The Term Structure of Growth-at-Risk

Adrian, Tobias; Grinberg, Federico; Liang, Nellie; Malik, Sheheryar

doi:10.2139/ssrn.3236794

Cited by 17 publications

(20 citation statements)

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“…Finally, Adrian et al. (2018) estimate quantile regressions which suggest that easy financial conditions and rapid credit growth raise the risk of a large decline in real growth over the next three years.…”

Section: Credit‐market Overheating and Future Economic Growthmentioning

confidence: 99%

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Predictable Financial Crises

Greenwood¹,

Hanson²,

Shleifer³

et al. 2022

The Journal of Finance

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Using historical data on postwar financial crises around the world, we show that the combination of rapid credit and asset price growth over the prior three years, whether in the nonfinancial business or the household sector, is associated with a 40% probability of entering a financial crisis within the next three years. This compares with a roughly 7% probability in normal times, when neither credit nor asset price growth is elevated. Our evidence challenges the view that financial crises are unpredictable “bolts from the sky” and supports the Kindleberger‐Minsky view that crises are the byproduct of predictable, boom‐bust credit cycles. This predictability favors policies that lean against incipient credit‐market booms.

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Section: Credit‐market Overheating and Future Economic Growthmentioning

confidence: 99%

“…Adrian et al. (2018) find that financial stability measures—which include credit growth—predict higher downside risks to GDP growth. We show that the business and household R‐zones also reliably predict GDP contractions, which we define as a 2% decline in real GDP in a given year.…”

mentioning

confidence: 99%

Predictable Financial Crises

Greenwood¹,

Hanson²,

Shleifer³

et al. 2022

The Journal of Finance

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show abstract

“…Before turning to results, a bit more review of quantile regressions may help some readers. While quantile regressions are less widely used in macroeconomics than least squares, the GDP at Risk literature has used the approach extensively (e.g., Cecchetti and Li 2008, IMF 2017, Adrian et al 2018, Adrian, Boyarchenko, and Giannone 2019. Moreover, the intuition is straightforward: quantile regression simply weights errors in the projections more heavily for errors near the quantile of interest and less heavily for errors distant from the quantile of interest.…”

Section: Measuring Unemployment Riskmentioning

confidence: 99%

Unemployment Risk

Kiley

2021

J of Money Credit Banking

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Fluctuations in the risk of a large increase in unemployment are examined. The analysis compares medium‐term risks—that is, risks at a 3‐year horizon—to those over a 1‐year horizon. The primary approach involves quantile regressions. Robustness exercises examine risks using a logistic regression to model the probability of a large increase in the unemployment rate. U.S. experience reveals an elevated risk of large increases in unemployment over the medium term when credit growth is high and when the unemployment rate is low. Near‐term risks to unemployment are closely tied to changes in corporate bond spreads or the slope of the yield curve, consistent with research on recession prediction, but these factors do not play a sizable role in medium‐term risks. These results highlight the value of considering different near‐ and medium‐term risk factors.

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“…In particular, for the monthly variables, we find that indicators related to economy, financial sector and monetary policy contribute significantly to increase forecast accuracy relative to the available macroeconomic information. This is a favorable result that could provide more accurate measures of Growth-at-Risk (see Adrian et al, 2018 andBrownlees andSouza, 2021 for further discussion about Growth-at-Risk applications).…”

Section: Introductionmentioning

confidence: 99%

Forecasting with Economic News

Barbaglia¹,

Consoli²,

Manzan³

2022

Preprint

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The goal of this paper is to evaluate the informational content of sentiment extracted from news articles about the state of the economy. We propose a fine-grained aspect-based sentiment analysis that has two main characteristics: 1) we consider only the text in the article that is semantically dependent on a term of interest (aspect-based) and, 2) assign a sentiment score to each word based on a dictionary that we develop for applications in economics and finance (fine-grained). Our data set includes six large US newspapers, for a total of over 6.6 million articles and 4.2 billion words. Our findings suggest that several measures of economic sentiment track closely business cycle fluctuations and that they are relevant predictors for four major macroeconomic variables. We find that there are significant improvements in forecasting when sentiment is considered along with macroeconomic factors. In addition, we also find that sentiment matters to explains the tails of the probability distribution across several macroeconomic variables.

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The Term Structure of Growth-at-Risk

Cited by 17 publications

References 0 publications

Predictable Financial Crises

Predictable Financial Crises

Unemployment Risk

Forecasting with Economic News

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