This paper shows that stock market contagion occurs as a domino effect, where confined local crashes evolve into more widespread crashes. Using a novel framework based on ordered logit regressions we model the occurrence of local, regional and global crashes as a function of their past occurrences and financial variables. We find significant evidence that global crashes do not occur abruptly but are preceded by local and regional crashes. Besides this form of contagion, interdependence shows up by the effect of interest rates, bond returns and stock market volatility on crash probabilities. When it comes to forecasting global crashes, our model outperforms a binomial model for global crashes only.

We test for a change in the volatility of 214 U.S. macroeconomic time series over the period 1959–1999. We find that approximately 80% of these series have experienced a break in unconditional volatility during this period. Even though more than half of the series experienced a break in conditional mean, most of the reduction in volatility appears to be due to changes in conditional volatility. Our results are robust to controlling for business cycle nonlinearity in both mean and variance. Volatility changes are more appropriately characterized as instantaneous breaks than as gradual changes. Nominal variables such as inflation and interest rates experienced multiple volatility breaks and witnessed temporary increases in volatility during the 1970s. On this evidence, we conclude that the increased stability of economic fluctuations is widespread.

We study the effects of FOMC announcements of federal funds target rate decisions on individual stock returns, volatilities and correlations at the intraday level. For all three characteristics we find that the stock market responds differently to positive and negative target rate surprises. First, the average response to positive surprises (that is, bad news for stocks) is larger. Second, in case of bad news the mere occurrence of a surprise matters most, whereas for good news its magnitude is more important. These new insights are possible due to the use of high-frequency intraday data.