Dissecting the financial cycle with dynamic factor models

Christian Menden and Christian Proaño

No 126, BERG Working Paper Series from Bamberg University, Bamberg Economic Research Group

Abstract: The analysis of the financial cycle and its interaction with the macroeconomy has become a central issue for the design of macroprudential policy since the 2007-08 financial crisis. This paper proposes the construction of financial cycle measures for the US based on a large data set of macroeconomic and financial variables. More specifically, we estimate three synthetic financial cycle components that account for the majority of the variation in the data set using a dynamic factor model. We investigate whether these financial cycle components have significant predictive power for economic activity, inflation and short-term interest rates by means of Granger causality tests in a factor-augmented VAR set-up. Further, we analyze if the synthetic financial cycle components have significant forecasting power for the prediction of economic recessions using dynamic probit models. Our main findings indicate that all financial cycle measures improve the quality of recession forecasts significantly. In particular, the factor related to financial market participants' uncertainty and risk aversion - related to Rey's (2013) global financial cycle - seems to serve as an appropriate early warning indicator for policymakers.

Keywords: financial cycle; dynamic factor model; Granger causality; recession forecasting; dynamic probit models; early warning systems (search for similar items in EconPapers)
JEL-codes: C35 C38 C52 C53 E32 E47 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-for and nep-mac
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Citations: View citations in EconPapers (19)

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Related works:
Journal Article: Dissecting the financial cycle with dynamic factor models (2017)
Working Paper: Dissecting the financial cycle with dynamic factor models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bamber:126

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