Discretizing Nonlinear, Non-Gaussian Markov Processes with Exact Conditional Moments

Leland Farmer and Alexis Akira Toda

MPRA Paper from University Library of Munich, Germany

Abstract: Approximating stochastic processes by finite-state Markov chains is useful for reducing computational complexity when solving dynamic economic models. We provide a new method for accurately discretizing general Markov processes by matching low order moments of the conditional distributions using maximum entropy. In contrast to existing methods, our approach is not limited to linear Gaussian autoregressive processes. We apply our method to numerically solve asset pricing models with various underlying stochastic processes for the fundamentals, including a rare disasters model. Our method outperforms the solution accuracy of existing methods by orders of magnitude, while drastically simplifying the solution algorithm. The performance of our method is robust to parameters such as the number of grid points and the persistence of the process.

Keywords: asset pricing models; duality; Kullback-Leibler information; numerical methods; solution accuracy (search for similar items in EconPapers)
JEL-codes: C63 C68 G12 (search for similar items in EconPapers)
Date: 2016-11-01
New Economics Papers: this item is included in nep-cmp and nep-ore
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Related works:
Journal Article: Discretizing nonlinear, non‐Gaussian Markov processes with exact conditional moments (2017)
Working Paper: Discretizing Nonlinear, Non-Gaussian Markov Processes with Exact Conditional Moments (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:78981

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