Forecasting Volatility of the U.S. Oil Market

By Erik Haugom, H. Langeland, Péter Molnár, Sjur Westgaard. 2014

211 citations recorded by the source corpus.

We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when forecasting realized volatility in the WTI futures market. Additionally, we study whether other market variables, such as volume, open interest, daily returns, bid-ask spread and the slope of the futures curve, contain predictive power beyond what is embedded in the implied volatility. In out-of-sample forecasting we find that econometric models based on realized volatility can be improved by including implied volatility and other variables. Our results show that including implied volatility significantly improves daily and weekly volatility forecasts; however, including other market variables significantly improves daily, weekly and monthly volatility forecasts.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/dd0e697769bb4458c8055fcf5d848d05b27b1a0d

How researchers see this paper

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

In Economics and Econometrics

1. Stavros Degiannakis *
2. George Filis
3. Feng Ma
4. M. Wahab
5. Dengshi Huang
6. Weiju Xu
7. Yu Wei
8. Vipin Arora

In Finance

1. George Filis
2. Stavros Degiannakis *
3. Feng Ma
4. M. Wahab
5. Weiju Xu
6. Dengshi Huang
7. R. Brooks

Citations in the corpus

• Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices (2017, unknown): “Haugom et al. (2014) provide evidence that daily and weekly volatility forecasting can significantly improve if information from the implied volatility index for crude oil is included in the forecasting model.”
• Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “As in Haugom et al. (2014) and Sévi (2014), Prokopczuk et al. (2015) also use a HAR model for their forecasting exercise.”
• Forecasting the realized volatility of the oil futures market: A regime switching approach (2017, background): “For example, Degiannakis and George (2016) point out that studies, such as Haugom et al. (2014), Sévi (2014), Prokopczuk et al. (2016), and Wen et al. (2016), try to forecast oil price volatility using ultra-high frequency data.”