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Simulation of asset pricing in information networks

Wentao Wang, Junhuan Zhang, Shangmei Zhao and Yanglin Zhang

Physica A: Statistical Mechanics and its Applications, 2019, vol. 513, issue C, 620-634

Abstract: 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.

Keywords: Asset pricing; Information networks; Risk aversion; Agent-based simulation (search for similar items in EconPapers)
Date: 2019
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Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

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