0000000000225080

AUTHOR

Michele Treccani

showing 3 related works from this author

Modelling systemic price cojumps with Hawkes factor models

2015

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.

Multivariate statisticsEconomicsSystemic shockPoisson distribution01 natural sciencesSynchronizationEconometrics and Finance (all)2001 EconomicsFOS: Economics and business010104 statistics & probabilitysymbols.namesakeHigh frequency data0502 economics and businessEconomicsEconometricsCojumps0101 mathematicsCojumps; Hawkes processes; High frequency data; Systemic shocks; Finance; Economics Econometrics and Finance (all)2001 Economics Econometrics and Finance (miscellaneous)Time clusteringFactor analysisSettore SECS-S/06 - Metodi mat. dell'economia e Scienze Attuariali e FinanziarieStatistical Finance (q-fin.ST)050208 financeSystemic shocksHawkes processe05 social sciencesQuantitative Finance - Statistical FinanceEconomics Econometrics and Finance (all)2001 Economics Econometrics and Finance (miscellaneous)Econometrics and Finance (miscellaneous)symbolsCojumpHawkes processesGeneral Economics Econometrics and FinanceFinanceSign (mathematics)Quantitative Finance
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Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics

2015

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and dai…

0301 basic medicineINFORMATIONEconomicsComputer scienceBig datalcsh:MedicineSocial SciencesQuantitative Finance - Computational Financesocial and economic systemsMathematical and Statistical TechniquesSociologybig dataEconometrics050207 economicsComputer NetworksCapital Marketslcsh:ScienceFinancial Marketsmedia_common050208 financeMultidisciplinary05 social sciencesCommerceSocial CommunicationSettore FIS/02 - Fisica Teorica Modelli e Metodi MatematiciSurpriseModels EconomicSocial NetworksPhysical SciencesSocial SystemsEngineering and TechnologyComputational sociologyBEHAVIORStatistics (Mathematics)Network AnalysisResearch ArticleComputer and Information SciencesExploitmedia_common.quotation_subjectTwitterComputational Finance (q-fin.CP)Research and Analysis MethodsFOS: Economics and business03 medical and health sciencesSEARCH0502 economics and businessHumansRelevance (information retrieval)Web navigationInvestmentsStatistical MethodsInternetStatistical Finance (q-fin.ST)STOCK-MARKETbusiness.industrylcsh:RSentiment analysisFinancial marketATTENTIONQuantitative Finance - Statistical FinanceCommunicationsNoise ReductionFinancial Firms030104 developmental biologySignal ProcessingPredictive powerlcsh:QStock marketbusinessSocial MediaFinanceMathematicsForecastingPLOS ONE
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Modelling Systemic Cojumps with Hawkes Factor Models

2013

Instabilities in the price dynamics of a large number of financial assets are a clear sign of systemic events. By investigating a set of 20 high cap stocks traded at the Italian Stock Exchange, we find that there is a large number of high frequency cojumps. We show that the dynamics of these jumps is described neither by a multivariate Poisson nor by a multivariate Hawkes model. We introduce a Hawkes one factor model which is able to capture simultaneously the time clustering of jumps and the high synchronization of jumps across assets.

symbols.namesakeMultivariate statisticsStock exchangeEconometricssymbolsEconomicsPoisson distributionSynchronizationTime clusteringFactor analysisSign (mathematics)SSRN Electronic Journal
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