Exploiting the errors: A simple approach for improved volatility forecasting

407 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models (2019, positive): “Based on Fleming & Hern´andez-Hern´andez (2003), Bollerslev et al. (2016), and Callot et al. (2017), we use the quadratic utility with risk aversion to determine the economic values of various forecast models: In this paper, we consider two levels of risk aversion rates for the investor, the mild…”
Forecasting the realized range-based volatility using dynamic model averaging approach (2017, methodology): “This model is popularly employed in forecasting volatility and shows its outstanding performance in capturing "stylized facts" in financial market, such as long memory and multi-scale behavior of volatility (Andersen et al., 2007; Corsi et al., 2010; Bekaert and Hoerova, 2014; Duong and Swanson, 201”
Forecasting the volatility of Bitcoin: The importance of jumps and structural breaks (2020, positive): “Bollerslev et al. (2016) also observe that there exists substantial estimation bias in daily , while the attenuation bias is much less severe in the weekly and monthly ones.”