The optimal hedge strategy of crude oil spot and futures markets: Evidence from a novel method

Zhao, Lu; Meng, Ya; Zhang, Yue‐Jun; Li, Yuntao

doi:10.1002/ijfe.1656

Cited by 17 publications

(6 citation statements)

References 66 publications

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“…Thereafter, the DCC specification is adopted by numerous investigations to depict the correlation between assets. The DCC model can be summarized by the following Equations (3)(4)(5)(6).…”

Section: Dcc-garch Modelmentioning

confidence: 99%

“…As a classic financial issue, using futures to hedge risks from the spot market has reached numerous achievements. For example, Chen et al (5) investigated the effectiveness of carbon futures in hedging the risks in the carbon spot market; Zhao et al (6) focused on the hedge strategies between crude oil spot and futures; Chan and Young (7) researched the copper futures and spot; Park and Switzer (8) estimated the optimal hedge ratio for stock and index futures. Benet (9) assessed the hedging effectiveness in the FX market and the like.…”

Section: Introductionmentioning

confidence: 99%

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Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

Zhang

Zhu

2021

Front. Public Health

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The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH) model and vector autoregression-Baba, Engle, Kraft, and Kroner-generalized autoregressive conditional heteroskedasticity (VAR-BEKK-GARCH) models and calculate the hedging effectiveness (HE) value to investigate the dynamic correlation and volatility spillover and assess the risk reduction of the Bitcoin futures to spot. The empirical results show that the Bitcoin spot and futures markets are highly connected; second, there exists a bi-directional volatility spillover between the spot and futures market; third, the HE value is equal to 0.6446, which indicates that Bitcoin futures can indeed hedge the risks in the Bitcoin spot market. Furthermore, we update the data to the post-Covid-19 period to do the robustness checks. The results do not change our conclusion that Bitcoin futures can hedge the risks in the Bitcoin spot market, and besides, the post-Covid-19 results indicate that the hedging ability of Bitcoin futures increased. Finally, we test whether the gold futures can be used as a Bitcoin spot market hedge, and we further control other cryptocurrencies to illustrate the hedging ability of the Bitcoin futures to the Bitcoin spot. Overall, the empirical results in this paper will surely benefit the related investors in the Bitcoin market.

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“…Thereafter, the DCC specification is adopted by numerous investigations to depict the correlation between assets. The DCC model can be summarized by the following Equations (3)(4)(5)(6).…”

Section: Dcc-garch Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

Zhang

Zhu

2021

Front. Public Health

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“…The commodity prices can vary widely in the free market, and producers must select the best marketing strategy for price risk management. Derivative instruments, such as options and futures, were developed to manage the price risk in the commodity markets, such as precious metals [2], energy commodities [3,4] and agricultural commodities [5,6]. Grain marketing strategies that use derivative contracts can assist grain producers in receiving the best possible prices for their crops, while reducing price risk [7].…”

Section: Introductionmentioning

confidence: 99%

Developing a Moving Average Crossover Strategy as an Alternative Hedging Strategy for the South Africa Maize Market

Monteiro¹,

Grové²,

Matthews³

2022

Agriculture

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Grain marketing is complex because important decisions are made on the timing of sales and the quantities sold at every trading activity. The literature suggest various grain-hedging strategies, however these strategies are not adaptable to changing market conditions or are difficult for a producer to implement. To address these limitations, our study developed tailor-made moving average crossover (MAC) strategies that are adaptable to changing market conditions and can be easily followed by risk neutral and risk averse grain producers. The study used daily closing prices for the white maize May futures contract for the period 2009/2010 to 2019/2020. An optimization model was solved using the evolutionary algorithm embedded in Excel® to identify the optimal MAC strategy that maximizes the margin above marketing cost for a risk aversion level. The results showed that optimal MAC strategies differ amongst producers with different levels of risk aversion. Furthermore, it was found that the risk-averse producers perform best by marketing their grain early in the marketing season. Meanwhile, the risk-neutral producers perform better by spreading their marketing activities throughout the season. The results further showed that the optimal MAC strategies performed better than the previously proposed routine strategies. The conclusion is therefore that an optimal MAC strategy outperforms routine strategies because of its ability to adapt to changing market conditions, while still being easy to implement.

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“…Therefore, the quantitative study on the contribution of global major crude oil futures to price discovery process can help to judge their impact on crude oil futures market, and then provide important reference information for crude oil price forecasting. Moreover, the reasonable measurement of risk transfer effect between major crude oil futures markets will not only help investors to optimise investment portfolios, but also play an important role in stabilising crude oil market and ensuring the safety of crude oil market (Zhang & Li, 2019; Zhao, Meng, Zhang, & Li, 2019), while the analysis of spillover between INE and international benchmark crude oil futures markets may furnish some reference for risk hedging and asset allocation of investors in the INE market (Zhang & Lin, 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring the dynamic price discovery, risk transfer and spillover among INE, WTI and Brent crude oil futures markets: Evidence from the high‐frequency data

Zhang

2020

Int. J Fin Econ

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In order to test whether Chinese crude oil futures (INE) has already played the role of futures market and whether it has had a significant impact on international benchmark market, we construct the permanent temporary model and Information Share model based on 15 min of high‐frequency trading data from March 26, 2018 to October 30, 2018 to inspect the proportions of new information in INE and Brent markets, and use the Garbade‐Silber model to measure the risk transfer effect. Furthermore, the generalised spillover index is proposed to examine the effects of return and volatility spillovers among INE, WTI and Brent futures markets. The results reveal that: firstly, during the sample period, INE is not yet a promoter of international benchmark crude oil prices, but more obvious followers. Secondly, although INE has begun to display the price discovery function, it is weaker than that of Brent, and the risk transfer function between them does not appear strong. Finally, INE market mainly acts as a net transmitter of return spillover before August 2018, but it has almost always been the net transmitter of volatility spillover during the full sample period. These findings are of interest to policy makers as well as investors for risk hedging and asset allocation of crude oil portfolios.

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The optimal hedge strategy of crude oil spot and futures markets: Evidence from a novel method

Cited by 17 publications

References 66 publications

Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?

Developing a Moving Average Crossover Strategy as an Alternative Hedging Strategy for the South Africa Maize Market

Exploring the dynamic price discovery, risk transfer and spillover among INE, WTI and Brent crude oil futures markets: Evidence from the high‐frequency data

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