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A Probabilistic Solution to High-Dimensional Continuous-Time Macro-Finance Models

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Author
Ji Huang
Category
Quantitative
Date Posted
2022/06/06
Date Retrieved
2022/09/24
Date Revised
Date Written
2022/05/29
Description
This paper demonstrates that the dynamics of a continuous-time macro-finance model can be characterized by the probabilistic solution of a coupled forward-backward stochastic differential equation system. It could overcome the ``curse of dimensionality by solving the probabilistic solution with deep reinforcement learning. The paper proposes a simple algorithm and assesses its performance by considering a multiple-country model which allows for an analytic solution under symmetric states. Malliavin derivatives are employed to characterize the propagation of diffusion shocks whose computation also uses the probabilistic approach.
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JEL Classifications
C63 E44 F40
Keywords
probabilistic solution macro-finance FBSDE deep learning Malliavin derivative
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Pages
24
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URL
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4228577
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