The predictive power of oil price shocks on realized volatility of oil: A note

Abstract

This paper examines the predictive power of oil supply, demand and risk shocks over the realized volatility of intraday oil returns. Utilizing the heterogeneous autoregressive realized volatility (HAR-RV) framework, we show that all shock terms on their own, and particularly financial market driven risk shocks, significantly improve the forecasting performance of the benchmark HAR-RV model, both in- and out-of-sample. Incorporating all three shocks simultaneously in the HAR-RV model yields the largest forecasting gains compared to all other variants of the HAR-RV model, consistently at short-, medium-, and long forecasting horizons. The findings highlight the predictive information captured by disentangled oil price shocks in accurately forecasting oil market volatility, offering a valuable opening for investors and corporations to monitor oil market volatility using information on traded assets at high frequency.

The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning

“…They suggested that policymakers and investors should consider volatility shocks through various swaps to construct a favorable hedging strategy decision toward market risk. Additionally, Demirer et al (2020) analyzed the forecasting power of oil demand, the oil supply, and risk-driven shocks across the realized volatility for high-frequency data. Thus, the authors used a Heterogeneous Autoregressive Realized Volatility (HAR-RV) approach introduced by Corsi (2009) and the framework established by Ready (2018) to fragment oil price shocks into three shock components: oil demand shocks, oil supply shocks, and risk shocks related to financial market risk.…”

“…Their results showed that all shock components, on their own, significantly produce extended forecasting completion of the HAR-RV process. Furthermore, Demirer et al (2020) provided crucial information for investors and market agents when controlling for oil market volatility. Other recent researchers have also examined the forecasting structure of the realized volatility for oil price returns by implementing the HAR-RV approach ( Prokopczuk et al, 2016 ; Degiannakis and Filis, 2017 ; Wen et al, 2019 ; Liu et al, 2018 ; Chen et al, 2019 ; Yang et al, 2019 ; Bonato et al, 2020 ; and Gkillas et al, 2020b ).…”

Oil market volatility: comparison of COVID-19 crisis with the SARS outbreak of 2002 and the global financial crisis of 2008

“…Similarly (Narayan, 2020) documented that COVID-19 infections have a significant impact on volatility and returns in oil market. Further, (Demirer et al, 2020) reported that significant predictive information over volatility of oil market is apprehended by shocks in financial risk even after supply and demand shocks in the market are taken as control variables. The result of this study also suggested that a discrete channel of risk transmission exists between commodity and financial markets.…”

Has the COVID-19 Pandemic Affected Maritime Connectivity? An Estimation for China and the Polar Silk Road Countries

“…Trade in fuels, on the contrary, has been severely affected by the pandemic, while oil prices have turned out to be far more perceptible to the slowdown of economic activities compared to mineral prices [138][139][140]. Global oil demand has been depressed with restrictions on travel and transport and industrial activity cuts across the world [1].…”