High-Frequency Credit Spread Information and Macroeconomic Forecast Revision

50 Pages Posted: 1 Jul 2019

See all articles by Bruno Deschamps

Bruno Deschamps

University of Nottingham, Ningbo

Christos Ioannidis

Aston University - Aston Business School

Kook Ka

Bank of Korea - Economic Research Institute

Date Written: May 3, 2019

Abstract

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using Mixed Data Sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both aggregate and individual forecast levels. The relations are shown to be notably strong during ‘bad’ economic conditions, suggesting that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating the amplification effect of financial developments on macroeconomic aggregates. Forecasts do not incorporate the totality of financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information.

Keywords: Forecast Revision, GDP Forecast, Credit Spread, High-Frequency Data, Mixed Data Sampling (MIDAS)

JEL Classification: C53, E32, E44

Suggested Citation

Deschamps, Bruno and Ioannidis, Christos and Ka, Kook, High-Frequency Credit Spread Information and Macroeconomic Forecast Revision (May 3, 2019). Bank of Korea WP 2019-17. Available at SSRN: https://ssrn.com/abstract=3381999 or http://dx.doi.org/10.2139/ssrn.3381999

Bruno Deschamps (Contact Author)

University of Nottingham, Ningbo ( email )

199 Taikang East Road
Ningbo, Zhejiang 315100

Christos Ioannidis

Aston University - Aston Business School ( email )

Aston Triangle
Birmingham, B47ET
United Kingdom

Kook Ka

Bank of Korea - Economic Research Institute ( email )

110, 3-Ga, Namdaemunno, Jung-Gu
Seoul 100-794
Korea, Republic of (South Korea)

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