No 3 (2025)

In the context of increasing geopolitical and macroeconomic instability, identifying and accounting for factors influencing option volatility is of particular interest to practicing investors. Over the past few years, the volumes of option trading have demonstrated steady growth, surpassing the volumes of futures trading, demonstrating the growing role of options in the structure of financial markets. High turbulence of economic processes leads to low risk management efficiency in options trading. This problem can be solved through a more accurate assessment of volatility price formation factors. The use of modern economic statistical analysis methods, including vector autoregressive (VAR) models, the generalized autoregressive conditional heteroscedasticity (GARCH) model, and machine learning techniques, allowed carrying out a comprehensive analysis aimed at identifying relevant variables and developing approaches to incorporating them in option volatility modeling. The authors focused on expanding and deepening scientific understanding of the nature and mechanisms of option volatility formation in the face of environmental instability. It is proved that geopolitical shocks and economic uncertainty have a significant impact on volatility, and that this impact is asymmetric: emerging markets demonstrate greater sensitivity to these factors than developed markets. The results and conclusions obtained can be used to forecast excess option volatility, which, when taken into account, will allow developing more profitable investment strategies.

Premature deindustrialization (PDI) has emerged as a critical challenge for middle-income countries amidst rapid technological innovations and globalization. This study explores how technological innovations (TI), measured by patent applications, influence PDI across 89 middle-income countries from 1980–2022. A novel composite index, integrating income levels and manufacturing sector contributions (value-added and employment shares), is developed to quantify PDI. Employing robust econometric techniques, including Ordinary Least Squares (OLS) with Oster’s stability test and Instrumental Variable Generalized Method of Moments (IV-GMM), we find that TI significantly reduces PDI’s likelihood. These results, consistent across subsamples and multiple robustness checks, underscore the pivotal role of innovation in fostering sustainable industrial growth. This research offers critical insights for policymakers aiming to balance technological advancement with industrial development in middle-income economies.