Dynamic frequency relationships and volatility spillovers in natural gas, crude oil, gas oil, gasoline, and heating oil markets: Implications for portfolio management

2021 ◽  
Vol 73 ◽  
pp. 102172
Author(s):  
Walid Mensi ◽  
Mobeen Ur Rehman ◽  
Xuan Vinh Vo
2012 ◽  
Vol 28 (6) ◽  
pp. 1237 ◽  
Author(s):  
Bernard Ben Sita ◽  
Salah Abosedra

<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none;" class="MsoNormal"><span lang="EN-CA" style="color: black; font-size: 10pt; mso-themecolor: text1; mso-ansi-language: EN-CA;"><span style="font-family: Times New Roman;">This paper provides evidence on the lead, the contemporaneous and the lagged transmission mechanism of extreme shocks across energy products. Our findings reveal a weak leadership of crude oil in guiding hedgers against risk that is specific to natural gas whose changes show a weak reliance on changes in crude oil. Moreover, our findings are consistent with the competitive use of energy products. It follows that substitutability characterizes the relationship between heating oil and natural gas when extreme standardized shocks are considered.<span style="mso-spacerun: yes;"> </span></span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>


2019 ◽  
Vol 36 (4) ◽  
pp. 682-699 ◽  
Author(s):  
Ikhlaas Gurrib

Purpose The purpose of this paper is to shed fresh light into whether an energy commodity price index (ENFX) and energy blockchain-based crypto price index (ENCX) can be used to predict movements in the energy commodity and energy crypto market. Design/methodology/approach Using principal component analysis over daily data of crude oil, heating oil, natural gas and energy based cryptos, the ENFX and ENCX indices are constructed, where ENFX (ENCX) represents 94% (88%) of variability in energy commodity (energy crypto) prices. Findings Natural gas price movements were better explained by ENCX, and shared positive (negative) correlations with cryptos (crude oil and heating oil). Using a vector autoregressive model (VAR), while the 1-day lagged ENCX (ENFX) was significant in estimating current ENCX (ENFX) values, only lagged ENCX was significant in estimating current ENFX. Granger causality tests confirmed the two markets do not granger cause each other. One standard deviation shock in ENFX had a negative effect on ENCX. Weak forecasting results of the VAR model, support the two markets are not robust forecasters of each other. Robustness wise, the VAR model ranked lower than an autoregressive model, but higher than a random walk model. Research limitations/implications Significant structural breaks at distinct dates in the two markets reinforce that the two markets do not help to predict each other. The findings are limited by the existence of bubbles (December 2017-January 2018) which were witnessed in energy blockchain-based crypto markets and natural gas, but not in crude oil and heating oil. Originality/value As per the authors’ knowledge, this is the first paper to analyze the relationship between leading energy commodities and energy blockchain-based crypto markets.


2021 ◽  
pp. 097215092110491
Author(s):  
Tarek Sadraoui ◽  
Rym Regaieg ◽  
Sabrine Abdelghani ◽  
Wajdi Moussa ◽  
Nidhal Mgadmi

The article examines the dynamic dependence structure and risk spillover between the future market of energy commodities and Brazil, Russia, India, China and South Africa (BRICS) stock markets for different market conditions. The study used copula-based multivariate GARCH model, or in short C-MGARCH model, to explore the conditional correlation by multivariate generalized autoregressive conditional heteroskedastic (MGARCH) and the remaining dependence by different copula models. Our results provide significant positive dynamic dependency among crude oil markets (natural gas market) and BRICS stock markets. We then explore the financial implications of volatility spillovers regarding portfolio risk management through an analysis of risk spillovers from energy market to BRICS countries using the value at Risk (VaR), conditional value at risk (CVaR) and delta CVaR. Our findings support the existence of significant risk spillover between crude oil markets (natural gas market) and BRICS stock markets. The presence of volatility spillover among oil prices, natural gas prices and BRICS stock market implies that oil market information (natural gas market information) enhances the volatility forecast in stock markets. Consequently, investors must take oil markets and natural gas markets into account at the time of financial portfolios structuring and in improving their hedging strategies.


Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1475 ◽  
Author(s):  
Chang ◽  
McAleer ◽  
Tian

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.


Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2757 ◽  
Author(s):  
Theodosios Perifanis ◽  
Athanasios Dagoumas

The paper examines both the time-varying price and volatility transmission between US natural gas and crude oil wholesale markets, over the period 1990–2017. Short iterations suggest that neither commodity determines other’s returns, but sub-periods with very short-lived causal relationships exist. It can be asserted that the markets are decoupled, where unconventional production further enhances the already established commodities’ independence. Using Momentum Threshold Autoregressive (MTAR) cointegration methodology, we find evidence of positive asymmetry from crude oil to natural gas prices, i.e., oil price increases cause faster adjustments to natural gas prices than decreases. We also find that an 1% change of oil price has positive and significantly larger long-term impact (between 0.01% to 0.02%) to the gas price, compared to the negligible impact of gas to oil. Volatility transmission is examined using the Dynamic Conditional Covariance (DCC)-Generalized Autoregressive Conditional Heteroscedasticity (GARCH) methodology, presenting their time-varying correlation. Results show that both commodities influence each other’s volatility at the aggregate level. Finally, we conclude that both regional commodity markets are liquid and integrated, where the market fundamentals drive their price formulation. However, although markets are decoupled and not appropriate for perfect hedging of each other, the existence of bidirectional volatility transmission and their substitutability might be useful for diversified portfolio allocation.


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