Financial Condition Indices in an Incomplete Data Environment

Abstract We construct a Financial Conditions Index (FCI) for the United States using a dataset that features many missing observations. The novel combination of probabilistic principal component techniques and a Bayesian factor-augmented VAR model resolves the challenges posed by data points being unavailable within a high-frequency dataset. Even with up to 62 % of the data missing, the new approach yields a less noisy FCI that tracks the movement of 22 underlying financial variables more accurately both in-sample and out-of-sample.