Using a market-indicator-based approach, this paper empirically examines whether the stability of the US and EU financial systems is affected by the digital finance revolution driven by BigTechs, FinTechs, and crypto-assets. These three sectors display different downside volatility profiles, with financial intermediaries being particularly sensitive to shocks from the crypto ecosystem only under extremely severe downturns, which are prevented in regulated equity markets. In that vein, we provide evidence that the Markets in Crypto Assets Regulation reduced financial systemic risk in EU. Overall, our empirical analysis shows that markets perceive the performance and riskiness of tech-driven companies and assets in differentiated ways, and that the transmission of shocks from digital finance ecosystems operates uniquely under varying conditions of systemic stress. Finally, we also document asymmetric spillover effects between advanced and emerging economies, with shock transmission from the US and EU to emerging markets being systematically stronger than in the reverse direction.

Bitcoin plunged by 30% on May 19, 2021. We examine the outage the largest crypto exchange Binance experienced during the crash, when it halted trading for retail clients and stopped providing transaction data. We find evidence that Binance back-filled these missing transactions with data that does not conform to Benford‘s Law. The Bitcoin futures price difference between Binance and other exchanges was seven times larger during the crash period compared to a prior reference period. Data manipulation is a plausible explanation for our findings. These actions are in line with Binance aiming to limit losses for its futures-related insurance fund.

By computing a volatility index (CVX) from cryptocurrency option prices, we analyze this market’s expectation of future volatility. Our method addresses the challenging liquidity environment of this young asset class and allows us to extract stable market implied volatilities. Two alternative methods are considered to compute volatilities from granular intra-day cryptocurrency options data, which spans over the COVID-19 pandemic period. CVX data therefore capture ‘normal’ market dynamics as well as distress and recovery periods. The methods yield two cointegrated index series, where the corresponding error correction model can be used as an indicator for market implied tail-risk. Comparing our CVX to existing volatility benchmarks for traditional asset classes, such as VIX (equity) or GVX (gold), confirms that cryptocurrency volatility dynamics are often disconnected from traditional markets, yet, share common shocks.