Systemic risk and the macroeconomy: An empirical evaluation

Giglio, Stefano; Kelly, Bryan T.; Pruitt, Seth

doi:10.1016/j.jfineco.2016.01.010

Cited by 409 publications

(99 citation statements)

References 44 publications

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“…The analysis is also linked to recent attempts to measure systemic risk using only publicly available information (Billio et al. , Adrian and Brunnermeier , Giglio, Kelly, and Pruitt , Acharya et al. , among others).…”

Section: Related Literaturementioning

confidence: 99%

How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe

Zinna

2018

J of Money Credit Banking

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We examine European banks' exposures to systematic and country‐specific sovereign risk. We organize our investigation around a multifactor affine credit risk model estimated on credit default swap data of different maturities. During the 2008–15 period, about one third of banks' credit risk is sovereign. However, banks strongly differ both in the magnitude and type of their sovereign exposures. Measures of indirect exposures, such as bank size and return on equity, capture these cross‐sectional differences better than measures of direct exposures. Furthermore, the properties of the distress risk premiums turn out to be important to understand the effect of sovereign risk on bank funding costs.

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Section: Related Literaturementioning

confidence: 99%

How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe

Zinna

2018

J of Money Credit Banking

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“…conditions, this subsection presents the key results of interest obtained by measuring financial conditions with the turbulence indicator ofGiglio, Kelly, and Pruitt (2016). In these results, turbulence replaces the NFCI as the financial variable in the VAR and QR models and as the variable to which volatility is attached in the BVAR-SVF-M specification.…”

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confidence: 99%

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Carriero

Clark

Marcellino

2020

Working Paper (Federal Reserve Bank of Cleveland)

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A rapidly growing body of research has examined tail risks in macroeconomic outcomes. Most of this work has focused on the risks of significant declines in GDP, and it has relied on quantile regression methods to estimate tail risks. Although much of this work discusses asymmetries in conditional predictive distributions, the analysis often focuses on evidence of downside risk varying more than upside risk. We note that this pattern in risk estimates over time could obtain with conditional distributions that are symmetric but subject to simultaneous shifts in conditional means (down) and variances (up). Building on that insight, we examine the ability of Bayesian VARs with stochastic volatility to capture tail risks in macroeconomic forecast distributions and outcomes. We consider both a conventional stochastic volatility specification and a specification with a common factor in volatility that enters the VAR's conditional mean. Even though the one-step-ahead conditional predictive distributions from the conventional stochastic volatility specification are symmetric, the model estimates yield more time variation in downside risk as compared to upside risk. Results from the model that includes a volatility factor in the conditional mean and thereby allows for asymmetries in conditional distributions are very similar. Our paper also extends the recent literature by formally evaluating the accuracy of tail risk forecasts and assessing the performance of Bayesian quantile regression, as well as our Bayesian VARs, in this context. Overall, the BVAR models perform comparably to quantile regression for estimating and forecasting tail risks, complementing BVARs' established performance for forecasting and structural analysis.

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“…From Equation 4 we havêi(k∕n) 1∕̂sV aR i (k∕n) =̂T iV aR s (k∕n). Similarly, Giglio et al (2016) observed return volatility in the financial sector to be a significant predictor of macroeconomic tail risk, whereas nonfinancial volatility was not. 12 For two early applications in the context of asset return linkages; see Hartmann et al (2004) and Straetmans et al (2008).…”

Section: Relationship To Other Measuresmentioning

confidence: 86%

Systemic risk and bank business models

Oordt

Zhou

2018

J of Applied Econometrics

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In this paper, we decompose banks' systemic risk into two dimensions: the risk of a bank ("bank tail risk") and the link of the bank to the system in financial distress ("systemic linkage"). Based on extreme value theory, we estimate a systemic risk measure that can be decomposed into two subcomponents reflecting these dimensions. Empirically, we assess the relationships of bank business models to the two dimensions of systemic risk. The observed differences in these relationships partly explain why micro-and macroprudential perspectives sometimes have different implications for banking regulation.

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Systemic risk and the macroeconomy: An empirical evaluation

Cited by 409 publications

References 44 publications

How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe

How Much of Bank Credit Risk Is Sovereign Risk? Evidence from Europe

Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions

Systemic risk and bank business models

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