Financial Condition Indices in an Incomplete Data Environment

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.