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dc.contributor.authorPonta, L.
dc.contributor.authorTrinh, M.
dc.contributor.authorRaberto, M.
dc.contributor.authorScalas, E. 
dc.contributor.authorCincotti, S.
dc.description.abstractWe study tick-by-tick financial returns for the FTSE MIB index of the Italian Stock Exchange (Borsa Italiana). We confirm previously detected non-stationarities. Scaling properties reported before for other high-frequency financial data are only approximately valid. As a consequence of our empirical analyses, we propose a simple model for non-stationary returns, based on a non-homogeneous normal compound Poisson process. It turns out that our model can approximately reproduce several stylized facts of high-frequency financial time series. Moreover, using Monte Carlo simulations, we analyze order selection for this class of models using three information criteria: Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and the Hannan–Quinn information criterion (HQ). For comparison, we perform a similar Monte Carlo experiment for the ACD (autoregressive conditional duration) model. Our results show that the information criteria work best for small parameter numbers for the compound Poisson type models, whereas for the ACD model the model selection procedure does not work well in certain cases.en_US
dc.description.sponsorshipMIUR, Italy PRIN 2009 2009H8WPX_5002 SDF fund (University of Sussex, UK)en_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.subjectStochastic processesen_US
dc.subjectInformation criteriaen_US
dc.subjectHigh-frequency financeen_US
dc.titleModeling non-stationarities in high-frequency financial time seriesen_US
dc.journal.titlePhysica A: Statistical Mechanics and its Applicationsen_US

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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España