Continuous-time random-walk model for financial distributions

Masoliver, Jaume; Montero, Miquel; Weiss, George H.

doi:10.1103/physreve.67.021112

Cited by 175 publications

(139 citation statements)

References 23 publications

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“…Several researchers have recently investigated the statistical properties of waiting times of high-frequency financial data [17][18][19][20][21][22][23][24], and Scalas et al [17][18][19][20][21] in particular have applied the theory of continuous time random walk (CTRW) to financial data. They also found that the waiting-time survival probability for high-frequency data of the 30 DJIA stocks is non-exponential [21].…”

Section: Introductionmentioning

confidence: 99%

Power law for the calm-time interval of price changes

Kaizoji

Kaizoji²

2004

Physica A: Statistical Mechanics and its Applications

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In this paper, we describe a newly discovered statistical property of time series data for daily price changes. We conducted quantitative investigation of the calm-time intervals of price changes for 800 companies listed in the Tokyo Stock Exchange, and for the Nikkei 225 index over a 27-year period from January 4, 1975 to December 28, 2001. A calm-time interval is defined as the interval between two successive price changes above a fixed threshold. We found that the calm-time interval distribution of price changes obeys a power law decay. Furthermore, we show that the power-law exponent decreases monotonically with respect to the threshold.

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Section: Introductionmentioning

confidence: 99%

Power law for the calm-time interval of price changes

Kaizoji

Kaizoji²

2004

Physica A: Statistical Mechanics and its Applications

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“…email and short message sending, online clicking of web pages, making calls, financial commerce, etc.) follow power-law distribution (Barabási 2005;Dezso et al 2006;Han et al 2008;Masoliver et al 2003). The results indicate that for human activities, instead of pure random processes, there might be more underlying principles.…”

Section: Timing Characteristics Of Research Interestsmentioning

confidence: 77%

Research interests: their dynamics, structures and applications in unifying search and reasoning

et al. 2010

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“…However, there is a large number of natural and industrial processes that involve non-exponential waiting times. [27][28][29][30] For example, coupling of mechanical degrees of freedom with chemical processes in motor proteins might lead to non-exponential waiting time distributions. 28 More generally, when a given state has an internal structure, for instance, a smaller sub-network of states, we should expect non-exponential dwell time distributions and violations of the Markov property.…”

Section: Introductionmentioning

confidence: 99%

Pathway structure determination in complex stochastic networks with non-exponential dwell times

Kolomeisky

Valleriani

2014

The Journal of Chemical Physics

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Analysis of complex networks has been widely used as a powerful tool for investigating various physical, chemical, and biological processes. To understand the emergent properties of these complex systems, one of the most basic issues is to determine the structure and topology of the underlying networks. Recently, a new theoretical approach based on first-passage analysis has been developed for investigating the relationship between structure and dynamic properties for network systems with exponential dwell time distributions. However, many real phenomena involve transitions with nonexponential waiting times. We extend the first-passage method to uncover the structure of distinct pathways in complex networks with non-exponential dwell time distributions. It is found that the analysis of early time dynamics provides explicit information on the length of the pathways associated to their dynamic properties. It reveals a universal relationship that we have condensed in one general equation, which relates the number of intermediate states on the shortest path to the early time behavior of the first-passage distributions. Our theoretical predictions are confirmed by extensive Monte Carlo simulations.

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Continuous-time random-walk model for financial distributions

Cited by 175 publications

References 23 publications

Power law for the calm-time interval of price changes

Power law for the calm-time interval of price changes

Research interests: their dynamics, structures and applications in unifying search and reasoning

Pathway structure determination in complex stochastic networks with non-exponential dwell times

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