Fast hierarchical risk parity methods for portfolio selection
针对分层风险平价方法在大规模资产中计算慢的问题，研究了资产排列的不变性和决策空间大小，提出一种快速方法，在保证类似表现的同时大幅缩短计算时间。
Abstract Hierarchical Risk Parity methods address some of the limitations of the classical mean-variance approach to portfolio selection by deriving a hierarchical structure. These methods are based on hierarchical clustering techniques and the recursive bisection of an ordered list of assets. When the number of assets is large, computational time becomes a limitation. This paper finds invariants of the allocation produced by simple asset permutations. We also study the size of the decision space to improve the understanding of the allocation algorithm. Building on these results, we propose a fast hierarchical risk parity portfolio selection method that reduces computational time while ensuring a similar performance.