Recent research by David Alaminos, M. Belén Salas-Compás, and Manuel Á. Fernández-Gámez investigates whether Bitcoin can exert speculative pressure on fiat currencies, particularly the US Dollar, during episodes of financial stress.

Using a novel hybrid model that integrates ARIMA-EGARCH, and Wavelet Neural Networks (WNN)—with additional enhancement through a Quantum Recurrent Neural Network (QRNN)—the authors analyze dynamic interactions between BTC/USD and EUR/USD exchange rates. The focus is on episodes of speculative attacks and short squeezes, moments characterized by abrupt currency movements and heightened trader response.

The findings show that Bitcoin trading volumes and price shocks are not confined to the crypto space. Instead, they can spill over into fiat currency markets, increasing volatility in the US Dollar and indirectly affecting EUR/USD dynamics. The study demonstrates that during high-stress financial periods, the BTC/USD rate acts as a volatility transmitter, prompting traders in traditional forex markets to rebalance portfolios, thereby creating feedback loops.

The hybrid model successfully captures non-linear relationships, volatility clustering, and time-frequency interactions. Moreover, when enhanced with a QRNN layer, the model achieves higher predictive accuracy, especially in anticipating volatility spikes during stress episodes. These results underscore Bitcoin’s potential to amplify systemic risk in broader currency markets.

The paper challenges the traditional view that Bitcoin operates in isolation. Instead, it positions Bitcoin as a speculative catalyst, capable of influencing global currency movements and complicating monetary policy transmission.

The authors conclude that as the crypto-financial ecosystem deepens its links with fiat markets, regulators and policymakers must incorporate these interactions into risk monitoring frameworks. Ignoring them could leave systemic vulnerabilities unaddressed, particularly during periods of liquidity stress or policy uncertainty.

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