We propose a general GARCH framework that allows the predict volatility using returns sampled at a higher frequency than the prediction horizon. We call the class of models High FrequencY Data-Based PRojectIon-Driven GARCH, or HYBRID-GARCH models, as the volatility dynamics are driven by what we...

We combine self-collected historical data from 1867 to 1907 with CRSP data from 1926 to 2012, to examine the risk and return over the past 140 years of one of the most popular mechanical trading strategies - momentum. We find that momentum has earned abnormally high risk-adjusted returns - a...

"We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in...

Multiperiod-ahead forecasts of returns’ variance are used in most areas of applied finance where long-horizon measures of risk are necessary. Yet, the major focus in the variance forecasting literature has been on one-period-ahead forecasts. In this review, we compare several approaches of...

Prior studies attribute analysts' forecast superiority over time-series forecasting models to their access to a large set of firm, industry, and macroeconomic information (an information advantage), which they use to update their forecasts on a daily, weekly or monthly basis (a timing...

This paper provides robustness checks and analytical derivations to supplement the material presented in the paper Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory.The paper to which these Appendices apply is available at the following URL:...