Financial Frictions and Fluctuations in Volatility

Arellano, Cristina; Bai, Yan; Kehoe, Patrick J.

doi:10.1086/701792

Cited by 251 publications

(81 citation statements)

References 39 publications

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“…Three main findings have emerged from the extant literature: First, heightened uncertainty triggers a contraction in real activity; second, uncertainty tends to be higher during economic recessions; third, the effects of uncertainty shocks are not constant over time. The fact that the relationship between uncertainty and real activity may not be constant over time is consistent with theoretical models that show how the effects of heightened uncertainty can be amplified in extreme conditions such as high financial stress (e.g., Alfaro, Bloom, & Lin, 2018;Arellano, Bai, & Kehoe, 2018;Gilchrist, Sim, & Zakrajsek, 2014) or when monetary policy is constrained by the zero lower bound (Basu & Bundick, 2017). The prevailing view is that uncertainty is recessionary in the presence of real options effects (e.g., Bloom, 2009) or financial frictions (e.g., Christriano, Motto, & Rostagno, 2014).…”

Section: Introductionsupporting

confidence: 80%

Uncertainty across volatility regimes

Angelini

Bacchiocchi

Caggiano

et al. 2018

J of Applied Econometrics

102

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Summary We propose a nonrecursive identification scheme for uncertainty shocks that exploits breaks in the volatility of macroeconomic variables and is novel in the literature on uncertainty. This approach allows us to simultaneously address two major questions in the empirical literature: Is uncertainty a cause or effect of decline in economic activity? Does the relationship between uncertainty and economic activity change across macroeconomic regimes? Results based on a small‐scale vector autoregression with US monthly data suggest that (i) uncertainty is an exogenous source of decline of economic activity, and (ii) the effects of uncertainty shocks amplify in periods of economic and financial turmoil.

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Section: Introductionsupporting

confidence: 80%

Uncertainty across volatility regimes

Angelini

Bacchiocchi

Caggiano

et al. 2018

J of Applied Econometrics

102

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show abstract

“…Following Bergin and Corsetti (), the love of variety parameter is set at

γ = σ / false(italicσ - 1 false)

in the baseline calibration. The risk aversion of the worker and the investor are set respectively at ρ = 2 (Arellano et al ., ) and ρ I = 1 (Iacoviello, ) to reflect that firms are more willing to take risks than workers. Both values are commonly used in macroeconomics literature.…”

Section: Quantitative Analysismentioning

confidence: 99%

Firm Entry and Financial Shocks

Bergin

Feng²,

Lin

2017

The Economic Journal

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This article shows that firm entry dynamics are an important part of the propagation of financial shocks to the real macroeconomy. A VAR documents empirically that adverse financial shocks are associated with significant declines in both new firm creation and equity values. A DSGE business cycle model combining endogenous firm entry and financial frictions, where firms have a choice of financing entry through debt or equity, can explain these facts. The model implies that adjustment in firm numbers can moderate the impact of financial shocks on aggregate output, as it buffers the equity value and financial stance of surviving firms.

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“…A recent literature on uncertainty emphasizes that firm‐level shocks appear more volatile during downturns (Bloom , Bloom et al. ) and that this basic fact may affect the efficiency of firm investment, aggregate productivity, financial markets, and output fluctuations over the cycle (Bachmann and Bayer , Senga , Arellano, Bai, and Kehoe ). In this subsection, I extend the model with fluctuations in the volatility of microeconomic shocks.…”

Section: Projection Versus Perturbation In Aggregatesmentioning

confidence: 99%

Alternative Methods for Solving Heterogeneous Firm Models

Terry¹

2017

J of Money Credit Banking

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I implement and compare five solution methods for a benchmark heterogeneous firms model with lumpy capital adjustment and aggregate uncertainty. The Krusell–Smith algorithm performs best within a group of methods using projection in the aggregate states. Another technique, Parameterization plus Perturbation, is much faster and performs best within a group of methods using perturbation in aggregates. However, projection and perturbation have nonoverlapping strengths and weaknesses. I highlight the resulting trade‐offs with several model extensions. I recommend that researchers apply projection methods to cases with large shocks or nonlinear dynamics, while cases with explicitly distributional channels at work favor perturbation.

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Financial Frictions and Fluctuations in Volatility

Cited by 251 publications

References 39 publications

Uncertainty across volatility regimes

Uncertainty across volatility regimes

Firm Entry and Financial Shocks

Alternative Methods for Solving Heterogeneous Firm Models

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