We investigate the time behaviour of the Italian MIB30 stock index collected every minute during two months in the period from May 17, 2006, up to July 24, 2006. We find short-range correlations in the price returns and, on the contrary, a long persistent time lag and slow decay in the autocorrelation functions of volatility. Besides, we find that the probability density functions (PDFs) of returns show fat tails, which are well fit by the log-normal model of Castaing [B. Castaing, Y. Gagne, E.J. Hopfinger, Physica D 46 (1990) 177], and a convergence toward a normal distribution for large time scales; we also find that the PDFs of volatility, for short time horizons, fit better with a log-normal distribution than with a Gaussian. Most of these features characterize the indexes and stocks of the largest American, European and Asian markets. We also investigate the distribution of stochastic separation between isolated strong events in the volatility signal. This is interesting because this gives us a deeper understanding about the price formation process. By using a test for the occurrence of local Poisson hypothesis, we show that the process we examined strongly departs from a Poisson statistics, the origin of this failure stemming from the presence of temporal clustering and of a certain amount of memory.

Waiting time distributions of the volatility in the Italian MIB30 index: Clustering or Poisson functions?

GRECO, Antonella;CARBONE, Vincenzo;
2008

Abstract

We investigate the time behaviour of the Italian MIB30 stock index collected every minute during two months in the period from May 17, 2006, up to July 24, 2006. We find short-range correlations in the price returns and, on the contrary, a long persistent time lag and slow decay in the autocorrelation functions of volatility. Besides, we find that the probability density functions (PDFs) of returns show fat tails, which are well fit by the log-normal model of Castaing [B. Castaing, Y. Gagne, E.J. Hopfinger, Physica D 46 (1990) 177], and a convergence toward a normal distribution for large time scales; we also find that the PDFs of volatility, for short time horizons, fit better with a log-normal distribution than with a Gaussian. Most of these features characterize the indexes and stocks of the largest American, European and Asian markets. We also investigate the distribution of stochastic separation between isolated strong events in the volatility signal. This is interesting because this gives us a deeper understanding about the price formation process. By using a test for the occurrence of local Poisson hypothesis, we show that the process we examined strongly departs from a Poisson statistics, the origin of this failure stemming from the presence of temporal clustering and of a certain amount of memory.
Econophysics ; Italian market ; Volatility ; Waiting times
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/141151
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