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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Nader Karimi & Erfan Salavati & Hirbod Assa & Hojatollah Adibi, 2023. "Sensitivity Analysis of Optimal Commodity Decision Making with Neural Networks: A Case for COVID-19," Mathematics, MDPI, vol. 11(5), pages 1-15, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1202-:d:1084088
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    References listed on IDEAS

    as
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