Hierarchical PCA and Applications to Portfolio Management

TL;DR

This paper introduces Hierarchical PCA (HPCA), a sector-based approach that simplifies interpretation of risk factors in portfolio management, maintaining information fidelity and extending applicability to markets with asynchronous data.

Contribution

The paper proposes HPCA, a sector-partitioned PCA method that preserves information and enhances interpretability, applicable to equities and markets with asynchronous data.

Findings

HPCA retains all information of standard PCA for equities.

Associated factors in HPCA are easily interpretable.

Applicable to markets with asynchronous price information.

Abstract

It is widely known that the common risk-factors derived from PCA beyond the first eigenportfolio are generally difficult to interpret and thus to use in practical portfolio management. We explore a alternative approach (HPCA) which makes strong use of the partition of the market into sectors. We show that this approach leads to no loss of information with respect to PCA in the case of equities (constituents of the S&P 500) and also that the associated common factors admit simple interpretations. The model can also be used in markets in which the sectors have asynchronous price information, such as single-name credit default swaps, generalizing the works of Cont and Kan (2011) and Ivanov (2016).