Multivariate Stock Returns Around Extreme Events: A Reassessment of Economic Fundamentals and the 1987 Market Crash

Abstract

This paper reassesses the role of economic fundamentals in the 1987 stock market crash using a two factor common-component model of returns. The model decomposes returns into idiosyncratic components, a common white noise component, and a common source of Poisson jumps. Among three two-year sample periods for Major Market Index stocks, only a 1987-88 sample results in an estimated jump component with low frequency and large size. Using Bayes' rule, we infer ex post jump probabilities for each sample day. In contrast to an analogous univariate model for an index return, the multivariate model captures information in the cross-section of returns. Leading financial news on the most likely jump days from the multivariate model is compared with news on a control group of high index return days. Days with high jump probabilities under the multivariate model contain systematically more news related to the dollar, trade deficits, and financing of the U. S. budget deficit. This suggest that the common jump component proxies for economic fundaments related to this cluster of news events, and that the unexpectedly large U.S. trade deficit news released on the Wednesday prior to the crash provided an economic catalyst for the event.