英文标题：
《Data-based Automatic Discretization of Nonparametric Distributions》
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作者：
Alexis Akira Toda
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最新提交年份：
2019
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英文摘要：
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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中文摘要：
虽然在经济模型中使用非高斯分布已变得越来越流行，但目前没有一种系统的方法可以在不施加参数假设的情况下从数据中校准离散分布。基于高斯求积的Golub-Welsch算法，提出了一种简单的非参数标定方法。对最优投资组合问题的应用表明，假设高斯冲击而非非非参数冲击会导致股票投资组合中高达17%的权重过高，因为投资者低估了崩溃的概率。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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