Stock Market Volatility and Macroeconomic Fundamentals

By Robert F. Engle, Eric Ghysels, Bumjean Sohn. Review of Economics and Statistics, 2013

977 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/62d08d505abd19a8feaa6952d2574259a40751f3

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Citations in the corpus

An Intertemporal CAPM with Stochastic Volatility (2018, background): “A much smaller literature has, like us, looked directly at the information in other economic and financial variables concerning future volatility ( Schwert, 1989; Christiansen et al., 2012; Paye, 2012; Engle et al., 2013 ).”
A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, neutral): “…and Galvao (2009), Kuzin et al. (2011), Kuzin et al. (2013), Marcellino et al. (2013)), stock market volatility (Ghysels et al. (2007), Ghysels and Valkanov (2012)) and the relation between stock market volatility and macroeconomic activity (Engle et al. (2013) and Schorfheide et al. (2014)).”
Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis (2018, methodology): “3 Fang et al. (2018) investigate whether global economic policy uncertainty predicts long-term gold volatility.”
Anticipating Long‐Term Stock Market Volatility (2014, positive): “Although future values of Xt may contain useful information about current long-term stock market volatility, the two-sided specification considered in Engle et al. (2012) is infeasible in realtime.”
Forecasting: theory and practice (2020, methodology): “ARCH terms in the fractionally integrated GARCH (FIGARCH) model. Separately, in a further approach to directly estimating long term volatility, the GARCH-MIDAS (Mixed Data Sampling) model proposed by Engle et al. (2013) decomposes the conditional volatility into the short-term volatility, as capture”
Stock market volatility: Identifying major drivers and the nature of their impact (2015, background): “Engle et al. (2013) use inflation and industrial production in a mixed-frequency GARCH framework to predict the volatility of U.S. stock returns.”
Exogenous drivers of Bitcoin and Cryptocurrency volatility – A mixed data sampling approach to forecasting (2019, methodology): “The GARCH-MIDAS model (Engle et al., 2013) disentangles the volatility into a shortterm (gt,m) and a long-term component (τm) and roots in the Component- and SplineGARCH models (Engle & Lee, 1999, Engle & Rangel, 2008).”
Does global economic uncertainty matter for the volatility and hedging effectiveness of Bitcoin? (2019, methodology): “The application of GARCH-MIDAS models shows that the long-term volatility of Bitcoin, MSCI, and S&P GSCI are significantly affected by EPU, although the effect on the long-run volatility of Bitcoin is different as compared to MCSI and PIMCO.”
Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, neutral): “The two models are the GARCH-MIDAS of Engle et al. (2013) and (a restricted version of) the MS-GARCH model of Haas et al. (2004).”
Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection (2020, methodology): “Previous studies find that aggregate financial volatility is countercyclical (Officer, 1973; Schwert, 1989; Engle et al., 2013; Conrad and Loch, 2015).”