This research paper aims to investigate the interdependent influences of barriers to implementing reverse logistics in the broad spectrum of processing activities in the conditions of an emerging economy. An effort was made to approach these barriers (i.e., organizational and management barriers, technical and technological barriers, and economic, financial, and market barriers) based on the relevant literature, predominant attitudes, and experts’ opinions, thus contributing to the body of knowledge in this domain. Determining the intensity of interdependent influences and the importance of barriers for implementing reverse logistics was performed to determine the most important (key) barriers that can be practically applied as guidelines for decision making. The Fuzzy DEMATEL method was used to determine the intensity of these influences on a sample of manufacturing companies in the Republic of Serbia. The results indicate that the most critical barriers to the successful implementation of reverse logistics are a lack of management support and cooperation with scientific institutions and professional associations to acquire knowledge and follow trends in the field.

This paper explores the vulnerability in agriculture in Serbia. Agricultural sector is extremely important for both country and the farmers. The country’s exposure to hazards has a direct effect on the economy, since around 20% of the population are farmers. This sector is exposed to all kinds of hazards and climate changes. When a hazard occurs, it is essential to act quickly and determine recovery and risk reduction requirements. This paper shows monetary losses and recorded damaged/destroyed goods in agricultural sector. Data of previously recorded hazards is obtained from United Nations DesInventar Open Source Initiative database. The analysis is based on 1493 agricultural events recorded from 1981. to 2023. Data is analysed using Python programming language. It is processed with Python’s DOM parser xml.dom.minidom. inside a Django web application. Visualisation of analysed data is implemented using Chart.js library.

The paper developed a model for market risk assessment based on deep learning in combination with non-parametric, parametric and semi-parametric VaR and ES models. We presented the ANN-GRU model more precisely. It is intended for insurance companies operating in emerging markets because the model was developed to cover all the characteristics of emerging markets. The research was conducted on the example of 8 optimal investment portfolios for insurance companies operating in the Balkan countries. The portfolios were calibrated at the daily level and calculated for the period from 1 January 2020 to 31 December 2023. The first 500 data were used to estimate the calibration of the VaR model, the other 250 to estimate the validity of the VaR model, and the last 250 to test the validity of the VaR/ES-GRU-DL market risk estimates in accordance with Directive II. Conditional and unconditional coverage tests were used to test the validity of VaR estimates, while Berkowitz's ES test was used to test the validity of ES estimates. Due to the limitations of these tests, the validity of the backtesting VaR estimate was performed using Dufour Monte Carlo simulations, while the validity of the backtesting ES estimate used the Bootstrap procedure. The backtesting results, as well as the results of the validity of the backtesting results, show that the model generates reliable estimates of VaR and ES in accordance with the Solvency II directive as well as produces better estimates compared to the popular and widely used VaR and ES models.