Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model

Abstract

Oil continues to be a major source of world energy, but oil prices and funds have experienced high volatility over the last decade. This study applies the generalized autoregressive conditional heteroskedasticity-mixed-data sampling (GARCH-MIDAS) model on data spanning 1 July 2014 to 30 April 2020 to examine volatility transmission from the equity, bulk shipping, commodity, currency, and crude oil markets to the United States Oil Fund (USO) and BlackRock World Energy Fund A2 (BGF). By dividing the sample into two subsamples, we find a significant volatility transmission from the equity market to the oil ETF and energy fund both before and after the 2018 U.S.–China trade war. The volatility transmission from the bulk shipping, commodity, and crude oil markets turns significant for the oil ETF and energy fund after the 2018 U.S.–China trade war, extending into the COVID-19 pandemic in early 2020. The results suggest that investors can use the equity market to predict the movement of oil and energy funds during both tranquil and turmoil periods. Moreover, investors can use bulk shipping, commodity, and crude oil markets in addition to the equity market to forecast oil and energy funds’ volatility during the turmoil periods. This paper benefits investors against the high volatility of the energy funds.

Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product

“…Previous studies confirmed the MIDAS model's effectiveness in predicting GDP [16][17][18][19][20][21][22][23][24][25][26][27][28], and this analysis produced further evidence of that conclusion by considering consumption, investment and trade. In Table 3, it can seen that no matter whether consumption, investment or trade was an explanatory variable, the RMSE of the mixed-frequency model was smaller than that of the same-frequency one.…”

“…Since then, many researchers have used this model to analyse the stock market [12,13]. Although the MIDAS model is effective in analysing the volatility of the stock market, since Engle et al [14] proposed the generalised autoregressive conditional heteroscedasticity MIDAS (GARCH-MIDAS) model and Colacito et al [15] developed the dynamic-condition-associated MIDAS (DCC-MIDAS) model, researchers have been more inclined to use these two methods to analyse the stock market [16][17][18][19][20][21][22].…”

Forecasting Crude Oil Risk Using a Multivariate Multiscale Convolutional Neural Network Model

“…Neurons in the pooling layers perform the pooling operation such as the maximum pooling and minimum pooling on the input data. The pooling function is shown in Equation (2).…”

“…With the deregulation wave spreading across the crude oil markets around the world and the widespread usage of energy derivatives, we have witnessed closer interaction and linkage between the crude oil markets and the global financial markets. Numerous empirical studies have reported the intensifying financialization of the crude oil markets, and the complicated relationship between crude oil markets, the stock market, and the exchange market [1,2]. For example, Ji [3] has discovered that before the 2008 global financial crisis, the crude oil markets were affected by the speculation factors in the short term and the fundamental factors in the long term.…”

Extreme risk measurement for the oil and China’s sectors system—network-based approach and machine learning methods

“…As an indispensable strategic energy source in today's world, oil is an important support for the stable operation and sustainable development of the national economy [1][2][3]. The oil market is subject to fluctuations resulting from a variety of factors, including international politics, economics and supply and demand.…”