The assessment of macroeconomic conditions in real time is challenging. Dynamic factor models, which summarize the comovement across many macroeconomic time series as driven by a small number of shocks, have become the workhorse tool for ‘nowcasting’ activity. This paper develops a novel dynamic factor model that explicitly captures three salient features of modern business cycles: low frequency movements in long-run growth and volatility, lead-lag patterns in the responses of variables to common shocks, and fat-tailed outliers. We use real-time unrevised data for the last two decades and cloud computing technology to conduct an out-of-sample evaluation exercise of the model. The exercise demonstrates the importance of considering these features for forecasting and probability assessment of economic conditions. In an application to the COVID-19 recession, we develop a method to incorporate newly available high-frequency data. The use of such alternative data is essential to track the downturn in activity, but a careful econometric specification is just as important.