A Survey of Alternative Measures of Macroeconomic Uncertainty: Which Measures Forecast Real Variables and Explain Fluctuations in Asset Volatilities Better?

Alexander David () and Pietro Veronesi ()
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Alexander David: Haskayne School of Business, University of Calgary, Calgary, Alberta, Canada
Pietro Veronesi: Booth School of Business, University of Chicago, Chicago, Illinois, USA

Annual Review of Financial Economics, 2022, vol. 14, issue 1, 439-463

Abstract: In the past 20 years, measures of economic uncertainty have been developed that are purely market price based; structural model based, using data on real fundamentals and asset prices; text based; or survey based. We compare the performance of these uncertainty measures in forecasting three real variables with irreversibilities—investment, hiring, and credit creation—as well as in explaining fluctuations in stock market and Treasury bond market volatility. In general, we find that structural model–based measures do better than measures constructed using other approaches, with a model of stock market volatility by David and Veronesi performing the best on several (but not all) dimensions. Their learning-based model's volatility places time-varying weights on inflation, earnings, and consumption news, as agents in the economy assess the impact that inflation has on the stability of real economic growth.

Keywords: forecasting; growth; inflation; irreversibility; learning; uncertainty; volatility (search for similar items in EconPapers)
JEL-codes: C32 C53 E22 E24 E31 E32 E44 G12 G17 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1146/annurev-financial-111720-091804

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