Can Adaptive Seriational Risk Parity Tame Crypto Portfolios?

Abstract

As cryptocoins are not tied to fundamental values or to investor protection regulation, their price dynamics is unhinged in both directions. In institutional asset management of conventional asset classes, target volatility concepts and dynamic allocation heuristics are popular to improve the robustness of portfolios. Can similar techniques also be used to construct delevered and diversified portfolios of crypto assets? A robust candidate approach for allocation is Hierarchical Risk Parity (HRP), as it incorporates a filtered correlation structure and is less sensitive to noise than quadratic optimization, as shown in several studies. Recent publications have extended the concept of HRP in several directions. We compare some of these extensions to determine which variant is most useful for constructing crypto baskets. We find that a particular type of adaptive HRP strategy outperforms other extensions on a risk-adjusted basis, leading us to a deeper investigation of the changing nature of correlation structures between cryptos - both quantitatively and visually. We find that structural breaks in crypto correlations are prevalent and that the best-fitting hierarchical cluster representations change over time, which is only captured by distance matrix-based adaptive HRP approaches.

Keywords: Machine learning, Graph theory, Hierarchical tree clustering, Asset allocation, Cryptocurrencies

JEL Classification: G15, G41