4 research outputs found
Minding impacting events in a model of stochastic variance
We introduce a generalisation of the well-known ARCH process, widely used for
generating uncorrelated stochastic time series with long-term non-Gaussian
distributions and long-lasting correlations in the (instantaneous) standard
deviation exhibiting a clustering profile. Specifically, inspired by the fact
that in a variety of systems impacting events are hardly forgot, we split the
process into two different regimes: a first one for regular periods where the
average volatility of the fluctuations within a certain period of time is below
a certain threshold and another one when the local standard deviation
outnumbers it. In the former situation we use standard rules for
heteroscedastic processes whereas in the latter case the system starts
recalling past values that surpassed the threshold. Our results show that for
appropriate parameter values the model is able to provide fat tailed
probability density functions and strong persistence of the instantaneous
variance characterised by large values of the Hurst exponent is greater than
0.8, which are ubiquitous features in complex systems.Comment: 18 pages, 5 figures, 1 table. To published in PLoS on
Intraday Seasonalities and Nonstationarity of Trading Volume in Financial Markets: Individual and Cross-Sectional Features
Intraday seasonalities and nonstationarity of trading volume in financial markets: Collective features
Microscopic spin model for the stock market with attractor bubbling on scale-free networks
Econophysics, Multi-agent models of financial markets, Complex networks, 89.65.Gh, 89.75.Hc, 05.45.-a,