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

• Published in: arXiv.org 2016-04
• Main Authors: Pruna, Radu T, Polukarov, Maria, Jennings, Nicholas R
• Format: Article
• Language: English
• Subjects: Clustering; Computer simulation; Economic models; Misalignment; Parameter estimation; Pricing; Statistical methods; Stochastic models; Stock market indexes; Volatility
• ISSN: 2331-8422

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.