A0641
Title: A copula-based network model for evaluating net bubble risk across asset classes
Authors: Giovanni De Luca - University of Naples Parthenope (Italy) [presenting]
Andrea Montanino - University of Naples Parthenope (Italy)
Abstract: Financial bubbles that periodically form and collapse, manifesting as cycles of sharp growth followed by sudden crashes, pose critical risks to global market stability. As such, timely identification and monitoring of these dynamics are essential for policymakers and financial analysts. The contribution to the literature is the focus on the concurrent emergence of speculative bubbles across different asset sectors, particularly cryptocurrencies and Big Tech equities. Using the backward supremum augmented Dickey-Fuller (BSADF) test, explosive price behaviors and flash crashes are detected and dated. To examine cross-asset dependencies and nonlinear co-movements in net bubble values, a copula-based modeling framework is applied. Additionally, network analysis is conducted to map interconnections during episodes of financial exuberance. Findings reveal that cryptocurrencies exhibit a higher frequency and intensity of speculative episodes compared to Big Tech stocks. However, within the Big Tech universe, Tesla demonstrates bubble characteristics on par with the most volatile cryptocurrencies. The network analysis further underscores a strong interdependence between extreme events in the cryptocurrency sector and Tesla stock behavior, suggesting potential systemic linkages.
