Economic Archive

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  REGIONAL DISPARITIES IN BANK HOUSING LENDING IN BULGARIA

  The subject of research in this article is bank housing lending. Its object is to outline the challenges arising from the existence of regional disparities in housing lending in Bulgaria, the negative consequences stemming from them, and the possibilities for overcoming these problems. The thesis defended in the study is that decisions regarding ... The subject of research in this article is bank housing lending. Its object is to outline the challenges arising from the existence of regional disparities in housing lending in Bulgaria, the negative consequences stemming from them, and the possibilities for overcoming these problems. The thesis defended in the study is that decisions regarding bank housing lending, the terms of the loans and their accessibility are directly dependent on regional economic development, the real estate market, and the demographic situation in the various regions of the country.

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  STRUCTURAL CHALLENGES AND DEVELOPMENT TRENDS IN GEORGIA’S MEDIA MARKET: KEY PLAYERS, MARKET SCOPE, AND BUSINESS MODELS

  This article examines Georgia’s media market between 2020 and 2025, focusing on (i) key market players, (ii) market scope and revenue dynamics, and (iii) business models and stakeholder dependencies. Using desk research of regulatory statistics, annual advertising market reviews, financial transparency monitoring, and information-environment ... This article examines Georgia’s media market between 2020 and 2025, focusing on (i) key market players, (ii) market scope and revenue dynamics, and (iii) business models and stakeholder dependencies. Using desk research of regulatory statistics, annual advertising market reviews, financial transparency monitoring, and information-environment assessments, the study maps how revenues are concentrated among a limited set of national broadcasters while pluralism is maintained through politically affiliated outlets that rely on non-market financing. The findings show that commercial advertising remains structurally decisive for major broadcasters, yet political spending (especially in election cycles), regulatory enforcement capacity, and donor-supported programs shape incentives and editorial alignment. The period features a pandemic-era shock (2020), a policy-driven advertising contraction connected to restrictions on gambling advertising (2022), revenue recovery with pronoun¬ced gains among pro-government broadcasters (2023), renewed contraction in mid-2024, and a 2025 consolidation signal marked by the closure of a major opposition-leaning channel. The article concludes that Georgia’s media market displays persistent political parallelism and mixed funding logics, producing a high-stakes ecosystem where commercial advertisers, state-linked spending, ownership subsidies, and external donor priorities interact with regulatory constraints to shape competitiveness and journalistic autonomy.

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  EVALUATING MODELS FOR OIL PRICE FORECASTING

  The present study is aimed at forecasting oil prices through the application of several quantitative models—moving averages, trend projection, Monte Carlo simulations, and autoregressive models. The analysis covers the period from 12 May 2024 to 12 May 2025, using daily-frequency historical data on Brent crude oil prices. For the moving average ... The present study is aimed at forecasting oil prices through the application of several quantitative models—moving averages, trend projection, Monte Carlo simulations, and autoregressive models. The analysis covers the period from 12 May 2024 to 12 May 2025, using daily-frequency historical data on Brent crude oil prices. For the moving average model, the classical accuracy indicators—Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE)—were calculated and compared. In the case of trend projection, the evaluation was carried out using standard error, relative standard error, and Absolute Percentage Error (APE). For the Monte Carlo simulations and autoregressive models, the emphasis was placed on the process parameters, forecasted values, and confidence intervals, without using the full set of classical error indicators. The obtained results show that moving averages with shorter periods provide lower error, whereas longer periods lead to smoothing but also to a loss of accuracy. Trend projection and Monte Carlo simulations complement the analysis with a broader scenario-based perspective, while autoregressive models demonstrate good predictive performance in short-term forecasting. This highlights the importance of a combined approach in modelling oil price dynamics and provides a basis for more reliable forecasts under conditions of market uncertainty.