A new structural stochastic volatility model of asset pricing and its stylized facts

TL;DR

This paper introduces a novel structural stochastic volatility model for asset pricing that captures key stylized facts of financial data by modeling agent interactions, herding, and strategy switching.

Contribution

It develops a new agent-based stochastic volatility model incorporating herding, price misalignment, and strategy switching, estimated via simulated moments and bootstrap methods.

Findings

Replicates heavy tails and volatility clustering.

Captures long memory in absolute returns.

Reproduces stylized facts like autocorrelation absence and price impact.

Abstract

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.