Asset Return Dynamics under Alternative Learning Schemes

In this paper we design an artificial financial market where endogenous volatility is created assigning to the agents diverse prior beliefs about the joint distribution of returns, and, over time, making agents rationally update their beliefs using common public information. We analyze the asset price dynamics generated under two learning environments: one where agents assume that the joint distribution of returns is IID, and another where agents believe in the existence of regimes in the joint distribution of asset returns. We show that the regime switching learning structure can generate all the most common stylized facts of financial markets: fat tails and long-range dependence in volatility coexisting with relatively efficient markets.