  Building on a prominent agent-based model, we present a new structural
stochastic volatility asset pricing model of fundamentalists vs. chartists
where the prices are determined based on excess demand. Specifically, this
allows for modelling stochastic interactions between agents, based on a herding
process corrected by a price misalignment, and incorporating strong noise
components in the agents' demand. The model's parameters are estimated using
the method of simulated moments, where the moments reflect the basic properties
of the daily returns of a stock market index. In addition, for the first time
we apply a (parametric) bootstrap method in a setting where the switching
between strategies is modelled using a discrete choice approach. As we
demonstrate, the resulting dynamics replicate a rich set of the stylized facts
of the daily financial data including: heavy tails, volatility clustering, long
memory in absolute returns, as well as the absence of autocorrelation in raw
returns, volatility-volume correlations, aggregate Gaussianity, concave price
impact and extreme price events.
