Sensitivity Analysis of Optimal Commodity Decision Making with Neural Networks: A Case for COVID-19

Nader Karimi, Erfan Salavati (), Hirbod Assa and Hojatollah Adibi
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Nader Karimi: Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran 1591634311, Iran
Erfan Salavati: Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran 1591634311, Iran
Hirbod Assa: Kent Business School, Canterbury CT2 7FS, UK
Hojatollah Adibi: Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran 1591634311, Iran

Mathematics, 2023, vol. 11, issue 5, 1-15

Abstract: The COVID-19 pandemic caused a significant disruption to food demand, leading to changes in household expenditure and consumption patterns. This paper presents a method for analyzing the impact of such demand shocks on a producer’s decision to sell a commodity during economic turmoil. The method uses an artificial neural network (ANN) to approximate the optimal value function for a general stochastic differential equation and calculate the partial derivatives of the value function with respect to various parameters of both the diffusion process and the payoff function. This approach allows for sensitivity analysis of the optimal stopping problem and can be applied to a range of situations beyond just the COVID-19 crisis.

Keywords: optimal stopping; deep learning; sensitivity analysis; value function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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