DIRECT CAUSAL CASCADE IN THE STOCK-MARKET

We use wavelets to decompose the volatility (standard deviation) of in traday (S&P500) return data across scales. We show that when investiga ting two-point correlation functions of the volatility logarithms acro ss different time scales, one reveals the existence of a causal inform ation cascade from large scales (i.e. small frequencies) to fine scale s. We quantify and visualize the information Aux across scales. We pro vide a possible interpretation of our findings in terms of market dyna mics.