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Mean field equilibrium asset pricing model under partial observation: An exponential quadratic Gaussian approach

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  • Masashi Sekine

Abstract

This paper studies an asset pricing model in a partially observable market with a large number of heterogeneous agents using the mean field game theory. In this model, we assume that investors can only observe stock prices and must infer the risk premium from these observations when determining trading strategies. We characterize the equilibrium risk premium in such a market through a solution to the mean field backward stochastic differential equation (BSDE). Specifically, the solution to the mean field BSDE can be expressed semi-analytically by employing an exponential quadratic Gaussian framework. We then construct the risk premium process, which cannot be observed directly by investors, endogenously using the Kalman-Bucy filtering theory. In addition, we include a simple numerical simulation to visualize the dynamics of our market model.

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  • Masashi Sekine, 2024. "Mean field equilibrium asset pricing model under partial observation: An exponential quadratic Gaussian approach," Papers 2410.01352, arXiv.org.
    • Handle: RePEc:arx:papers:2410.01352
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    References listed on IDEAS

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    1. Jianhui Huang & Shujun Wang, 2016. "Dynamic Optimization of Large-Population Systems with Partial Information," Journal of Optimization Theory and Applications, Springer, vol. 168(1), pages 231-245, January.
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