Stock markets can be characterized by fat tails in the volatility distribution, clustering of volatilities and slow … description of the data. As a new type of data we describe the volatility cluster by the waiting time distribution, which can be …

-based clustering procedure which starts from (i) asset return and (ii) volatility time series. The MST is obtained at different times … volatility time series. Our analysis also shows that the degree of stocks has a very slow dynamics with a time scale of several …

. Volatility also scales, but with a cross-over point of 1 day, with long-run correlations being particularly important. …

volatility. The existence of a Cramér function, the characteristic function for self-similarity, is confirmed by analyzing real …

working months). Volatility also scales, with long-run correlations being particularly important. …

stochastic volatility. The description of the unconditional distribution for the absolute returns is in good agreement with the … volatility clusters are described by a scaling law for the distribution of returns conditional to the value at the previous day …

respect to the base a. We unravel that the volatility multipliers possess multifractal nature which is independent of …

We investigate the historical volatility of the 100 most capitalized stocks traded in US equity markets. An empirical … probability density function (pdf) of volatility is obtained and compared with the theoretical predictions of a lognormal model … and of the Hull and White model. The lognormal model well describes the pdf in the region of low values of volatility …

fluctuations has correlations that decay as a power-law, persisting for several months. (iii) Volatility and trading activity: We …