Identification of Social Interaction Effects in Financial Data

In this paper, we present a stochastic agent-based model and extend an artificial financial market by considering a stochastic process of market fundamentals. The model predicts that groups of noise traders are busy communicating when market uncertainty is high. In particular, we examine the effects of social interactions on price movements, based on parameter estimation of the group behavior. As traders’ reactions to new information act much like an endogenous shock on return volatility, however, we cannot easily find an exact solution for the model with social interactions. Thus, simulation-based inference is used for the model validation; we investigate whether our artificial economy can match the empirical moments observed in five major foreign exchange data sets (as closely as possible). The results indicate that the return volatility under scrutiny can be robustly decomposed into news (45–55 %) and social interaction effects (45–55 %). Copyright Springer Science+Business Media New York 2015