This paper provides a nonparametric test for deciding the dimensionality of a policy shock as manifest in the abnormal change in asset returns’ stochastic covariance matrix, following the release of a macroeconomic announcement. We use high-frequency data in local windows before and after the event to estimate the covariance jump matrix and then test its rank. We find a one-factor structure in the covariance jump matrix of the yield curve resulting from the Federal Reserve’s monetary policy shocks before the 2007–2009 financial crisis. The dimensionality of policy shocks increased afterwards because of the use of unconventional monetary policy tools.

Employing an international sample of bank loans from 37 countries, we find that both borrowers' intensive media coverage and positive media sentiment reduce the interest rate spreads on bank loans. In syndicated lending, positive media sentiment increases the likelihood of a non-relationship bank leading or participating in a loan syndicate and decreases the loan share of the lead arranger. Furthermore, we demonstrate that the negative impact of media news on loan spreads is more pronounced in countries with better financial information and governance environments, a higher representation of privately owned media, and lower government control of banks. These findings underscore the significance of media coverage and sentiment in shaping the costs of bank loans worldwide.

We propose a decomposition of the realized covariance matrix into components based on the signs of the underlying high‐frequency returns, and we derive the asymptotic properties of the resulting realized semicovariance measures as the sampling interval goes to zero. The first‐order asymptotic results highlight how the same‐sign and mixed‐sign components load differently on economic information related to stochastic correlation and jumps. The second‐order asymptotic results reveal the structure underlying the same‐sign semicovariances, as manifested in the form of co‐drifting and dynamic “leverage” effects. In line with this anatomy, we use data on a large cross‐section of individual stocks to empirically document distinct dynamic dependencies in the different realized semicovariance components. We show that the accuracy of portfolio return variance forecasts may be significantly improved by exploiting the information in realized semicovariances.

This paper explores implications of weak identification in common ‘long memory’ and recent ‘rough’ approaches to modeling volatility dynamics of financial assets. We unveil an asymptotic near-observational equivalence between a long memory model with weak autoregressive dynamics and a rough model with a near-unit autoregressive root. Standard methods struggle to distinguish them, and conventional asymptotics are invalid. We propose an identification-robust approach to construct confidence sets that reveal the uncertainty and aid inference. Empirical studies based on realized volatility and trading volume often fail to statistically reject either model, thereby providing evidence of their potential coexistence.