An agent-based stochastic volatility model

The behavioral origins of the stylized facts of financial returns have been addressed in a growing body of agent-based models of financial markets. While the traditional efficient market viewpoint explains all statistical properties of returns by similar features of the news arrival process, the more recent behavioral finance models explain them as imprints of universal patterns of interaction among investors. In this thesis, we contribute to this literature by introducing a very simple agent-based model in which the ubiquitous stylized facts (fat tails, volatility clustering) are emergent properties of the interaction among traders. The simplicity of the model allows us to estimate the underlying parameters, since it is possible to derive a closed form solution for the distribution of returns. The big advantage of our model with respect to the models proposed in the financial econometrics is the ability to explain the origin of the randomness present in the market. It is in fact, very clear how the interactions based on herding among agents play the crucial rule in the emergence of the market fluctuations. We can precisely identify the source of the aggregate regularities of the returns in terms of the agents behavioral assumptions.