Bayesian Risk Assessment Technique for Economic Stress-Strength Models

Article DOI: https://doi.org/10.57017/jaes.v18.4(82).03

Author(s):

Vladimir SAVCHUK International Institute of Business, Kiev, Ukraine and Royal Holloway University of London, Department of Economics, Ukraine

Abstract:

This paper explores two areas of risk assessment modelling in economics and business: the Stress-Strength model and Bayesian techniques. The model assumes that the probability of stress exceeding strength is a measure of risk. The interpretation of stress and strength largely depends on the particular event or phenomenon being modelled. The use of the Stress-Strength model is demonstrated through the Gaussian assumption of probability distributions for random model parameters, particularly in assessing the risk of not achieving a required margin value. The concept of the capability function, representing the difference between strength and stress, is introduced in the modelling process. The probability distribution for the capability function is initially determined based on the Gaussian distribution of the random variables used in the model, allowing for evaluating the risk metric. The Bayesian approach is then applied to generalise the problem statement when dealing with unknown parameters of probability distributions for the Stress and Strength models. The uncertainty of these parameters is modelled through uniform probability distributions, and equations for calculating prior and posterior estimates are consistently obtained. Since multidimensional integrals are involved in these calculations, and solutions cannot be obtained in closed analytical form, Monte Carlo simulation is used to solve this computation problem.

How to cite:

Savchuk, V. (2023). Bayesian Risk Assessment Technique for Economic Stress-Strength Models. Journal of Applied Economic Sciences, Volume XVIII, Winter, 4(82): 285–295. https://doi.org/10.57017/jaes.v18.4(82).03

Article’s history:

Received: 21st of November, 2023; Received in revised form: 17th of December, 2023; Accepted: 20th of December, 2023; Available online: 23rd of December, 2023. Published: 30th of December, 2023 as article in the Volume XVIII, Winter, Issue 4(82).

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