Anomalous waiting times in high-frequency financial data

Enrico Scalas; Rudolf Gorenflo; Hugh Luckock; Francesco Mainardi; Maurizio Mantelli; Marco Raberto

doi:10.1080/14697680500040413

Anomalous waiting times in high-frequency financial data

Enrico Scalas
, Rudolf Gorenflo
, Hugh Luckock
, Francesco Mainardi
, Maurizio Mantelli
, Marco Raberto

Risultato della ricerca: Contributo su rivista › Articolo in rivista › peer review

Abstract

In high-frequency financial data not only returns, but also waiting times between consecutive trades are random variables. Therefore, it is possible to apply continuous-time random walks (CTRWs) as phenomenological models of the high-frequency price dynamics. An empirical analysis performed on the 30 DJIA stocks shows that the waiting-time survival probability for high-frequency data is non-exponential. This fact imposes constraints on agent-based models of financial markets.

Lingua originale	Inglese
pagine (da-a)	695-702
Numero di pagine	8
Rivista	Quantitative Finance
Volume	4
Numero di pubblicazione	6
DOI	https://doi.org/10.1080/14697680500040413
Stato di pubblicazione	Pubblicato - dic 2004
Pubblicato esternamente	Sì

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10.1080/14697680500040413

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title = "Anomalous waiting times in high-frequency financial data",

abstract = "In high-frequency financial data not only returns, but also waiting times between consecutive trades are random variables. Therefore, it is possible to apply continuous-time random walks (CTRWs) as phenomenological models of the high-frequency price dynamics. An empirical analysis performed on the 30 DJIA stocks shows that the waiting-time survival probability for high-frequency data is non-exponential. This fact imposes constraints on agent-based models of financial markets.",

author = "Enrico Scalas and Rudolf Gorenflo and Hugh Luckock and Francesco Mainardi and Maurizio Mantelli and Marco Raberto",

note = "Funding Information: We would like to acknowledge useful discussions with Sergio Focardi of The Intertek Group. This work was supported by grants from the Italian MIUR Project COFIN 2003 {\textquoteleft}Order and Chaos in nonlinear extended systems: coherent structures, weak stochasticity and anomalous transport{\textquoteright} and by the Italian MIUR FISR Project {\textquoteleft}Ultra-high frequency dynamics of financial markets{\textquoteright}.",

year = "2004",

month = dec,

doi = "10.1080/14697680500040413",

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AU - Scalas, Enrico

AU - Gorenflo, Rudolf

AU - Luckock, Hugh

AU - Mainardi, Francesco

AU - Mantelli, Maurizio

AU - Raberto, Marco

N1 - Funding Information: We would like to acknowledge useful discussions with Sergio Focardi of The Intertek Group. This work was supported by grants from the Italian MIUR Project COFIN 2003 ‘Order and Chaos in nonlinear extended systems: coherent structures, weak stochasticity and anomalous transport’ and by the Italian MIUR FISR Project ‘Ultra-high frequency dynamics of financial markets’.

PY - 2004/12

Y1 - 2004/12

N2 - In high-frequency financial data not only returns, but also waiting times between consecutive trades are random variables. Therefore, it is possible to apply continuous-time random walks (CTRWs) as phenomenological models of the high-frequency price dynamics. An empirical analysis performed on the 30 DJIA stocks shows that the waiting-time survival probability for high-frequency data is non-exponential. This fact imposes constraints on agent-based models of financial markets.

AB - In high-frequency financial data not only returns, but also waiting times between consecutive trades are random variables. Therefore, it is possible to apply continuous-time random walks (CTRWs) as phenomenological models of the high-frequency price dynamics. An empirical analysis performed on the 30 DJIA stocks shows that the waiting-time survival probability for high-frequency data is non-exponential. This fact imposes constraints on agent-based models of financial markets.

UR - https://www.scopus.com/pages/publications/18544373378

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DO - 10.1080/14697680500040413

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JF - Quantitative Finance

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ER -

Anomalous waiting times in high-frequency financial data

Abstract

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