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Data-based Automatic Discretization of Nonparametric Distributions

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  • Alexis Akira Toda

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.

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  • Alexis Akira Toda, 2018. "Data-based Automatic Discretization of Nonparametric Distributions," Papers 1805.00896, arXiv.org, revised May 2019.
    • Handle: RePEc:arx:papers:1805.00896
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