High frequency volatility of oil futures in China: Components, modeling, and prediction

Abstract

AbstractThis paper investigates the high‐frequency volatility modeling and prediction for crude oil futures in China, a new asset class emerging in recent years. Two volatility measures, the realized variance () and realized bi‐power variations () are constructed at various frequencies by virtue of 1‐minute crude oil futures prices. The distinctive components of these volatility estimators are further identified to exploit the information contents in the in‐sample explanatory power of the realized variance dynamics and the out‐of‐sample prediction of realized variance across different horizons, leading to four new HAR‐RV‐type models. First, the empirical results show that the continuous component of the weekly realized variance, representing investors' trading behavior in the medium‐term, is the dominant factor driving up volatility trends in China's crude oil futures market over a range of market conditions. Second, the monthly jump component in realized variance presents the significant in‐sample explanatory power, and yet marginally improves prediction performance in realized variance during the two out‐of‐sample periods. Finally, these results are robust toward various market/model setups, over day‐ and night‐trading hours, and across a range of prediction horizons and relative to prediction benchmarks.