Marco Avellaneda (New York University)
Hierarchical Principal Component Analysis and applications to portfolio management
Common risk-factors derived from PCA beyond the first factor are generally difficult to interpret and thus to use in practical portfolio management. We explore an 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, extending previous work of Cont and Kan (2011) and Ivanov (2016).