Journal articles: 'Oil volatility transmission'

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Park, Jaehwan. "Volatility Transmission between Oil and LME Futures." Applied Economics and Finance 5, no. 2 (January 21, 2018): 65. http://dx.doi.org/10.11114/aef.v5i2.2944.

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This paper investigates the volatility transmission between oil and base metals to assess the possibility of hedge strategy across commodity markets. In order to identify the volatility linkage of oil to the base metals, the bivariate GARCH model is applied using daily returns data period over 2000-2016. It is found that evidence of volatility transmission between oil and base metals is somewhat strong with a 1% significant level. This result suggests the investment idea of commodity hedging strategy of cross-market is important.

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Lin, Arthur J., and Hai-Yen Chang. "Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model." Mathematics 8, no. 9 (September 8, 2020): 1534. http://dx.doi.org/10.3390/math8091534.

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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.

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Darinda, Dwika, and Fikri C. Permana. "Volatility Spillover Effects In Asean-5 Stock Market: Does The Different Oil Price Era Change The Pattern?" Kajian Ekonomi dan Keuangan 3, no. 2 (August 31, 2019): 116–34. http://dx.doi.org/10.31685/kek.v3i2.484.

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The aim of this study is to identify the pattern of volatility transmission in ASEAN-5 (Indonesia, Malaysia, Thailand, Singapore and the Philippines) stock market by examine Global Macro Shocks (proxyed by Brent oil price); Cross-Market Linkages (proxied by Dow Jones Index); and Economic Fundamental (proxied by exchange rate) as the sources of volatility. This paper utilizing VAR and asymmetric GARCH (1,1)-BEKK model using the daily data between 4 January 2012 and 30 June 2017. The result shows that all independent variables have a significant volatility transmission to every ASEAN-5 stock market. Then in order to capture the different volatility transmission pattern, we divided the data into two periods which are “high-oil price” era and “low-oil price” era. Besides the different rate of volatility, we also find a different pattern of volatility transmission at Malaysia stock market (KLCI); Thailand stock market (SETI); and at Philippines stock market (PSEI) between these two eras.

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Jin, Xiaoye, Sharon Xiaowen Lin, and Michael Tamvakis. "Volatility transmission and volatility impulse response functions in crude oil markets." Energy Economics 34, no. 6 (November 2012): 2125–34. http://dx.doi.org/10.1016/j.eneco.2012.03.003.

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Siami-Namini, Sima. "Volatility Transmission Among Oil Price, Exchange Rate and Agricultural Commodities Prices." Applied Economics and Finance 6, no. 4 (June 10, 2019): 41. http://dx.doi.org/10.11114/aef.v6i4.4322.

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The aim of this article is to examine the interdependence relationship among the volatilities of crude oil price, U.S. dollar exchange rate, and a set of agricultural commodities prices. An autoregressive (AR) with an exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model or AR-EGARCH process and vector error correction model (VECM) approach was used on monthly data spanning from Jan 1986 to Dec 2005 as the pre-crisis period and from Jan 2006 to Nov 2015 as the post-crisis period. The results show that volatility in the agricultural commodity returns for most cases are affected by the volatility of the crude oil returns in the post-crisis period. Also, the volatility of the U.S. dollar exchange rate highly affects the agricultural commodities returns in the pre-crisis than the post-crisis periods. Furthermore, crude oil returns volatility does affect the U.S. dollar exchange rate volatility in the post-crisis period, which in turn affects the volatility of the agricultural commodities returns through changes in prices. The results of impulse response function (IRFs) are significant for most agricultural commodities volatility in the post-crisis period than the pre-crisis period.

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Abdelhedi, Mouna, and Mouna Boujelbène-Abbes. "Transmission of shocks between Chinese financial market and oil market." International Journal of Emerging Markets 15, no. 2 (August 27, 2019): 262–86. http://dx.doi.org/10.1108/ijoem-07-2017-0244.

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Purpose The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period. Design/methodology/approach This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model. Findings The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets. Originality/value This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

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Perifanis, Theodosios, and Athanasios Dagoumas. "Price and Volatility Spillovers Between the US Crude Oil and Natural Gas Wholesale Markets." Energies 11, no. 10 (October 15, 2018): 2757. http://dx.doi.org/10.3390/en11102757.

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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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Kumar, Dilip. "On Volatility Transmission from Crude Oil to Agricultural Commodities." Theoretical Economics Letters 07, no. 02 (2017): 87–101. http://dx.doi.org/10.4236/tel.2017.72009.

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Ewing, Bradley T., Farooq Malik, and Ozkan Ozfidan. "Volatility transmission in the oil and natural gas markets." Energy Economics 24, no. 6 (November 2002): 525–38. http://dx.doi.org/10.1016/s0140-9883(02)00060-9.

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Kang, Sang Hoon, Chongcheul Cheong, and Seong-Min Yoon. "Structural changes and volatility transmission in crude oil markets." Physica A: Statistical Mechanics and its Applications 390, no. 23-24 (November 2011): 4317–24. http://dx.doi.org/10.1016/j.physa.2011.06.056.

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Malik, Farooq, and Bradley T. Ewing. "Volatility transmission between oil prices and equity sector returns." International Review of Financial Analysis 18, no. 3 (June 2009): 95–100. http://dx.doi.org/10.1016/j.irfa.2009.03.003.

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Guesmi, Khaled, and Salma Fattoum. "Return and volatility transmission between oil prices and oil-exporting and oil-importing countries." Economic Modelling 38 (February 2014): 305–10. http://dx.doi.org/10.1016/j.econmod.2014.01.022.

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Yousaf, Imran, Shoaib Ali, Muhammad Naveed, and Ifraz Adeel. "Risk and Return Transmissions From Crude Oil to Latin American Stock Markets During the Crisis: Portfolio Implications." SAGE Open 11, no. 2 (April 2021): 215824402110138. http://dx.doi.org/10.1177/21582440211013800.

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Using the DCC-GARCH model, this study examines the return and volatility spillovers between crude oil and emerging Latin American stock markets during the entire studying period and two subsamples, including the global financial crisis and the Chinese Stock market crash. The findings reveal a positive causal effect from Brazil and Mexico’s stock price changes to the oil market during the global financial crisis. During the Chinese stock market crash, the return spillover is unidirectional from the oil to Brazil and Mexico equity markets. The findings show no significant volatility transmission between oil and Latin American stock markets during the global financial crisis. Contrarily, we observe bidirectional volatility transmission between the oil and Brazilian stock markets during the Chinese stock market crash. Finally, we calculate the optimal weights and hedge ratios for the oil and stock portfolios. In comparison to the global financial crisis, the results suggest that lesser oil assets are required to minimize portfolio risk in the Chinese stock market crash. These results offer valuable insights for portfolio diversification, asset pricing, and risk management.

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Gormus, Alper, John David Diltz, and Ugur Soytas. "Energy mutual funds and oil prices." Managerial Finance 44, no. 3 (March 12, 2018): 374–88. http://dx.doi.org/10.1108/mf-04-2017-0124.

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Purpose The purpose of this paper is to examine the price level and volatility impacts of oil prices on energy mutual funds (EMFs). The authors also examine specific fund characteristics which might influence those interactions. Design/methodology/approach The authors test for volatility transmission between the oil prices and the funds in the sample. Later, the authors test to see which fund characteristics impact these volatility interactions. Findings The results show oil price movements lead majority of sample EMFs. The authors also find a volatility feedback relationship with most of the sample. Furthermore, the authors show the fund characteristics to be important indicators of these interactions. Morningstar rating, market capitalization and management tenure are found to be significant drivers of the relationships between EMFs and oil prices. Originality/value To the knowledge, there is not a study in literature which examines these relationships.

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Bouri, Elie I. "Do Fine Wines Blend with Crude Oil? Seizing the Transmission of Mean and Volatility Between Two Commodity Prices." Journal of Wine Economics 8, no. 1 (May 2013): 49–68. http://dx.doi.org/10.1017/jwe.2013.6.

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AbstractThis study applies a multivariate model to examine the dynamics of mean and volatility transmission between fine wine and crude oil prices using daily observations from January 2004 to December 2011. The results suggest that the crude oil mean determines the wine market. In each series, volatility persistence is high and significant; innovations in each market seem to include figures that are valuable to risk managers seeking to predict volatility in other markets. During the financial crisis of 2008, wine and oil conditional volatilities climbed but then returned to their overall pre-crisis levels. (JEL Classifications: G11, G15, Q14, Q40)

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Muhammad, Sagheer, Adnan Akhtar, and Nasir Sultan. "Shock Dependence and Volatility Transmission Between Crude Oil and Stock Markets: Evidence from Pakistan." Lahore Journal of Business 5, no. 1 (September 1, 2016): 1–14. http://dx.doi.org/10.35536/ljb.2016.v5.i1.a1.

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This paper investigates shock dependence and volatility transmission between the crude oil and equity markets, based on crude oil returns and stock index returns for the period 2 January 2009 to 27 January 2014. We employ the bivariate BEKK-GARCH (1, 1) model developed by Engle and Kroner (1995) as well as the Engle and Granger (1987) cointegration and unit root tests. These parameterization tools are more flexible and innovative than other specifications, which often give counter-intuitive results. The results of the cointegration test reject the notion of a long-run relationship between the crude oil market and stock market. The results of the BEKK-GARCH model suggest that shocks and volatility created in the oil market have a significant effect on the Pakistan Stock Exchange. They also reveal bidirectional shock persistence and a unidirectional volatility spillover between crude oil prices and Pakistani equity prices. These empirical findings can help predict price movements in each market efficiently. The empirical results are also important for policymakers involved in shock prevention and for portfolio managers seeking optimal portfolio allocation.

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Noor, Md Hasib, and Anupam Dutta. "On the relationship between oil and equity markets: evidence from South Asia." International Journal of Managerial Finance 13, no. 3 (June 5, 2017): 287–303. http://dx.doi.org/10.1108/ijmf-04-2016-0064.

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Purpose The purpose of this paper is to investigate the volatility linkage between global oil market and major South Asian equity markets. Design/methodology/approach In order to serve the purpose, the authors employ a recently developed vector autoregressive-generalized autoregressive conditional heteroskedastic model to examine whether shocks and volatility spill over from the oil market to various equity markets under consideration. Findings The findings of the empirical analysis suggest that all the markets studied do receive volatility from the oil market. Not surprisingly, the authors do not find any significant evidence of volatility transmission from the equity markets to the global oil market. Additionally, while computing the optimal portfolio weights and hedge ratios, the authors document that inclusion of oil in the portfolio of stocks tends to reduce the risk of the resultant portfolio. Originality/value Fully original.

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Qabhobho, Thobekile, Emmanuel Asafo-Adjei, Peterson Owusu Junior, and Anokye M. Adam. "Quantifying information transfer between Commodities and Implied Volatilities in the Energy Markets: A Multi-frequency Approach." International Journal of Energy Economics and Policy 12, no. 5 (September 27, 2022): 472–81. http://dx.doi.org/10.32479/ijeep.13403.

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We investigate the multi-scale information transmission between two implied volatilities in the energy markets (crude oil volatility and volatility in the energy market) and energy commodities returns (global energy commodity, brent, heating oil, natural gas and petroleum). The Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) based Rényi transfer entropy approach is employed to accomplish the research objective. The study’s outcome underscores that information flow between implied volatilities and energy commodities is negative with significance being scale-dependent. Especially, significant negative information flow is found at specific intrinsic mode functions (IMFs) such as IMF1, and from IMFs 6-9 suggesting short-, upper medium and long-term energy markets dynamics. Comparatively, we find profound negative information flow with the crude oil implied volatility than the volatility in the entire energy market implying the former’s strong hedging benefits. Investors and policymakers should have knowledge about the dynamics of implied volatilities, particularly, the crude oil implied volatility when designing strategies for the energy commodities markets.

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Kumar, Ashish, and Dilip Kumar. "Return and Volatility Transmission between Crude Oil and Agricultural Commodities." International Review of Business Research Papers 12, no. 2 (September 2016): 141–51. http://dx.doi.org/10.21102/irbrp.2016.09.122.09.

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Ewing, Bradley T., and Farooq Malik. "Volatility transmission between gold and oil futures under structural breaks." International Review of Economics & Finance 25 (January 2013): 113–21. http://dx.doi.org/10.1016/j.iref.2012.06.008.

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Malhotra, Meenakshi, and Dinesh Kumar Sharma. "Volatility Dynamics in Oil and Oilseeds Spot and Futures Market in India." Vikalpa: The Journal for Decision Makers 41, no. 2 (May 31, 2016): 132–48. http://dx.doi.org/10.1177/0256090916642686.

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Executive Summary India occupies the fifth position in the vegetable oil economy of the world. The demand for oilseeds and vegetable oil has far exceeded the domestic output necessitating huge imports. Futures market helps to bring price stability for the development of the underlying physical market. The present study investigates the volatility dynamics in spot and futures markets of select oil and oilseeds commodities. The objectives of this article are to study (a) the information transmission process between spot and futures markets, also called volatility spillover and (b) the impact of futures trading activity on the volatility of physical market prices. The commodities selected from oil and oilseeds segment are refined soya oil, mustard seed, crude palm oil, and mentha oil. The study uses basic Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to capture volatility in prices of the selected commodities. Bivariate GARCH model makes use of information in the history of two different markets for testing volatility spillover between two markets of the same underlying commodity. The relationship between futures trading activity and spot price volatility is investigated for examining the impact of futures trading activity on the volatility of underlying spot market. Two variables, viz., futures trading volume and open interest are decomposed into expected and unexpected components and are taken as a proxy for the level of trading activity. The contemporaneous and dynamic relationships are studied with the help of augmented GARCH model and Granger causality, respectively. It is observed that there is an efficient transmission of information between spot and futures markets but it is the spot market which leads to the flow of information to futures and hence causes greater spillover of volatility. The spot market has a greater impact on the volatility of futures market, indicating that informational efficiency of oilseeds spot market is stronger than that of the futures market. The contemporaneous and dynamic relationship between spot price volatility and futures trading activity tested with econometric models provide evidence of the destabilizing impact of an unexpected increase in futures trading activity (volume or open interest) on the spot price volatility in three out of four commodities studied. This indicates that badly informed traders present in futures market are destabilizing the underlying spot market by inducing noise and lowering the information content of prices.

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Naeem, Muhammad Abubakr, Saqib Farid, Safwan Mohd Nor, and Syed Jawad Hussain Shahzad. "Spillover and Drivers of Uncertainty among Oil and Commodity Markets." Mathematics 9, no. 4 (February 23, 2021): 441. http://dx.doi.org/10.3390/math9040441.

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The paper aims to examine the spillover of uncertainty among commodity markets using Diebold–Yilmaz approach based on forecast error variance decomposition. Next, causal impact of global factors as drivers of uncertainty transmission between oil and other commodity markets is analyzed. Our analysis suggests that oil is a net transmitter to other commodity uncertainties, and this transmission significantly increased during the global financial crisis of 2008–2009. The use of linear and nonlinear causality tests indicates that the global factors have a causal effect on the overall connectedness, and especially on the spillovers from oil to other commodity uncertainties. Further segregation of transmissions between oil to individual commodity markets indicates that stock market implied volatility, risk spread, and economic policy uncertainty are the influential drivers of connectedness among commodity markets.

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Jihed Majdoub, Jihed Majdoub. "Volatility Spillover among Equity Indices and Crude Oil Prices: Evidence from Islamic Markets." journal of king Abdulaziz University Islamic Economics 31, no. 1 (January 2, 2018): 27–45. http://dx.doi.org/10.4197/islec.31-1.2.

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This paper studies the volatility spillover between Islamic equity markets and oil prices. We use a sample of five countries from the Gulf region. The results show that there is a reduction in the volatility spillover, particularly for the Saudi market. This can be interpreted, in our opinion, in terms of the distinguishing features of the Islamic financial intermediation mode, which is more able to alleviate the transmission of shocks to domestic markets and ensure a better stability to financial markets. The new structure of volatility spillover has important implications for international investors with respect to portfolio diversification benefits and for financial policymakers regarding contagion risks and portfolio allocative policies. Keywords: Volatility spillover, GCC, Oil price, Islamic finance.

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Saadaoui, Amir, Kais Saidi, and Mohamed Kriaa. "Transmission of shocks between bond and oil markets." Managerial Finance 46, no. 10 (May 26, 2020): 1231–46. http://dx.doi.org/10.1108/mf-11-2019-0554.

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PurposeThis paper aims at looking into the transmission of shocks between bond and oil markets using a bivariate GARCH (BEKK and DCC) model. As lots of financial assets have been exchanged due to these index returns, it is essential for financial market participants to figure out the mechanism of volatility transmission through time and via these series for the purpose of taking optimal decisions of portfolio allocation. The outcomes drawn reveal an important volatility transmission between sovereign bond and oil indices, with great sensitivity during and after the subprime crisis period.Design/methodology/approachIn this context, we propose our hypotheses. Indeed, our study aims to see whether the financial crisis has been responsible for the sharp drop in oil prices since October 2008. To this end, we suggest, in this paper, the empirical study of the shock transmission between the bond and oil markets, using BEK-GARCH and DCC models. To our knowledge, this is the first document using the BEKK-GARCH and the DCC models in studying the shock transmission between a sovereign bond and oil indices.FindingsWe have noticed that in the event of a disruption in the bond market, oil prices respond to these shocks in the short term. It has also been emphasized, however, that this relationship has exacerbated if the period has extended. This makes us conclude that the financial market situation affects the oil price only throughout the crisis period; and that this situation is causally significant only in the event of a severe crisis, such as those of subprime and sovereign debt.Originality/valueThe global financial system has been going through an acute crisis since mid-2007. This crisis, initially occurred only in the US real estate market, progressively affects the global financial system, and is now becoming a general economic crisis. The objective of this work is to analyze the effects of the current financial market disturbance on oil prices based on econometric models in order to promote the proper functioning of this study.

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Fowowe, Babajide. "Return and volatility spillovers between oil and stock markets in South Africa and Nigeria." African Journal of Economic and Management Studies 8, no. 4 (December 4, 2017): 484–97. http://dx.doi.org/10.1108/ajems-03-2017-0047.

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Purpose The purpose of this paper is to empirically examine return and volatility spillovers between oil and the stock markets of Nigeria and South Africa. Design/methodology/approach The authors make use of an innovative new methodology of capturing spillovers, which is different from what many existing studies use. The authors employ the measures of return spillovers and volatility spillovers of Diebold and Yilmaz (2009, 2012), referred to as spillover indexes. The spillover index facilitates an assessment of the net contribution of one market in the information transmission mechanism of another market. Findings The empirical results show bi-directional, but weak interdependence between the South African and Nigerian stock markets returns and oil market returns. The results for volatility spillovers show independence of volatilities between Nigeria stock markets and oil markets, while weak bi-directional spillovers were found between South African equity volatilities and oil volatilities. The time-varying total spillover plots for returns and volatilities are broadly similar and show a trend that has been observed in other studies: an increasing trend during the non-crisis period, a burst in the crisis year, a maintained higher level of transmission afterwards. Originality/value Existing studies examining spillovers between oil and stock markets have largely ignored Sub-Saharan African markets. A common feature of existing studies is that they have been conducted for two groups of countries: either European and US markets; or Gulf Cooperation Council markets Thus, this study fills this gap in the literature by examining return and volatility spillovers between oil and the stock markets of Nigeria and South Africa.

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MALIK, MUHAMMAD IRFAN, and ABDUL RASHID. "RETURN AND VOLATILITY SPILLOVER BETWEEN SECTORAL STOCK AND OIL PRICE: EVIDENCE FROM PAKISTAN STOCK EXCHANGE." Annals of Financial Economics 12, no. 02 (June 2017): 1750007. http://dx.doi.org/10.1142/s2010495217500075.

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This paper aims to investigate the return and volatility spillover between world oil prices and the sectoral stock of Pakistan. We estimate a bivariate VAR(1)-AGARCH (1,1) model using weekly data sampled from January 1, 2001 to December 31, 2015. The model results are used to estimate the optimal portfolio weights and hedge ratios. The empirical findings suggest no short-run price transmission between world oil prices and stock sectors of Pakistan Stock Exchange. Only the past unexpected shocks in world oil prices has significant effect on the volatility of sectoral stock returns of Pakistan Stock Exchange, and no volatility spillover exist between world oil price and stock sectors. The optimal portfolio weights and hedge ratios for oil/stock holdings are sensitive to sectors considered. These findings are of great interest for policy makers, hedge fund managers, [Formula: see text] investors and market participants.

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Shirazi, Masoud, Abdolrasoul Ghasemi, Teymour Mohammadi, Jurica Šimurina, Ali Faridzad, and Atefeh Taklif. "A Dynamic Network Comparison Analysis of Crude Oil Trade: Evidence from Eastern Europe and Eurasia." Zagreb International Review of Economics and Business 23, no. 1 (May 1, 2020): 95–119. http://dx.doi.org/10.2478/zireb-2020-0007.

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AbstractThis article characterizes a dynamic crude oil trade network of Eastern Europe and Eurasia using the network connectedness measure of Diebold and Yilmaz (2014, 2015) and asymmetric reaction of crude oil bilateral trade flow in response to the positive and negative changes of its key determinants using the nonlinear panel ARDL model. Results indicate the existence of large and time-varying spillovers with a considerable explanatory power among the crude oil trade flow volatility of Iran, Russia, US and Saudi Arabia in Eastern Europe and Eurasia crude oil trade network. The findings also show that crude oil trade flow of Eastern Europe and Eurasia experiences net volatility transmission to Iran, Russia and US respectively, whereas it is a net volatility receiver from Saudi Arabia. Also based on gravity models, the analysis confirms the existence of impact, reaction and adjustment asymmetry through different magnitude among network participants.

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Umar, Zaghum, Mariya Gubareva, Muhammad Naeem, and Ayesha Akhter. "Return and volatility transmission between oil price shocks and agricultural commodities." PLOS ONE 16, no. 2 (February 19, 2021): e0246886. http://dx.doi.org/10.1371/journal.pone.0246886.

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This paper studies the connectedness between oil price shocks and agricultural commodities. Our sample period ranges from January 2002 to July 2020, covering the three global crises; Global Financial Crisis, the European sovereign debt crisis and Covid-19 pandemic crisis. We employ Granger causality tests, and the static and dynamic connectedness spillover index methodology. We find that the shocks in oil prices are Granger-caused mainly by price changes of grains, live cattle, and wheat, while supply shock granger causes variations mostly in grain prices. We find that, from the point of view of static connectedness, for both, price and volatility spillovers, the livestock is the largest transmitter, while the lean hogs are the major receiver. Our dynamic analysis evidences that connectedness increases during the financial crisis period. Our results are potentially useful for investors, portfolios managers and policy makers.

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Gomes, Mathieu, and Anissa Chaibi. "Volatility Spillovers Between Oil Prices And Stock Returns: A Focus On Frontier Markets." Journal of Applied Business Research (JABR) 30, no. 2 (February 27, 2014): 509. http://dx.doi.org/10.19030/jabr.v30i2.8421.

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Frontier markets are increasingly sought by investors in search of higher returns and low correlation with traditional assets. As such, it is important for financial market participants to understand the volatility transmission mechanism across these markets in order to make better portfolio allocation decisions. This paper employs a bivariate BEKK-GARCH(1,1) model to simultaneously estimate the mean and conditional variance between equity stock markets (twenty-one national frontier stock indices and two broad indices the MSCI Frontier Markets and the MSCI World) and oil prices. We examine weekly returns from February 8, 2008 to February 1, 2013 and find significant transmission of shocks and volatility between oil prices and some of the examined markets. Moreover, this spillover effect is sometimes bidirectional.

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강상훈 and 윤성민. "Return and Volatility Transmission Between Oil Prices and Emerging Asian Markets." Seoul Journal of Business 19, no. 2 (December 2013): 74–93. http://dx.doi.org/10.35152/snusjb.2013.19.2.003.

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Kang, Sang Hoon, and Seong-Min Yoon. "Information Transmission of Volatility between WTI and Brent Crude Oil Markets." Environmental and Resource Economics Review 22, no. 4 (December 30, 2013): 671–89. http://dx.doi.org/10.15266/kerea.2013.22.4.671.

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Lee, Seungwon, Yu Tao, Lei Dai, and Hao Hu. "The volatility transmission between crude oil market and tanker freight market." International Journal of Shipping and Transport Logistics 12, no. 6 (2020): 619. http://dx.doi.org/10.1504/ijstl.2020.10032895.

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Dai, Lei, Hao Hu, Yu Tao, and Seungwon Lee. "The volatility transmission between crude oil market and tanker freight market." International Journal of Shipping and Transport Logistics 12, no. 6 (2020): 619. http://dx.doi.org/10.1504/ijstl.2020.111122.

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Wang, Shu Ping, Ai Mei Hu, and Zhen Xin Wu. "The Impact of Oil Price Volatility on China’s Economy: An Empirical Investigation Based on VAR Model." Advanced Materials Research 524-527 (May 2012): 3211–15. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.3211.

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China has been in rapid economic growth and industrial structure reform for recent years, and oil, as a most important raw material for industrial production, its price fluctuations have direct impact on energy-intensive industries as well as non-energy-intensive industries and their associated industries’ overall demands. Under the price transmission mechanism, oil price volatility imposes significant influences on economic growth rate, price level, unemployment rate and monetary policy as well. This paper established VAR model among oil prices and economic indicators such as economic growth rate, price level, unemployment rate and monetary policy, and by data processing , stability test and cointegration test, we found that there existed long term stable cointegration relations among these sequences; through Granger Causality test we found that oil price volatility was the Granger cause of the fluctuations of economic growth rate, price level and monetary policy, and meanwhile, changes in economic growth rate is the Granger cause of that in price level. The result of our empirical study indicated that, oil price volatility has a profound influence on China’s economy, and thus, China should improve the establishment of the oil futures market to avoid risks of oil price volatility and secure long-term stability of its economic growth.

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Demiralay, Sercan, Nikolaos Hourvouliades, and Athanasios Fassas. "Dynamic co-movements and directional spillovers among energy futures." Studies in Economics and Finance 37, no. 4 (June 26, 2020): 673–96. http://dx.doi.org/10.1108/sef-09-2019-0374.

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Purpose This paper aims to examine dynamic equicorrelations (DECO) and directional volatility spillover effects among four energy futures markets, namely, West Texas Intermediate crude oil, heating oil, natural gas and reformulated blendstock for oxygenate blending gasoline, by using a multivariate fractionally integrated asymmetric power ARCH–DECO–generalized autoregressive conditional heteroskedasticity (GARCH) model and the spillover index technique. Design/methodology/approach The empirical analysis uses the dynamic equicorrelation model of Engle and Kelly (2012) to examine time-varying correlations at equilibrium. The authors further analyze dynamic volatility transmission among energy futures by using Diebold and Yilmaz (2012) dynamic spillover index based on generalized value-at-risk framework. Findings The empirical results provide evidence of heightened equicorrelations at times of financial turmoil. More specifically, the dynamic spillover analysis shows that volatility is transmitted predominantly from crude oil to the other markets and risk transfer among four markets exhibits asymmetries. Spillovers are found to be highly responsive to dramatic events such as the 9/11 terror attack, 2008–2009 global financial crisis and 2014–2016 oil glut. Practical implications The results of this study have important practical implications for investors, portfolio managers and energy policymakers as the presence of time-varying co-movements and spillovers suggests the need for dynamic trading strategies. There are also implications regarding risk management practices, as there is evidence of increased volatility transmission at times of financial turmoil and uncertainty. Finally, the results provide insights to policymakers in a better understanding of the spillover dynamics. Originality/value This paper investigates the DECOs and spillover effects among crude oil, natural gas, heating oil and gasoline futures markets. To the best of the knowledge, this is one of a few studies that examine co-movements and risk transfer in energy futures in a comprehensive framework.

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Gardebroek, Cornelis, and Manuel A. Hernandez. "Do energy prices stimulate food price volatility? Examining volatility transmission between US oil, ethanol and corn markets." Energy Economics 40 (November 2013): 119–29. http://dx.doi.org/10.1016/j.eneco.2013.06.013.

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Ben Sita, Bernard, and Salah Abosedra. "Volatility Spillovers: Evidence On U.S. Oil Product Markets." Journal of Applied Business Research (JABR) 28, no. 6 (October 25, 2012): 1237. http://dx.doi.org/10.19030/jabr.v28i6.7339.

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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.

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Bergmann, Dennis, Declan O’Connor, and Andreas Thümmel. "Price and volatility transmission in, and between, skimmed milk powder, livestock feed and oil markets." Outlook on Agriculture 46, no. 4 (December 2017): 248–57. http://dx.doi.org/10.1177/0030727017744928.

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Price and volatility transmission effects between European Union (EU) and World skimmed milk powder (SMP) prices, as well as those between both SMP series, soybeans and crude oil prices from 2004 to 2014 were analysed using a vector error correction model combined with a multivariate GARCH model. The results show significant transmission effects between EU and World SMP prices, but no significant transmission effects from soybeans or crude oil to either of the SMP prices. For policymakers and modellers, these results indicate the need to consider World SMP prices when considering EU prices. On the other hand, the finding of no transmission effects from soybean to SMP prices reduces the opportunity for a successful cross-hedging for dairy commodities using well-established soybean derivative markets.

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Kirkulak-Uludag, Berna, and Omid Safarzadeh. "Exploring shock and volatility transmission between oil and Chinese industrial raw materials." Resources Policy 70 (March 2021): 101974. http://dx.doi.org/10.1016/j.resourpol.2020.101974.

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Kirkulak-Uludag, Berna, and Omid Safarzadeh. "Exploring shock and volatility transmission between oil and Chinese industrial raw materials." Resources Policy 70 (March 2021): 101974. http://dx.doi.org/10.1016/j.resourpol.2020.101974.

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Malik, Farooq, and Shawkat Hammoudeh. "Shock and volatility transmission in the oil, US and Gulf equity markets." International Review of Economics & Finance 16, no. 3 (January 2007): 357–68. http://dx.doi.org/10.1016/j.iref.2005.05.005.

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Tsuji, Chikashi. "Return transmission and asymmetric volatility spillovers between oil futures and oil equities: New DCC-MEGARCH analyses." Economic Modelling 74 (August 2018): 167–85. http://dx.doi.org/10.1016/j.econmod.2018.05.007.

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Chan, Wing, Bryce Shelton, and Yan Wu. "Volatility Spillovers Arising from the Financialization of Commodities." Journal of Risk and Financial Management 11, no. 4 (October 27, 2018): 72. http://dx.doi.org/10.3390/jrfm11040072.

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This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results show financialization has an impact on the volatility of commodity prices, predominantly for non-energy commodities. However, the impact on volatility is not symmetric across all commodities. The analysis of index investment and investors’ positions in futures markets shows that, when a relationship exists, it is generally negatively correlated with the realized volatility of non-energy commodities. Using realized volatility in the difference-in-difference model provides estimates that are inconsistent with other findings that non-energy commodities, traded as a part of indices, have experienced higher volatility. The results are similar to the index investment and futures market analysis, where increased participation by investors through new investment products has put download pressure on realized volatility.

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Ngene, Geoffrey, Jennifer Brodmann, and M. Kabir Hassan. "DYNAMIC VOLATILITY AND SHOCK INTERACTIONS BETWEEN OIL AND THE U.S. ECONOMIC SECTORS." Journal of Business Accounting and Finance Perspectives 1, no. 1 (August 26, 2019): 1. http://dx.doi.org/10.26870/jbafp.2018.01.002.

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his study examines (i) the dynamic shocks and volatility interactions between each of the eleven U.S. economic sectors and the oil market; (ii) riskminimizing optimal capital allocations between each sector and oil; and (iii) the hedging effectiveness resulting from the inclusion of oil in each sector portfolio. Using weekly data spanning the period June 1994 through February 2016, we document the following regularities: (i) the conditional correlation between each sector and the oil market is time-varying and slowly decaying; (ii) there is either volatility or shock transmission from oil to each sector but not the reverse; and (iii) investors can minimize and hedge risk by allocating a portion of their wealth to oil commodities and forming a portfolio consisting of sector stocks and oil commodities. however, they will need to overweight their investment in sector stocks. Our findings indicate that oil commodities offer diversification potential to U.S. investors holding sector portfolios such as sector ETFs and mutual funds. Further, the risk parity portfolio weights significantly differ from the capital allocation weights.

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Thi Ngoc Nguyen, Mien. "Examining contagion effects between global crude oil prices and the Southeast Asian stock markets during the COVID-19 pandemic." Investment Management and Financial Innovations 20, no. 1 (January 24, 2023): 77–87. http://dx.doi.org/10.21511/imfi.20(1).2023.08.

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Many previous studies identify the contagion effect among various types of assets, defined as the increase in correlation of these assets during a financial or economic crisis. During the COVID-19 outbreak, a historic fall in global fuel demand and oil prices has been witnessed. Because crude oil has a strategic position among the export products of the Southeast Asian economies, even a tiny global oil price change leads to a plunge in these stock markets. This study addresses the spillovers of the volatility between the West Texas Intermediate crude oil prices and stock indices across six ASEAN emerging economies. Besides, the study examines whether a contagion connecting the global energy prices and these stock markets exists during the coronavirus pandemic. The empirical results are acquired by applying the Bayesian test for equality of means on the dynamic conditional correlations computed from DCC-GARCH models. The findings present positive volatility transmission from crude oil prices toward these emerging equity markets. During the health crisis, co-movements intensify, indicating the occurrence of contagion effects. The empirical results provide valid implications for policymakers and international investors because a precise volatility forecast is vital for managing portfolio risk. AcknowledgmentThis research is funded by University of Economics Ho Chi Minh City, Vietnam.

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Haq, Inzamam UI, Hira Nadeem, Apichit Maneengam, Saowanee Samantreeporn, Nhan Huynh, Thasporn Kettanom, and Worakamol Wisetsri. "Do Rare Earths and Energy Commodities Drive Volatility Transmission in Sustainable Financial Markets? Evidence from China, Australia, and the US." International Journal of Financial Studies 10, no. 3 (September 6, 2022): 76. http://dx.doi.org/10.3390/ijfs10030076.

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The high volatility and energy usage of rare earths have raised sustainable and financial concerns for environmentalists and sustainable investors. Therefore, this paper aims to investigate time-varying volatility transmission among rare earths elements, energy commodities, and sustainable financial markets. The sample covers global and major financial markets, i.e., US, China, and Australia. Using daily log returns from 2018 to 2022, the paper considers the dynamic Time Varying Parameter-Vector Autoregression (TVP-VAR) connectedness approach to gauge the time-varying features of volatility spillovers. The findings of total spillovers index reveal weak connectedness among markets during the sampled period. US and China rare earth markets were net volatility transmitters, whereas the Dow Jones Australia Sustainability Index (ASI), China Sustainability Index (CSI), Dow Jones Sustainability World Index (SWI), and MVIS Global Rare Earth Index (MVISGREI) were net recipients. Moreover, energy commodities i.e., WTI Crude Oil, Gasoline, and Natural Gas were net volatility transmitters, while ASI, CSI, and SWI were major volatility recipients. The weak financial contagion effect and connectedness across financial markets uncovers possible diversification opportunities. However, the US sustainable financial market is persistently not affected by these volatility spillovers. Policymakers need to establish strict regulations to protect sustainable financial markets in China and Australia.

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Salisu, Afees A., and Hakeem Mobolaji. "Modeling returns and volatility transmission between oil price and US–Nigeria exchange rate." Energy Economics 39 (September 2013): 169–76. http://dx.doi.org/10.1016/j.eneco.2013.05.003.

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Alqahtani, Abdullah, Amine Lahiani, and Ali Salem. "Crude oil and GCC stock markets." International Journal of Energy Sector Management 14, no. 4 (January 13, 2020): 745–56. http://dx.doi.org/10.1108/ijesm-06-2019-0013.

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Purpose This paper aims to investigate the transmission of international oil prices to the stock market indices of the Gulf Cooperation Council (GCC) countries over the weekly period from April 07, 2004, to August 15, 2018. Design/methodology/approach The authors use the augmented Dickey–Fuller (ADF) unit root test to check the order of integration of data series. Afterward, the authors use the ordinary least square method to determine the spillover of international oil prices to the stock markets of GCC countries while accounting for the time-varying volatility of oil and stock market returns through the generalized autoregressive conditional heteroskedasticity. Then, the Johansen (1991) cointegration test is used to determine the long-run equilibrium relationship. Finally, the Granger (1969) causality test is used to determine the short-run causal effects between oil and the stock markets returns of GCC countries. Findings The findings indicate that the stock markets of GCC countries are efficient and respond significantly to international oil prices and evidence of high volatility associated with oil returns. Originality/value Investors and portfolio managers should consider the association between international oil prices and GCC stock returns when allocating their funds for diversification strategy. Moreover, policymakers should better understand the behavior of local stock markets.

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Abbas, Ghulam, David G. McMillan, and Shouyang Wang. "Conditional volatility nexus between stock markets and macroeconomic variables." Journal of Economic Studies 45, no. 1 (January 8, 2018): 77–99. http://dx.doi.org/10.1108/jes-03-2017-0062.

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Purpose The purpose of this paper is to analyse the relation between stock market volatility and macroeconomic fundamentals for G-7 countries using monthly data over the period from July 1985 to June 2015. Design/methodology/approach The empirical methodology is based on two steps: in the first step, the authors obtain the conditional volatilities of stock market returns and macroeconomic variables through the GARCH family of models. The authors also incorporate the impact of early 2000s dotcom and the global financial crises. In the second step, the authors estimate multivariate vector autoregressive model to analyze the dynamic relation between stock markets return and macroeconomic variables. Findings The overall results for G-7 countries indicate a weak volatility transmission from macroeconomic factors to stock market volatility at individual level but the collective impact of volatility transmission is highly significant. Although, the results of block exogeneity indicate a bidirectional causality except UK, but the causal linkage is quite weak from stock market to macroeconomic variables. Moreover, the local financial variables excluding interest rate are closely integrated, and the volatility of industrial production growth and oil price are identified as the most significant macroeconomic factors that could possibly influence the directions of stock markets. Originality/value This research establishes the nature of the links between stock market and macroeconomic volatility. Research to date has been unable to satisfactorily establish the empirical nature of such links. The authors believe this paper begins to do this.

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Etienne, Xiaoli Liao, Andrés Trujillo-Barrera, and Seth Wiggins. "Price and volatility transmissions between natural gas, fertilizer, and corn markets." Agricultural Finance Review 76, no. 1 (May 3, 2016): 151–71. http://dx.doi.org/10.1108/afr-10-2015-0044.

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Purpose – The purpose of this paper is to investigate the price and volatility transmission between natural gas, fertilizer (ammonia), and corn markets, an issue that has been traditionally ignored in the literature despite its significant importance. Design/methodology/approach – The authors jointly estimate a vector error correction model for the conditional mean equation and a multivariate generalized autoregressive heteroskedasticity model for the conditional volatility equation to investigate the interactions between natural gas, ammonia, and corn prices and their volatility. Findings – The authors find significant interplay between fertilizer and corn markets, while only a mild linkage in prices and volatility exist between those markets and natural gas during the period 1994-2014. There is not only a positive relationship between corn and ammonia prices in the short run, but both prices react to deviations from the long-run parity. Furthermore, the lagged conditional volatility of ammonia prices positively affects conditional volatility in the corn market and vice versa. This result is robust to a specification using crude oil price as an alternative to natural gas price to account for the large transportation cost built into ammonia prices. Results for the period of 2006-2014 indicate virtually no linkage between natural gas prices and those of fertilizer and corn during that period, while linkages in price level and volatility between the latter remain strong. Originality/value – This paper is the first in the literature to comprehensively examine the role of fertilizer on corn prices and volatility, and its relation to natural gas prices.

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Journal articles on the topic 'Oil volatility transmission'

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