Simulation of asset pricing in information networks

We simulate the asset pricing in the framework of information networks when the number of agents is constant and tends to infinity. When the number of agents is a constant, we find that a higher risk aversion coefficient, a lower information uncertainty, or a higher standard variance of payoff volatility induces a lower asset price; a higher number of agents induces a higher aggregate demand. When the number of agents tends to infinity, we study and simulate the closed form expressions for asset price with risk aversion coefficient. We find that a higher network connectedness or a lower risk aversion coefficient induces a higher information driven volatility component and a lower Sharpe ratio; a higher network connectedness or a lower risk aversion coefficient induces a higher market efficiency. Liquidity driven volatility component, trading profit, price volatility are non-monotonic functions of network connectedness, or risk aversion coefficient.