"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...

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...

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 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...

This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle...

We introduce a new measure called Inflation-at-Risk (I@R) associated with (left and right) tail inflation risk. We estimate I@R using survey-based density forecasts. We show that it contains information not covered by usual inflation risk indicators which focus on inflation uncertainty and do...