Optimal Hedging Strategies for Natural Gas

This study examines the optimal hedge performance between natural gas market and crude oil, ECO, gold and US-bonds markets. To calculate optimal hedge ratios and hedging effectiveness, we apply several multivariate volatility models, namely CCC, DCC, cDCC and bayesDCC. The empirical results show that crude oil is the best asset to hedge natural gas followed by gold and ECO. This is a new result relative to the existing literature on natural gas prices. Additionally, we find that the bayesDCC model has the best performance on optimal hedge ratios (OHRs) calculation in terms of hedging effectiveness. Our findings will hold important financial risk management implications and asset portfolio for those invest in natural gas market.

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Using Calls And Puts As Risk Management Tools

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v3i1.2734 ◽

2011 ◽

Vol 3 (1) ◽

Author(s):

Ducksang Choi

Keyword(s):

Risk Management ◽

Natural Gas ◽

Electric Utility ◽

Font Size ◽

Management Tools ◽

Natural Gas Market ◽

Utility Company ◽

Strong Case ◽

Hedging Strategies ◽

Gas Market

A very strong case is presented for why the financial manager of an electric utility company should use hedging strategies to ensue a continuous supply of natural gas to his/her utility.  A combination of hedging strategies has been presented and tested for the natural gas market.  The combination of strategies works and works well while substantially reducing risk at the same time.

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How does the U.S. natural gas market react to demand and supply shocks in the crude oil market?

Energy Economics ◽

10.1016/j.eneco.2017.01.007 ◽

2017 ◽

Vol 63 ◽

pp. 66-74 ◽

Cited By ~ 33

Author(s):

Ali Jadidzadeh ◽

Apostolos Serletis

Keyword(s):

Natural Gas ◽

Crude Oil ◽

Demand And Supply ◽

Natural Gas Market ◽

Oil Market ◽

Supply Shocks ◽

Gas Market ◽

The U.S

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The Dependence and Risk Spillover Between Energy Market and BRICS Stock Markets: A Copula-MGARCH Model Approach

Global Business Review ◽

10.1177/09721509211049123 ◽

2021 ◽

pp. 097215092110491

Author(s):

Tarek Sadraoui ◽

Rym Regaieg ◽

Sabrine Abdelghani ◽

Wajdi Moussa ◽

Nidhal Mgadmi

Keyword(s):

At Risk ◽

Natural Gas ◽

Crude Oil ◽

Stock Markets ◽

Value At Risk ◽

Energy Market ◽

Market Information ◽

Natural Gas Market ◽

Oil Markets ◽

Gas Market

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.

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Cointegration and ARDL Specification Between the Dubai Crude Oil and the US Natural Gas Market

SSRN Electronic Journal ◽

10.2139/ssrn.3656214 ◽

2020 ◽

Author(s):

Stavros Stavroyiannis

Keyword(s):

Natural Gas ◽

Crude Oil ◽

Natural Gas Market ◽

The Us ◽

Gas Market

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Hedging Efficiency of Indian Commodity Futures

Paradigm ◽

10.1177/0971890717700529 ◽

2017 ◽

Vol 21 (1) ◽

pp. 1-20 ◽

Cited By ~ 4

Author(s):

Shashi Gupta ◽

Himanshu Choudhary ◽

D.R. Agarwal

Keyword(s):

Natural Gas ◽

Crude Oil ◽

Least Square ◽

Castor Seed ◽

Hedge Ratio ◽

Vector Autoregressive ◽

Vector Error Correction ◽

Hedging Effectiveness ◽

Gold Silver ◽

Hedge Ratios

This article examines the hedge ratio and hedging effectiveness in agricultural (castor seed, guar seed) and non-agricultural (copper, nickel, gold, silver, natural gas and crude oil) commodities traded in National Commodity and Derivative Exchange (NCDEX) and Multi Commodity Exchange (MCX), respectively. Constant and dynamic hedge ratios are estimated by using ordinary least square (OLS), vector autoregression (VAR), vector error correction model (VECM) and vector autoregressive-multivariate generalized autoregressive conditional heteroskedasticity model (VAR-MGARCH). The results of constant as well as dynamic hedge ratios reveal that the Indian futures market provides higher hedging effectiveness in case of precious metal (65–75 per cent) compared to industrial metal and energy commodities (less than 50 per cent). Hedging effectiveness for castor seed and natural gas is even lower than 10 per cent. This study concluded that VECM and VAR-MGARCH both are providing higher hedging although VECM is providing the highest hedge ratio. It has been found that the next to near month futures provide better hedging effectiveness as compared to near month futures for crude oil and silver. It is recommended that the policy makers should pay attention towards the number of delivery centres, standard of quality of underlying assets and transaction costs in spot market.

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