TY - JOUR
T1 - Statistical analysis and agent-based microstructure modeling of high-frequency financial trading
AU - Ponta, Linda
AU - Scalas, Enrico
AU - Raberto, Marco
AU - Cincotti, Silvano
N1 - Funding Information:
Manuscript received June 30, 2011; revised October 21, 2011; accepted October 27, 2011. Date of publication October 31, 2011; date of current version July 13, 2012. This work was supported in part by the University of Genoa and in part by the Italian Ministry of Education, University, and Research (MIUR). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ali Akansu.
PY - 2012
Y1 - 2012
N2 - A simulation of high-frequency market data is performed with the Genoa Artificial Stock Market. 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. A renewal order-generation process is used having a waiting-time distribution between consecutive orders that follows a Weibull law, in line with previous studies. The simulation results show that this mechanism can reproduce fat-tailed distributions of returns without ad-hoc behavioral assumptions on agents. In the simulated trade process, when the order waiting-times are exponentially distributed, trade waiting times are exponentially distributed. However, if order waiting times follow a Weibull law, analogous results do not hold. These findings are interpreted in terms of a random thinning of the order renewal process. This behavior is compared with order and trade durations taken from real financial data.
AB - A simulation of high-frequency market data is performed with the Genoa Artificial Stock Market. 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. A renewal order-generation process is used having a waiting-time distribution between consecutive orders that follows a Weibull law, in line with previous studies. The simulation results show that this mechanism can reproduce fat-tailed distributions of returns without ad-hoc behavioral assumptions on agents. In the simulated trade process, when the order waiting-times are exponentially distributed, trade waiting times are exponentially distributed. However, if order waiting times follow a Weibull law, analogous results do not hold. These findings are interpreted in terms of a random thinning of the order renewal process. This behavior is compared with order and trade durations taken from real financial data.
KW - Artificial stock market
KW - Weibull distribution
KW - high-frequency financial time-series
KW - random thinning
UR - http://www.scopus.com/inward/record.url?scp=84864127180&partnerID=8YFLogxK
U2 - 10.1109/JSTSP.2011.2174192
DO - 10.1109/JSTSP.2011.2174192
M3 - Article
SN - 1932-4553
VL - 6
SP - 381
EP - 387
JO - IEEE Journal on Selected Topics in Signal Processing
JF - IEEE Journal on Selected Topics in Signal Processing
IS - 4
M1 - 6064868
ER -