Sensitivity Analysis of Optimal Commodity Decision Making with Neural Networks: A Case for COVID-19
Open Access
- 28 February 2023
- journal article
- research article
- Published by MDPI AG in Mathematics
- Vol. 11 (5) , 1202
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
This publication has 26 references indexed in Scilit:
- Commodity Price Movements in a General Equilibrium Model of StorageIMF Economic Review, 2013
- The empirical relevance of the competitive storage modelJournal of Econometrics, 2011
- The Stochastic Behavior of Commodity Prices: Implications for Valuation and HedgingThe Journal of Finance, 1997
- Competitive Storage and Commodity Price DynamicsJournal of Political Economy, 1996
- A Theory of Commodity Price FluctuationsJournal of Political Economy, 1996
- Estimating a nonlinear rational expectations commodity price model with unobservable state variablesJournal of Applied Econometrics, 1995
- The numerical solution of stochastic differential equationsThe Journal of the Australian Mathematical Society. Series B. Applied Mathematics, 1977
- Stochastic Speculative PriceProceedings of the National Academy of Sciences, 1971
- Rational Expectations and the Theory of Price MovementsEconometrica, 1961
- Implications of Recent Research on Optimal Storage RulesJournal of Farm Economics, 1958