TAIL RISK HEDGING WITH CRYPTOCURRENCIES: A COPULA-GARCH APPROACH FOR GLOBAL SECTORAL EQUITIES

Abstract

Given the rising volatility in global financial markets and the increasing demand for sophisticated risk management approaches, this research explores the potential of cryptocurrencies as instruments for hedging against extreme losses in sectoral equity markets. The study primarily aims to assess the capacity of digital assets, specifically Bitcoin and Ethereum, to alleviate severe downside risks within sectoral portfolios, with particular attention to technology equities. A quantitative methodology was adopted, integrating GARCH (1,1) models to estimate marginal volatility, Gaussian copula functions to model dependencies, and machine learning classifiers trained on simulation-generated datasets using Python on Google Colab. The results indicate that cryptocurrencies, notably Bitcoin, provide greater predictive value for identifying tail events than conventional sectoral equity measures, with Bitcoin emerging as the most significant feature for tail risk prediction. Nevertheless, the classifier encountered difficulties in detecting rare tail events accurately, as evidenced by zero precision, recall, and F1-scores, attributed to pronounced class imbalance. The study suggests that cryptocurrencies convey meaningful signals regarding tail risk dynamics and warrant consideration in portfolios sensitive to such risks. It advocates incorporating alternative assets and simulation-driven stress testing within tail risk management frameworks. Practically, these findings offer guidance for institutional investors seeking adaptive, data-oriented hedging strategies. A principal limitation of the study is the skewed distribution of data, which reduced model sensitivity; future investigations should address this through more sophisticated copula models and balanced learning methodologies.