The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization which can take advantage of the mixed-frequency time series panel data structures. 

Yes. The quantity of risk underlying cross-sectional return spreads is time-varying, yielding swings in factors' risk premia. We define ``macro-relevant'' factors as those whose risk premium variation induce consumption fluctuations, and more broadly, a business-cycle.

This paper defines risk-on risk-off (RORO), an elusive terminology in pervasive use, as the variation in global investor risk aversion. Our high-frequency RORO index captures time-varying investor risk appetite across multiple dimensions: advanced economy credit risk, equity market volatility, funding conditions, and currency dynamics. The index exhibits risk-off skewness and pronounced fat tails, suggesting its amplifying potential for extreme, destabilizing events. Compared with the conventional VIX measure, the RORO index reflects the multifaceted nature of risk, underscoring the diverse provenance of investor risk sentiment. Practical applications of the RORO index highlight its significance for international portfolio reallocation and return predictability.

Using firm-level administrative tax data, we document dramatic reductions in private leverage since the Global Financial Crisis, while leverage among public firms rose during this period. Our findings suggest that banks' credit supply plays a prominent role in explaining the leverage pattern of private firms.

Using machine learning techniques, we uncover an important number of dealers in the U.S. municipal bond market who focus on geographically adjacent states, a characteristic distinct from dealer centrality. These “specialized” dealers enjoy larger market shares in states with greater local ownership and in local bonds with more complex features. We also find that trades intermediated by these specialized dealers have significantly larger markups than those intermediated by national dealers.

Quantum computers are not yet up to the task of providing computational advantages for practical stochastic diffusion models commonly used by financial analysts. In this paper we introduce a class of stochastic processes that are both realistic in terms of mimicking financial market risks as well as more amenable to potential quantum computational advantages.

We examine a perplexing phenomenon wherein technological innovations induce short-term contractions, using a two-sector New-Keynesian model. Pivotal to explaining the evidence are sticky prices, which alter the cyclicality of relative prices, impacting production during innovative phases.