Аннотация
In this paper, the statistical properties of
high-frequency data are investigated by means of
computational experiments performed with the Genoa
Artificial Stock Market (Raberto et al. 2001, 2003,
2004). In the market model, heterogeneous agents trade
one 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 is modelled with a renewal process where the
distribution of waiting times between two consecutive
orders is a Weibull distribution. This hypothesis is
based on recent empirical investigation made on
high-frequency financial data (Mainardi et al. 2000,
Raberto et al. 2002, Scalas et al. 2003). We
investigate how the statistical properties of prices
and of waiting times between transactions are affected
by the particular renewal process chosen for orders.
Results point out that the mechanism of the limit order
book is able to recover fat tails in the distribution
of price returns without ad-hoc behavioral assumptions
regarding agents; moreover, the kurtosis of the return
distribution depends also on the renewal process chosen
for orders. As regarding the renewal process underlying
trades, in the case of exponentially distributed order
waiting times, also trade waiting times are
exponentially distributed. Conversely, if order waiting
times follow a Weibull, the same does not hold for
trade waiting times.
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