This study introduces the frontier market valuation model (FMVM), a novel and exploratory framework designed to more accurately estimate the cost of equity in structurally constrained markets. Traditional models such as CAPM and Damodaran’s country risk premium (CRP) fail to capture the layered risks prevalent in frontier economies. FMVM addresses this gap by integrating four explicit premiums: sovereign risk, liquidity constraints, behavioral volatility, and institutional quality deficits. The aim is to develop a context-sensitive model that better reflects equity risk in frontier and transitioning markets. Using empirical data from five countries—Croatia, Serbia, Bosnia and Herzegovina, Turkey, and Hungary—the model is calibrated and applied in an attempt to assess its robustness and explanatory power. Croatia serves as the primary case study, with data spanning a 15-year period, offering a detailed benchmark for model validation. The findings show that FMVM consistently yields higher and more differentiated discount rates than traditional models, particularly in markets with severe illiquidity, governance deficits, or sentiment-driven volatility. FMVM provides theoretical advancement and practical utility by quantifying unpriced structural risks and guiding reforms aimed at reducing capital costs.
Building on the established link between financial inclusion and sustainable economic growth, this study examines the role of connectivity, a key enabler of fintech, in advancing financial inclusion across four dimensions: account ownership, digital payments, savings, and credit. A financial connectivity index is developed using principal component analysis (PCA) to consolidate multiple access channels into a single, comprehensive indicator. Incorporating control variables identified in the literature, panel data models reveal that connectivity promotes financial inclusion at all its stages. However, as individuals deepen their engagement with financial services, factors such as education, cultural traits, and regulatory quality become increasingly influential. Stable, high-quality employment also emerges as a critical determinant, particularly for long-term financial commitments. Despite limitations in data quality, the consistency and explanatory power of the models across all dimensions support the robustness of the study’s hypotheses. Overall, the findings underscore that, while financial connectivity is essential, advancing global financial inclusion requires cultural sensitivity and a robust regulatory framework.
Accurate forecasting of financial time series plays a critical role in understanding market dynamics and informing investment decisions. However, the inherent volatility, nonlinearity, and noise of financial data make accurate prediction highly challenging. This study compares five deep learning architectures, including recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and two hybrid models, RNN-LSTM and GRU-LSTM, to evaluate their forecasting performance on the S&P 500 Index between January 2022 and January 2025. Daily open, high, low, and volume values are used as input features, while the closing price serves as the target variable. The hyperparameters of all models are optimized using the random search procedure, and their predictive accuracy is assessed based on RMSE, MAE, and MAPE. The Diebold–Mariano test is further applied to examine whether differences in predictive accuracy among models are statistically significant. The empirical results indicate that hybrid architectures, particularly the GRU-LSTM model, outperform the standalone models by effectively capturing both short-term fluctuations and long-term dependencies in financial time series. The GRU-LSTM model achieves an approximately ten percent lower prediction error compared to the LSTM model, demonstrating the effectiveness of hybridization in improving deep learning forecasting performance. These findings confirm the robustness of hybrid deep learning architectures for financial time series forecasting and provide valuable insights for researchers and practitioners in quantitative finance.
This paper examines whether gold can act as a safe haven and/or a hedge against the eurozone stock market by identifying periods of negative or zero integrated covariance (ICOV), estimated from high-frequency prices for each trading day. There is no consensus on a unique ICOV estimator due to the sensitivity of realized counterparts to market imperfections. Hence, this research employs an appropriate synchronization scheme, an optimal sampling frequency, and a top-ranked robust price-jump estimator. Unlike existing studies that rely on daily prices or simulated intraday prices, this paper contributes by determining which realized covariance provides an unbiased and consistent ICOV estimate between the EUROSTOXX50 index and gold using one-minute transaction data. The robust version of the realized two-times scaled covariance (RRTSCOV) is appointed as the benchmark against which other estimators are compared and ranked by mean squared error, quasi-likelihood loss functions, and the Diebold-Mariano test. The realized outlyingness-weighted covariance (ROWCOV) at a sampling frequency of 22 minutes confirms that gold served as a safe haven during the first wave of the COVID-19 pandemic and the onset of the war in Ukraine. Thus, in the short run, gold exhibits strong safe-haven properties, but in the long run, the realized covariance indicates a weak hedge for leading eurozone stocks.
This study uses bibliometric analysis to examine research on financial sector efficiency in Southeast Europe (SEE) from 1998 to 2024. Using PRISMA methodology, we identified and analyzed articles that investigate the efficiency of the financial sector in SEE countries. Based on the reviewed literature, we explore key quantitative methods and approaches used in efficiency analysis and uncover opportunities and gaps for future research. The analysis reveals methodological choices, regional challenges, and differences in data availability among countries. Alongside practical suggestions, such as supporting data-sharing initiatives and encouraging methodological diversification, the study highlights the importance of recent data, interdisciplinary collaboration, and advanced mathematical techniques. These findings enhance our understanding of financial efficiency and offer researchers and policymakers recommendations for improving the stability, resilience, and integration of Southeast Europe’s financial systems.
