Penalized Independent Factor

Abstract

We propose a penalized independent factor (PIF) method to extract independent factors via a sparse estimation. Compared to the conventional independent component analysis, each PIF only depends on a subset of the measured variables and is assumed to follow a realistic distribution. Our main theoretical result claims that the sparse loading matrix is consistent. We detail the algorithm of PIF, investigate its finite sample performance and illustrate its possible application in risk management. We implement the PIF to the daily probability of default data from 1999 to 2013. The proposed method provides good interpretation of the dynamic structure of 14 economies’ global default probability from pre-Dot Com bubble to post-Sub Prime crisis.