Staff research

How do central bank crisis interventions calm market fears? Using options data, we measure the perceived risk of large asset price drops across horizons from two weeks to ten years. Studying the Fed's response to the 2020 turmoil, we find asset purchases reduce short-term fears while interest rate actions shape long-term expectations.

During the COVID-19 financial market crisis, central banks introduced programs to support liquidity in important core funding markets. As well as acting as a backstop to market prices, these programs produce useful trading data on prevailing market conditions. When summary information from this data is shared publicly, it can help market participants understand current conditions and aid the recovery of market functioning.

We characterize the bias in cross-sectional Hill estimates caused by common underlying factors and propose two simple-to-implement remedies. To test for the presence, direction and size of the bias, we use monthly US stock returns and annual US Census county population data.

We look at the informational content of consensus pricing in opaque over-the-counter markets. We show that the availability of price data informs participants mainly about other participants’ valuations, rather than about the value of a financial security.

Financial markets can experience sudden and extreme downward movements. Investors are highly concerned about the performance of their assets in such scenarios. Some assets perform badly in a downturn in the market; others have milder reactions.

The most extreme events, such as economic crises, are rare but often have a great impact. It is difficult to precisely determine the likelihood of such events because the sample is small.

Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach.