Forecasting Volatility with MIDAS

By Eric Ghysels, Rossen Valkanov. 2012

55 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ddfc3867184d970f06ff49e2f917a7345b4a0a25

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Citations in the corpus

• Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019, methodology): “We follow the convention to set k max =403, and follow Ghysels et al. (2002, 2006, 2007), Ghysels and Valkanov (2012), and Forsberg and Ghysels (2007) to set up the weight function kw using the following Beta function: 3 The value of 40 is determined by grid search.”
• Testing for Granger Causality in Large Mixed-Frequency VARs (2016, unknown): “Indeed, the difference between HAR and MIDAS models can be small (Ghysels and Valkanov, 2012).”
• A MIDAS Approach to Modeling First and Second Moment Dynamics (2016, unknown): “…Clements 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.…”