Degiannakis, Stavros and Filis, George (2016): Forecasting oil price realized volatility: A new approach.

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Abstract
This paper adds to the extremely limited strand of the literature focusing on the oil price realized volatility forecasting. More specifically, we evaluate the information content of four different asset classes’ volatilities when forecasting the oil price realized volatility for 1day until 66day ahead. To do so, we concentrate on the Brent crude oil and fourteen other assets, which are grouped into four different asset classes, based on Heterogeneous AutoRegressive (HAR) framework. Our outofsample forecasting results can be summarised as follows. (i) The use of exogenous volatilities statistically significant improves the forecasting accuracy at all forecasting horizons. (ii) The HAR model that combines volatilities from multiple asset classes is the best performing model. (iii) The Direction of Change suggests that all HAR models are highly accurate in predicting future movements of oil price volatility. (iv) The forecasting accuracy of the models is better gauged using the Median Absolute Error and the Median Squared Error. (v) The findings are robust even during turbulent economic periods. Hence, different asset classes’ volatilities contain important information which can be used to improve the forecasting accuracy of oil price volatility.
Item Type:  MPRA Paper 

Original Title:  Forecasting oil price realized volatility: A new approach 
Language:  English 
Keywords:  Volatility forecasting, realized volatility, crude oil futures, Brent crude oil, HAR, MCS. 
Subjects:  C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods G  Financial Economics > G1  General Financial Markets > G13  Contingent Pricing ; Futures Pricing Q  Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q0  General > Q02  Commodity Markets Q  Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4  Energy > Q47  Energy Forecasting 
Item ID:  69105 
Depositing User:  George Filis 
Date Deposited:  30 Jan 2016 10:41 
Last Modified:  27 Sep 2019 10:01 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/69105 