How does the microstructure of an over-the-counter market respond in a time of stress? We test several hypotheses of network-based models by analysing the 2015 crash of the Swiss franc-euro FX derivatives market. To do so we employ unique data at transaction and counterparty identity level, and a new analytical framework that uses the trading network topology to segment the market into a multi-layered structure. We document limited intermediation by inner-core nodes, in particular dealers with loss making outstanding positions. Clients in greater need of trading were less likely to trade, pointing to a supply driven liquidity shortage. However, more central and better connected clients were able to access the market sooner and at better prices than more peripheral clients, lending support to theory predictions that network centrality matters for sourcing liquidity and execution quality.

We propose a new approach to examine sell-side analysts’ career concerns by relating their forecast boldness to their employers’ news flows. Specifically, we use banking sector news to proxy for the severity of career concerns. Analysts follow more closely the consensus forecast when the prospects of the banking sector are negative (and vice versa). The effect is both economically and statistically significant after controlling for various firm, analyst, brokerage house, and forecasting characteristics, as well as sector and economy wide effects. The more established analysts, in terms of reputation and experience, are generally unaffected by banking sector news. In contrast, their less established peers tend to cluster their forecasts near the consensus after a sequence of negative news flows for banks. Collectively, our results support the notion that during banking stresses when job security is low analysts’ tendency to imitate others increases.

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.