Bayesian Change Point Analysis of Bitcoin Returns
Bayesian Change Point Analysis of Bitcoin Returns
复制标题
比特币收益的贝叶斯变点分析
DOI：
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发表时间：
2018
影响因子：
10.4
通讯作者：
Péter Molnár
中科院分区：
文献类型：
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作者：
Sven Thies;Péter Molnár
本文研究了比特币价格的平均回报和波动性是否存在结构性突破。我们利用贝叶斯变化点模型来检测结构断裂并将时间序列划分为片段。我们发现比特币平均回报率和波动性的结构性突破非常频繁。通过将具有相似属性的部分合并到制度中，我们确定了几种具有正平均回报的制度和一种具有负平均回报的制度。在各个制度中，较高的波动性与较高的平均回报相关，但波动性最大的制度除外，这是唯一平均回报为负的制度。
This paper studies existence of structural breaks in the average return and volatility of the Bitcoin price. We utilize a Bayesian change point model to detect structural breaks and to partition the time series into segments. We find that structural breaks in average returns and volatility of Bitcoin are very frequent. By merging segments with similar properties into regimes we identify several regimes with positive average returns and one regime with negative average returns. Across regimes, higher volatility is associated with higher average returns, with exception of the most volatile regime, which is the only regime with negative average returns.