may weaken the connection between fundamental economic (behavioral-based non-fundamental) predictors and market returns. We find that although fundamental variables can...

Motivated by the present-value framework, this article proposes a novel and flexible semiparametric time-varying model to examine the so-called `pockets of predictability,' i.e., stock returns or cash flows are significantly predictable in a given local period. We apply a semiparametric profile...