  This paper develops a two-step estimation methodology, which allows us to
apply catastrophe theory to stock market returns with time-varying volatility
and model stock market crashes. Utilizing high frequency data, we estimate the
daily realized volatility from the returns in the first step and use stochastic
cusp catastrophe on data normalized by the estimated volatility in the second
step to study possible discontinuities in markets. We support our methodology
by simulations where we also discuss the importance of stochastic noise and
volatility in deterministic cusp catastrophe model. The methodology is
empirically tested on almost 27 years of U.S. stock market evolution covering
several important recessions and crisis periods. Due to the very long sample
period we also develop a rolling estimation approach and we find that while in
the first half of the period stock markets showed marks of bifurcations, in the
second half catastrophe theory was not able to confirm this behavior. Results
suggest that the proposed methodology provides an important shift in
application of catastrophe theory to stock markets.
