Disasters Risk and Business Cycles

By François Gourio. 2012

665 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/2237adb3dc2bb568f5221a17526d0d2719cb1ce1

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Citations in the corpus

Inequality, Leverage and Crises: The Case of Endogenous Default (2013, neutral): “[26] Gourio, F.”
Asset Pricing in the Frequency Domain: Theory and Empirics (2016, methodology): “we apply the standard Euler equation estimation methodology of Hansen and Singleton (1982) using the specific functional forms implied by utility-based models, no coefficients are consistently significant across various groups of test assets (and the implied risk-aversion parameters at the point est”
Uncertainty Shocks as Second-Moment News Shocks (2017, result): “That is natural as standard models imply 3It is conceptually similar to consumption-based models like Barro (2006) and production-based models like Gourio (2012), but with smaller and more frequent “disasters”, consistent with the evidence in Backus, Chernov, and Martin (2011).”
Uncertainty Shocks, Asset Supply and Pricing over the Business Cycle (2018, neutral): “The latter can take the form of time varying disaster risk (Gourio (2012)) or stochastic volatility (Basu and Bundick (2012), Caldara et al. (2012), Malkhozov and Shamloo (2012)).”
‘Wait-and-See’ business cycles? (2013, background): “Another literature stresses the importance of rare, but drastic surprise changes in the economic environment: see Gourio (2012).”
Tail Risk and Asset Prices (2014, methodology): “Examples include the Rietz (1988) and Barro (2006) rare disaster hypothesis and its extensions to dynamic settings by Gabaix (2011), Gourio (2012) and Wachter (2013), as well as extensions of Bansal and Yaron’s (2004) long run risks model that incorporate fattailed endowment shocks (Eraker and…”
News Implied Volatility and Disaster Concerns (2017, methodology): “We dig deeper into the nature of the uncertainty captured by NVIX and find three pieces of evidence that these return predictability results are driven by variation in investors’ concerns regarding rare disasters as in Gabaix (2012), Wachter (2013) and Gourio (2008, 2012).”