Anticipating Long‐Term Stock Market Volatility

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Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models (2020, unknown): “As in Conrad and Loch (2015), we choose K = 36 for the monthly macroeconomic variables. The estimates for the parameters in the conditional variance are reported in Table 4. For all variables except housing starts, we find that a restricted Beta weighting scheme with w1 = 1 is the best choice; that ”
Long- and Short-Term Cryptocurrency Volatility Components: A GARCH-MIDAS Analysis (2018, methodology): “These measures have been shown to be important drivers of US stock market volatility in previous studies (see, among others, (Engle et al. 2013; Conrad and Loch 2015; and Conrad and Kleen 2018)).”
Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification (2016, unknown): “Conrad and Loch (2012) use the GARCH-MIDAS framework to decompose the stock returns into short-run and long-run components.”
Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection (2020, methodology): “Data revision can be substantial for macroeconomic & financial variables (Conrad and Loch, 2015).”