Jitka Meluchová

The purpose of the study is to conduct a comparative analysis of the determinants affecting the financial performance of agricultural enterprises in Poland, Slovakia and Ukraine. As the main research method, panel data regression analysis was used to analyze data from 34 Polish, 123 Slovak, and 305 Ukrainian agricultural companies for the period 2017–2020. To analyze the links between financial performance measures and its determinants, nine models were developed based on three selected dependent variables (Return on Assets, Return on Equity, Return on Sales) in each of the countries studied. Seven independent variables were used, such as Leverage, Long-Term Debt to Assets, Short-Term Debt to Assets, Debt to Equity, Current Ratio, Asset Tangibility, Capital Intensity, and two control variables such as Size and Dummy variable for legal form. The most significant impact on the financial performance of agricultural enterprises has: for Polish enterprises – Return on Assets – Leverage and Asset Tangibility, Return on Equity – Debt to Equity and Dummy variable for legal form, Return on Sales – Current Ratio, Capital Intensity, and Size; for Slovak enterprises – Return on Assets – Current Ratio, Return on Equity – Debt to Equity, Return on Sales – Current Ratio, and Capital Intensity; for Ukrainian enterprises – Return on Assets – Leverage and Size, Return on Equity – Debt to Equity, and Current Ratio, Return on Sales – Capital Intensity.

Type of the article: Research Article

Abstract
The increasing complexity of insurance risks within the Solvency II regulatory framework highlights the need for accurate quantitative tools to assess the capital adequacy of insurance companies and model extreme insurance events. This study aims to demonstrate how the R programming language can be effectively used to perform Monte Carlo simulations of aggregate losses and subsequently estimate the capital requirement for large claims within partial internal solvency models used by insurance companies. The research methodology is based on Monte Carlo simulation implemented in the R programming environment using the replicate function to generate thousands of stochastic scenarios of claim frequency and individual claim severity based on selected probability distributions. Using real data from non-life insurance, the model generates hundreds of thousands of simulated scenarios of aggregate losses and constructs an empirical distribution of total losses from which risk measures are estimated. The simulation results show that the generated distribution captures not only the typical development of claims but also rare extreme events, which allows the estimation of the capital required to cover large claims at high confidence levels. These results enable insurance companies to more accurately quantify underwriting risk, analyze potential catastrophic loss scenarios, and determine the level of capital required to maintain solvency. The results confirm that Monte Carlo simulations implemented in the R programming language represent an effective tool for modeling aggregate losses and support risk management and capital optimization within internal solvency models.

Acknowledgment
This paper was prepared within the framework of the VEGA research projects No. 1/0497/25 Implementation of innovative approaches in managing and modelling of risk in internal models of insurance companies and No. 1/0377/25 Innovative methods of enterprise risk management and their application in risk modelling and management.