The predictive content of CBOE crude oil volatility index

Hongtao Chen, Li Liu and Xiaolei Li

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 837-850

Abstract: Volatility forecasting is an important issue in the area of econophysics. The information content of implied volatility for financial return volatility has been well documented in the literature but very few studies focus on oil volatility. In this paper, we show that the CBOE crude oil volatility index (OVX) has predictive ability for spot volatility of WTI and Brent oil returns, from both in-sample and out-of-sample perspectives. Including OVX-based implied volatility in GARCH-type volatility models can improve forecasting accuracy most of time. The predictability from OVX to spot volatility is also found for longer forecasting horizons of 5 days and 20 days. The simple GARCH(1,1) and fractionally integrated GARCH with OVX performs significantly better than the other OVX models and all 6 univariate GARCH-type models without OVX. Robustness test results suggest that OVX provides different information from as short-term interest rate.

Keywords: CBOE oil volatility index; GARCH-class models; Out-of-sample forecasting; MCS test (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (26)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:492:y:2018:i:c:p:837-850

DOI: 10.1016/j.physa.2017.11.014

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