While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps clustered around black swan events, resembling volatility and trading volume seasonalities. Regressions show that intra-day jumps significantly influence end of day returns in size and direction. This provides fundamental research for crypto option pricing models. However, we need better econometric methods for capturing the specific market microstructure of cryptos. All calculations are reproducible via the quantlet.com technology.
翻译:虽然注意是数字资产价格的预测,而Bitcoin价格的暴涨是众所周知的,但我们对其替代物知之甚少。 研究高频加密数据使我们有独特的机会确认跨市场数字资产回报是由黑天鹅事件、类似波动和交易量季节性的高频跳动驱动的。 回归表明日内跳跃对日终回报的大小和方向影响很大。 这为加密选项定价模型提供了基础研究。 但是,我们需要更好的计量生态的方法来捕捉具体的隐蔽物市场微观结构。 所有计算都可以通过昆虫技术复制。 com。