The dynamic frequency-based shock transmission between Bitcoin investors’ emotions and U.S. sectoral stock conditional volatility: assessing the optimal weightage of Bitcoin returns in a U.S. sectoral stock return portfolio

This study pioneers the investigation of how shocks are transmitted between the conditional volatility of U.S. sectoral equity markets and investor sentiment in Bitcoin – measured via fear and greed indices (BSI) – across both high- and low-frequency time scales. To achieve this, we adopt a connectedness framework in both time and frequency domains, grounded in the Generalized Vector Auto-regression methodology. Additionally, we implement the DCC-GARCH-t copula model to derive optimal portfolio allocations, with the objective of identifying effective strategies for hedging long-term fluctuations in Bitcoin returns through sectoral U.S. equities. Overall findings suggested that a shock in the conditional volatility of all the U.S. sectoral stock transmitted higher contribution of shocks of 36.91% in the long-term as compared with only 9.56% in the short-term toward the BSI. Whereas, in the short-term, BSI also transmitted the lower contribution of shocks of 5.70% as compared with 25.2% in the long-term toward all the U.S. sectoral stocks’ conditional volatility. Moreover, in the short-term (long-term), the U.S. sectoral stocks of Financials, Industries and Materials (Industries) received the lowest contributions of shocks from BSI. Ultimately, the results indicate that utilizing optimal portfolio weight strategy derived from the DCC-GARCH-t copula model yields superior hedging performance relative to conventional hedge ratio methods. Therefore, to hedge against Bitcoin’s long-term volatility, portfolios that include U.S. sectoral stocks from Health Care, Goods, Telecommunications and Financials exhibit superior hedging effectiveness of 94%, 93%, 93% and 92%, respectively. We offer comprehensive practical ramification for long-term stockholders, fund managers and speculators.