Forecasting oil price realized volatility using information channels from other asset classes
By Stavros Degiannakis *, George Filis. 2017
262 citations recorded by the source corpus.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/c3a466673549b44ba86430eae3c0153a63bf1eda
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In Economics and Econometrics
- Stavros Degiannakis *
- George Filis
- Vipin Arora
- Hossein Hassani
- Feng Ma
- Yaojie Zhang
- Yin Liao
- Yang Cao
Citations in the corpus
- Forecasting global stock market implied volatility indices (2018, methodology): “…to go long (short) in a straddle when the forecasted implied volatility at time t+s is higher (lower) than the actual implied volatility index at the present time t. Similar approaches have been employed by Degiannakis and Filis (2017), Andrada-Felix et al. (2016), Angelidis and Degiannakis (2008).”
- Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, background): “More recently, Degiannakis and Filis (2017) show that the incorporation of stock market index volatility from the major global stock market indices (E-mini S&P500, FTSE100, Eurostoxx 50 and Hang Seng) does improve the forecasting and directional accuracy of Brent crude oil volatil - ity compared to…”
- Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models (2019, background): “The application of HAR-type models on agricultural commodities to forecast volatility is much less focused on than on other commodities such as crude oil (Souˇcek & Todorova, 2013, S´evi, 2014, Wen et al., 2016, Degiannakis & Filis, 2017), precious metals (Caporin et al., 2015, Ly´ocsa & Moln´ar, 20”
- Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019, methodology): “First, it connects with the growing literature that uses intraday data to improve volatility forecasting for the crude oil market (see, for example, Haugom et al., 2014; Sévi, 2014; Prokopczuk et al., 2016; Phan et al., 2016; Ma et al., 2018; Degiannakis and Filis, 2017).”