Modeling and Forecasting

By M. Salsburg. Int. CMG Conference, 2007

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Exploiting the errors: A simple approach for improved volatility forecasting (2016, methodology): “All rights reserved. inherent in high-frequency data, Andersen et al. (2003) suggested the use of reduced form time series forecastingmodels for the daily so-called realized volatilities constructed from the summation of the squared high-frequency intraday returns.1 Set against this background, we…”
Forecasting the realized range-based volatility using dynamic model averaging approach (2017, methodology): “Overall, HAR-type models perform better than GARCH-class models in capturing the volatility dynamics and are more widely used in highfrequency data (see, e.g., Andersen et al., 2003; Hansen and Lunde, 2005; Koopman et al., 2005; Wei et al., 2010; Çelik and Ergin, 2014; Ma et al., 2015).”
Quantile forecasts of daily exchange rate returns from forecasts of realized volatility (2008, unknown): “This is standard practice in the volatility forecasting literature e.g., Giot and Laurent (2004), Andersen et al. (2003b), and Ghysels, Santa-Clara and Valkanov (2006), but less so more generally.”