A double-auction artificial market with time-irregularly spaced orders

Enrico Scalas and Silvano Cincotti ()

No 225, Computing in Economics and Finance 2004 from Society for Computational Economics

Abstract: In this paper, a simulation of high-frequency market data is performed with the Genoa Artificial Stock Market. In the market model, heterogeneous agents trade a risky asset in exchange for cash. Agents have zero intelligence and issue random limit or market orders depending on their budget constraints. The price is cleared by means of a limit order book. The order generation process is a renewal process where the waiting-time distribution between two consecutive orders follows a Weibull law. This hypothesis is motivated by recent theoretical and empirical studies on high-frequency financial data. According to simulation results, the mechanism of the limit order book can reproduce fat-tailed distributions of returns without ad-hoc behavioral assumptions on agents. As for the simulated trade process, in the case of exponentially distributed order waiting times, also trade waiting times are exponentially distributed. Conversely, if order waiting times follow a Weibull law, the same does not hold true for trade waiting times. These findings are interpreted in terms of a random thinning of the order renewal process. References: Raberto et al, Computational Economics, vol 22 (2003), pp. 255-272. Scalas et al, Physical Review E, vol. 69 (2004), pp. 011107(1-8). Raberto et al., Physica A, vol. 314 (2002), pp. 749-755.

Keywords: Agent-based simulation; artificial financial market; limit order book (search for similar items in EconPapers)
JEL-codes: C15 C16 D44 (search for similar items in EconPapers)
Date: 2004-08-11
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sce:scecf4:225

Access Statistics for this paper

More papers in Computing in Economics and Finance 2004 from Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F. Baum ().