Financial Crises

Evaluating the resilience of ESG investments in European Markets during turmoil periods ArXiv ID: 2501.03269 “View on arXiv” Authors: Unknown Abstract This study investigates the resilience of Environmental, Social, and Governance (ESG) investments during periods of financial instability, comparing them with traditional equity indices across major European markets-Germany, France, and Italy. Using daily returns from October 2021 to February 2024, the analysis explores the effects of key global disruptions such as the Covid-19 pandemic and the Russia-Ukraine conflict on market performance. A mixture of two generalised normal distributions (MGND) and EGARCH-in-mean models are used to identify periods of market turmoil and assess volatility dynamics. The findings indicate that during crises, ESG investments present higher volatility in Germany and Italy than in France. Despite some regional variations, ESG portfolios demonstrate greater resilience compared to traditional ones, offering potential risk mitigation during market shocks. These results underscore the importance of integrating ESG factors into long-term investment strategies, particularly in the face of unpredictable financial turmoil. ...

Multiple-bubble testing in the cryptocurrency market: a case study of bitcoin ArXiv ID: 2401.05417 “View on arXiv” Authors: Unknown Abstract Economic periods and financial crises have highlighted the importance of evaluating financial markets to investors and researchers in recent decades. Keywords: financial markets, economic periods, financial crises, market evaluation, General Financial Markets Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The paper applies advanced statistical methods like the Right-Tail Augmented Dickey–Fuller (RTADF) test, indicating significant mathematical modeling, but the excerpt shows no implementation details, backtesting results, or data processing steps, resulting in low empirical readiness. flowchart TD A["Research Question<br>Identify & test for multiple bubbles<br>in the cryptocurrency market"] --> B["Data Input<br>Historical Bitcoin Price Data<br>across different time periods"] B --> C["Methodology<br>Advanced Bubble Testing<br>e.g., GSADF or SADF"] C --> D["Computational Process<br>Calculate Test Statistics<br>Identify Bubble Regimes"] D --> E["Key Findings<br>Detect multiple bubble periods<br>Assess crash risks<br>Market implications"]

Non-parametric cumulants approach for outlier detection of multivariate financial data ArXiv ID: 2305.10911 “View on arXiv” Authors: Unknown Abstract In this paper, we propose an outlier detection algorithm for multivariate data based on their projections on the directions that maximize the Cumulant Generating Function (CGF). We prove that CGF is a convex function, and we characterize the CGF maximization problem on the unit n-circle as a concave minimization problem. Then, we show that the CGF maximization approach can be interpreted as an extension of the standard principal component technique. Therefore, for validation and testing, we provide a thorough comparison of our methodology with two other projection-based approaches both on artificial and real-world financial data. Finally, we apply our method as an early detector for financial crises. ...