Abstract

The main purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices. The approach taken in the paper is different from others in the literature; the purpose is to examine the usefulness of modeling and testing volatility spillovers in the oil and financial markets. The paper investigates co-volatility spillovers (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-volatility in another physical or financial asset) between the oil and financial markets. The oil industry has four major regions, namely North Sea, the USA, Middle East, and South-East Asia. Associated with these regions are two major financial centers, namely the UK and the USA. For these reasons, the data to be used are the returns on alternative crude oil markets, returns on crude oil derivatives, specifically futures, and stock index returns in the UK and the USA. Given the importance of the Chinese financial and economic systems, the paper also analyzes Chinese financial markets, where the data are more recent. The USA and China are the world’s two largest economies and the UK is the world’s sixth largest economy (and second in the existing EU) behind the USA, China, Japan, Germany, and India. Moreover, the USA and the UK are associated with WTI and Brent oil, respectively. One of the purposes of the paper is to examine how China might be different from the USA and the UK, which seems to be borne out in the empirical analysis. Based on the conditional covariances to test the co-volatility spillovers, dynamic hedging strategies will be suggested to analyze market fluctuations in crude oil prices and associated financial markets.

Highlights

  • Crude oil is the most influential commodity in energy markets

  • The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices

  • The Quasi Maximum Likelihood Estimator (QMLE) of the parameters of the underlying univariate models are not presented as they form the first step in estimating the multivariate Diagonal BEKK model

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Summary

Introduction

Crude oil is the most influential commodity in energy markets. In industrialized nations, crude oil drives machinery, generates heat, fuels domestic and commercial vehicles, and allows commercial air travel for businesses and private travel and transportation for domestic and international tourists.Energies 2019, 12, 1475; doi:10.3390/en12081475 www.mdpi.com/journal/energiescrude oil components can produce almost all chemical products, such as plastics and detergents. Crude oil is the most influential commodity in energy markets. Crude oil drives machinery, generates heat, fuels domestic and commercial vehicles, and allows commercial air travel for businesses and private travel and transportation for domestic and international tourists. Crude oil components can produce almost all chemical products, such as plastics and detergents. Refined energy products, such as gasoline and diesel, are widely used in industry and commerce. Crude oil prices affect many industries simultaneously. Crude oil and its derivative products, such as options, futures, and forward prices, and associated index and volatility indexes, such as Exchange Traded Funds (ETF) and VIX, respectively, are traded widely in international markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices

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