Data-based Automatic Discretization of Nonparametric Distributions

Alexis Akira Toda

Papers from arXiv.org

Abstract: Although using non-Gaussian distributions in economic models has become increasingly popular, currently there is no systematic way for calibrating a discrete distribution from the data without imposing parametric assumptions. This paper proposes a simple nonparametric calibration method based on the Golub-Welsch algorithm for Gaussian quadrature. Application to an optimal portfolio problem suggests that assuming Gaussian instead of nonparametric shocks leads to up to 17% overweighting in the stock portfolio because the investor underestimates the probability of crashes.

Date: 2018-05, Revised 2019-05
New Economics Papers: this item is included in nep-ecm
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Journal Article: Data-Based Automatic Discretization of Nonparametric Distributions (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1805.00896

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