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Coupled continuous time random walks in finance

  • Mark M. Meerschaert
  • Enrico Scalas

Continuous time random walks (CTRWs) are used in physics to model anomalous diffusion, by incorporating a random waiting time between particle jumps. In finance, the particle jumps are log-returns and the waiting times measure delay between transactions. These two random variables (log-return and waiting time) are typically not independent. For these coupled CTRW models, we can now compute the limiting stochastic process (just like Brownian motion is the limit of a simple random walk), even in the case of heavy tailed (power-law) price jumps and/or waiting times. The probability density functions for this limit process solve fractional partial differential equations. In some cases, these equations can be explicitly solved to yield descriptions of long-term price changes, based on a high-resolution model of individual trades that includes the statistical dependence between waiting times and the subsequent log-returns. In the heavy tailed case, this involves operator stable space-time random vectors that generalize the familiar stable models. In this paper, we will review the fundamental theory and present two applications with tick-by-tick stock and futures data.

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File URL: http://arxiv.org/pdf/physics/0608281
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Paper provided by arXiv.org in its series Papers with number physics/0608281.

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Date of creation: Aug 2006
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Publication status: Published in Physica A, vol. 370, 114-118, 2006
Handle: RePEc:arx:papers:physics/0608281
Contact details of provider: Web page: http://arxiv.org/

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  1. Mainardi, Francesco & Raberto, Marco & Gorenflo, Rudolf & Scalas, Enrico, 2000. "Fractional calculus and continuous-time finance II: the waiting-time distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 468-481.
  2. Scalas, Enrico & Gorenflo, Rudolf & Mainardi, Francesco, 2000. "Fractional calculus and continuous-time finance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 284(1), pages 376-384.
  3. M. Raberto & E. Scalas & F. Mainardi, 2002. "Waiting-times and returns in high-frequency financial data: an empirical study," Papers cond-mat/0203596, arXiv.org.
  4. Scheffler, Hans-Peter, 1999. "On estimation of the spectral measure of certain nonnormal operator stable laws," Statistics & Probability Letters, Elsevier, vol. 43(4), pages 385-392, July.
  5. Bertram, William K, 2004. "An empirical investigation of Australian Stock Exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 341(C), pages 533-546.
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