Good and bad uncertainty: Macroeconomic and financial market implications ☆

By Gill Segal, Ivan Shaliastovich, Amir Yaron. 2015

453 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f631540c364dafca97293f7227e0c5ac112c5aaf

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Citations in the corpus

Nonparametric Estimation of the Leverage Effect: A Trade-Off Between Robustness and Efficiency (2017, methodology): “These methods are now commonly used in economics and finance, see, for example, Patton and Verardo (2012), Bandi and Renò (2015), Bollerslev, Li, and Todorov (2016), and Segal, Shaliastovich, and Yaron (2015).”
The effects of uncertainty on market liquidity: Evidence from Hurricane Sandy (2019, background): “12See Bloom et al. (forthcoming) for a recent and highly visible example in the macroeco- nomic context and Segal et al. (2015) for a recent example in the macro-finance context.”
Asymmetric Connectedness on the U.S. Stock Market: Bad and Good Volatility Spillovers (2015, unknown): “Segal et al. (2015) define bad uncertainty as the volatility that is associated with negative innovations to quantities (e.g., output, returns) and good uncertainty as the volatility that is associated with positive shocks to these variables.”
Downside Variance Risk Premium (2015, unknown): “2Similar to Segal, Shaliastovich, and Yaron (2015) and Bekaert, Engstrom, and Ermolov (2014), we define “good uncertainty” and “bad uncertainty” as volatility associated with positive or negative shocks to fundamentals such as consumption of dividend growth.”
Do cryptocurrencies and traditional asset classes influence each other? (2019, methodology): “We can use negative semivariance as a proxy for bad volatility, and positive semivariance as a proxy for good volatility (Segal, Shaliastovich, and Yaron, 2015), thus, it allows us to observe different effects of good and bad volatility.”
Textual analysis in finance (2019, background): “Some authors uses quantitative variables as a proxy for uncertainty (Carrière-Swallow and Céspedes, 2013; Segal, Shaliastovich, and Yaron, 2015) or events that cause uncertainty shocks (Liu, Shu, and Wei, 2017).”