Oil financialization and volatility forecast: Evidence from multidimensional predictors

Abstract

Using the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.

A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm

“…Therefore, studies on the selection of influencing factors are mainly divided into single variables and multivariate variables. Due to the method characteristics, single-variable prediction mainly focuses on the traditional econometric model [20][21][22][23] and decomposition-integrated prediction model [24][25][26][27]. For example, Tso and Yau [28] examined the effects of socioeconomic indicators on the electricity demand.…”

Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation

“…Fourth, the related financial variables are mainly extracted from crude oil speculative attribute, global liquidity and U.S. dollar exchange rate. With the improvement of financialization of crude oil, speculation plays an increasingly important role in crude oil price fluctuation [16,48,49]. Specifically, in the selection of crude oil speculation indicators, we refer to Hamilton [14], Kilian and Murphy [16], and Wei et al [2], and use the ratio of OECD oil inventories to U.S. oil inventories recorded by the EIA to represent oil speculation.…”

New Challenge and Research Development in Global Energy Financialization

“…Energy financialization also provides new research ideas and directions for the study of price behavior, risk contagion mechanisms and risk management in the energy market. The price behavior characteristics of energy financialization in the energy market mainly manifest through four aspects: volatility (Ma, Ji, and Pan 2019b;Zavadska, Morales, and Coughlan 2018), uncertainty (Agbeyegbe 2015;Liu, Ji, and Fan 2017), complexity (Zhang, Ji, and Kutan 2019) and infectivity (Ji, Liu, and Fan 2019a;Mahadeo, Heinlein, and Legrenzi 2019).…”

“…The development of web data provides a channel that can rapidly link information factors, such as policy release, to the transmission of market price information, thus leading to the spread of market risk at a faster level and a broader scale (Ji and Guo 2015). The analysis of market uncertainty and market sentiment has become a new field of energy finance research (Ji, Li, and Sun 2019b;Ma, Ji, and Pan 2019b).…”