This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments.

中文翻译：

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金融传染的定量建模：揭示市场动态和泡沫检测机制

本研究探讨了主要金融指数之间复杂的相互作用和金融传染的潜在风险，重点关注布加勒斯特交易所交易投资基金指数 (BET-FI) 以及标准普尔 500 指数、纳斯达克综合指数 (IXIC) 和道琼斯指数等全球指数工业平均指数 (DJIA)。我们的分析涵盖了从 2012 年到 2023 年的广泛时期，特别关注罗马尼亚的金融市场。我们采用自回归分布滞后 (ARDL) 模型来检查这些指数之间的相互关系，将 BET-FI 指数视为我们的主要变量。我们的研究还整合了指数曲线拟合（EXCF）和广义至上增强迪基-富勒（GSADF）模型来识别和审查这些指数中潜在的价格泡沫。我们分析了受政府政策、总统选举、科技行业表现、COVID-19 大流行和地缘政治紧张局势（特别是俄罗斯-乌克兰冲突）等多种因素影响的高波动时刻和偏离典型市场趋势的时刻。 ARDL 模型揭示了变量之间稳定的长期关系，表明它们之间的相互关联性。我们的研究还强调了短期市场变化导致长期均衡的重要性，如误差修正模型（ECM）所示。这表明存在传染效应，即短期的小事件可能会对金融市场引发长期的、多米诺骨牌般的影响。此外，我们的方差分解检查了不同因素随时间变化的贡献，揭示了它们不断变化的相互作用和影响。乔列斯基因子证明了指数之间的相互依赖性，这对于理解金融传染效应至关重要。因此，我们的研究揭示了金融传染的微妙动态，提供了对市场变化、我们模型的有效性以及检测金融泡沫的策略的见解。这项研究为学术领域贡献了宝贵的知识，并为动荡的金融环境中的投资者提供了实用的见解。