Continuous time persistent random walk: a review and some generalizations

2017 ◽  
Vol 90 (6) ◽  
Author(s):  
Jaume Masoliver ◽  
Katja Lindenberg
2018 ◽  
Vol 28 (7) ◽  
pp. 075507 ◽  
Author(s):  
Daniel Escaff ◽  
Raúl Toral ◽  
Christian Van den Broeck ◽  
Katja Lindenberg

1989 ◽  
Vol 157 (2) ◽  
pp. 891-898 ◽  
Author(s):  
Jaume Masoliver ◽  
Katja Lindenberg ◽  
George H. Weiss

2021 ◽  
Vol 501 (2) ◽  
pp. 125180
Author(s):  
Arka Ghosh ◽  
Steven Noren ◽  
Alexander Roitershtein

2021 ◽  
Vol 34 (4) ◽  
Author(s):  
M. Muge Karaman ◽  
Jiaxuan Zhang ◽  
Karen L. Xie ◽  
Wenzhen Zhu ◽  
Xiaohong Joe Zhou

2014 ◽  
Vol 46 (02) ◽  
pp. 400-421 ◽  
Author(s):  
Daniela Bertacchi ◽  
Fabio Zucca

In this paper we study the strong local survival property for discrete-time and continuous-time branching random walks. We study this property by means of an infinite-dimensional generating functionGand a maximum principle which, we prove, is satisfied by every fixed point ofG. We give results for the existence of a strong local survival regime and we prove that, unlike local and global survival, in continuous time, strong local survival is not a monotone property in the general case (though it is monotone if the branching random walk is quasitransitive). We provide an example of an irreducible branching random walk where the strong local property depends on the starting site of the process. By means of other counterexamples, we show that the existence of a pure global phase is not equivalent to nonamenability of the process, and that even an irreducible branching random walk with the same branching law at each site may exhibit nonstrong local survival. Finally, we show that the generating function of an irreducible branching random walk can have more than two fixed points; this disproves a previously known result.


2017 ◽  
Author(s):  
Kang Kang ◽  
Elsayed Abdelfatah ◽  
Maysam Pournik ◽  
Bor Jier Shiau ◽  
Jeffrey Harwell

2012 ◽  
Vol 26 (23) ◽  
pp. 1250151 ◽  
Author(s):  
KWOK SAU FA

In this paper, we model the tick-by-tick dynamics of markets by using the continuous-time random walk (CTRW) model. We employ a sum of products of power law and stretched exponential functions for the waiting time probability distribution function; this function can fit well the waiting time distribution for BUND futures traded at LIFFE in 1997.


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