论文标题
$ k- $ bahc协方差清洁，反应性全球最小差异投资组合
Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning
论文作者
论文摘要
我们引入了$ k $折叠的版本，以进行相关性和协方差矩阵的平均平均分层聚类清洁程序。然后，我们将此方法应用于全球最小差异组合的各种$ K $的值，并将其性能与其他最先进的方法进行比较。通常，我们发现，尽管需要更大的营业额，但交易成本后的方法比竞争过滤方法产生更好的急剧比率。
We introduce a $k$-fold boosted version of our Boostrapped Average Hierarchical Clustering cleaning procedure for correlation and covariance matrices. We then apply this method to global minimum variance portfolios for various values of $k$ and compare their performance with other state-of-the-art methods. Generally, we find that our method yields better Sharpe ratios after transaction costs than competing filtering methods, despite requiring a larger turnover.
