Thursday 6th May 2021, 4pm

Speaker: Deniz Erdemlioglu – IESEG School of Management

Title: News-Driven Systemic Tail Risk at High Frequency

This is a Virtual Event – instructions on how to join the event will follow

Abstract

We develop a new testing framework to measure systemic tail risk embedded in a panel of high-frequency stock returns. We estimate time-varying jump intensities and introduce test statistics that are conditional on the release times of news events. Our approach allows us to pinpoint (i) when precisely individual stocks or portfolio indices jump at the same time (systemic cojumps), or headed in a downward direction, (ii) when they crash together systemically at high frequency (systemic crashes). Controlling for the multiple testing bias, we establish the bootstrap consistency of the tests and show in simulations that the tests have reasonable power. Based on the high-frequency data on Dow Jones constituents and sector-specific ETFs, our empirical analysis provides strong evidence of systemic downside risk driven by the Federal Open Market Committee (FOMC) news. Utilizing the test statistics, we further construct a simple proxy for news-driven systemic tail risk. This stress indicator helps explain the pre-FOMC announcement drift and reveals return predictability ahead of the upcoming Fed meeting. Unlike the monetary policy announcements, we find no evidence that macro news creates systemic cojumps or crashes at high frequency. We discuss the practical implications of our results for portfolio diversification and (news-driven) realized tail risk monitoring.

*This is a joint work with Christopher J. Neely (The Federal Reserve Bank of St. Louis) and Xiye Yang (Rutgers University). The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.

Please sign up here:  https://www.eventbrite.co.uk/e/oxford-man-institute-quantitative-finance-seminar-tickets-142875046109