论文标题
霍克斯的过程建模比特币区块链中
Hawkes Process Modeling of Block Arrivals in Bitcoin Blockchain
论文作者
论文摘要
该论文构建了比特币块到达和价格上涨的多变量鹰队过程模型。霍克斯的过程是自我激发点过程，可以捕获块挖掘和比特币价格波动的自我和交叉驱散作用。我们使用公开可用的区块链数据集通过最大似然估计来估计模型参数。结果表明，比特币价格波动率提高了块开采率和比特币投资回报表明平均归还。分位数量词图表明，所提出的霍克斯过程模型比泊松过程模型更适合区块链数据集。
The paper constructs a multi-variate Hawkes process model of Bitcoin block arrivals and price jumps. Hawkes processes are selfexciting point processes that can capture the self- and cross-excitation effects of block mining and Bitcoin price volatility. We use publicly available blockchain datasets to estimate the model parameters via maximum likelihood estimation. The results show that Bitcoin price volatility boost block mining rate and Bitcoin investment return demonstrates mean reversion. Quantile-Quantile plots show that the proposed Hawkes process model is a better fit to the blockchain datasets than a Poisson process model.
