We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short run from secular movements. We combine insights from Engle and Rangel (2007) and the recent work on mixed data sampling (MIDAS), as in e.g. Ghysels,...

We revisit the relation between stock market volatility and macroeconomic activity using a new class of component models that distinguish short run from secular movements. We combine insights from Engle and Rangel (2007) and the recent work on mixed data sampling (MIDAS), as in e.g. Ghysels,...

We estimate MIDAS regressions with various (bi)power variations to predict future volatility - measured via increments in quadratic variation. Instead of pre-determining the (bi)power variation we parameterize it and estimate the intra-daily return power transformation that optimally predicts...

We estimate MIDAS regressions with various (bi)power variations to predict future volatility measured via increments in quadratic variation. Instead of pre-determining the (bi)power variation we parameterize it and estimate the intra-daily return power transformation that optimally predicts...

We propose a model of dynamic correlations with a short- and long-run component specification, by extending the idea of component models for volatility. We call this class of models DCC-MIDAS. The key ingredients are the Engle (2002) DCC model, the Engle and Lee (1999) component GARCH model...