Financial condition indices in an incomplete data environment

Herculano, M. C. and Jacob, P. (2023) Financial condition indices in an incomplete data environment. Studies in Nonlinear Dynamics and Econometrics, (doi: 10.1515/snde-2022-0115) (Early Online Publication)

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.

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Colburn Herculano, Mr Miguel
• Authors: Herculano, M. C., and Jacob, P.
• College/School: College of Social Sciences > Adam Smith Business School > Economics
• Journal Name: Studies in Nonlinear Dynamics and Econometrics
• Publisher: De Gruyter
• ISSN: 1081-1826
• ISSN (Online): 1558-3708
• Published Online: 21 December 2023
• Copyright Holders: Copyright © 2023 Walter de Gruyter GmbH, Berlin/Boston
• First Published: First published in Studies in Nonlinear Dynamics and Econometrics 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy