Toward Sustainable Development: Assessing the Effects of Financial Contagion on Human Well-Being in Romania

Abstract

In a globally interconnected economy marked by volatility, this study employs the Autoregressive Distributed Lag (ARDL) model to examine financial contagion’s impact on Romania’s financial stability. It investigates both conventional and unconventional channels through which financial contagion is transmitted, emphasizing its sensitivity to factors such as geopolitical events and investor sentiment. The study also assesses the influence of unemployment, market capitalization, and financial freedom on Romania’s Human Development Index (HDI) from 2000 to 2022. Using HDI, which encompasses health and education alongside economic aspects, the research provides a holistic view of well-being and quality of life. In addition to the ARDL model’s insights, this study expands its scope by conducting a multilinear regression analysis, with GDP as the dependent variable. We have incorporated independent variables such as HDI, transaction volume, and the BET-FI index to comprehensively assess their relationships and potential impact on Romania’s economic growth. This analytical approach unveils intricate connections between key economic and financial indicators, paving the way for a deeper understanding of how these variables interact. Furthermore, to shed light on the financial dynamics within Romania, a supplementary analysis in the Altreva Adaptive Modeler was undertaken, focusing on the BET-FI index. This software-based exploration provides a nuanced perspective on the index’s behavior and its interactions with other economic and social indicators. This additional dimension contributes to our holistic understanding of the effects of financial contagion and the implications for sustainable human development in Romania. By combining traditional econometric methodologies with cutting-edge modeling techniques, this study strives to offer a robust framework for comprehending the multifaceted nature of financial contagion and its implications for both the national economy and well-being. These findings have the potential to guide policymakers and financial institutions in implementing more effective risk management strategies, driving economic development, and ultimately enhancing the overall quality of life in Romania.

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

“…The ARDL model is particularly useful when examining the links between variables that may evolve over time and affect each other in the long run. Although not developed specifically for detecting financial bubbles, it is often used in broader economic and financial analyses to examine how variables evolve in the long term and interact with each other (Nica et al 2023a(Nica et al , 2023b. Detecting financial bubbles typically involves performing specific analyses, applying methods for testing the existence of bubbles, and assessing their impact on financial markets.…”

“…The ARDL process involves the following steps (Nica et al 2023a;Androniceanu et The ARDL model was improved by incorporating the bounds test, which examines whether a combination of I(0) and I(1) variables exhibits a long-term relationship. The structure of the ARDL bounds test is delineated by Equation (8):…”

“…Through advanced econometric techniques, we aimed to uncover hidden patterns, assess the impact of various factors, and provide valuable predictive insights into the performance of stock indices. According to Nica et al (2023a), the ARDL model stands out as a flexible instrument for scrutinizing time series data and unraveling relationships among research variables. Its proficiency in addressing both short-term and long-term dynamics, as well as handling cointegration and ensuring robustness, renders it indispensable for empirical research.…”

Trading Activity in the Corporate Bond Market: A SAD Tale of Macro-Announcements and Behavioral Seasonality?

“…In macroeconomic time series analysis, the ARDL approach of Pesaran and Shin (1998) and Pesaran et al (2001) has become particularly popular with respect to modeling cointegrated time series when the series include a mix of I(0) and I(1) variables. This method was elegantly discussed and employed in Nica et al (2023) within the macro-finance context of financial contagion.…”

Quantifying Impact, Uncovering Trends: A Comprehensive Bibliometric Analysis of Shadow Banking and Financial Contagion Dynamics

“…This concept describes the rapid spread of financial problems from one area or sector of the market to other financial areas or sectors, creating a domino effect (Nica et al 2023).…”