Academic literature on the topic 'Oil volatility transmission'
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Journal articles on the topic "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.
Full textDissertations / Theses on the topic "Oil volatility transmission"
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Mokengoy, Mardochée Bopo. "Volatility transmission between the oil price, the exchange rate and the stock market index." Master's thesis, Université Laval, 2015. http://hdl.handle.net/20.500.11794/25856.
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Ce mémoire analyse la transmission de volatilité entre le prix du pétrole, le taux de change et l’indice boursier au Canada et aux États-Unis de 1999/01/04 à 2014/03/21. En utilisant un modèle MGARCH-BEKK, nos résultats montrent qu’au Canada, il existe une transmission bidirectionnelle de volatilité entre le taux de change $US/$CAD et l’indice boursier TSX, une transmission positive de l’indice boursier au prix du pétrole, ainsi qu’une transmission négative du taux de change au prix du pétrole. Les résultats suggèrent également que ces relations ne sont pas stables dans le temps. Pour les États-Unis, le modèle estimé ne satisfait pas la condition de stationnarité de la covariance pour la période totale et la sous période 1999/01/04 – 2002/10/08. C’est pourquoi nous considérons uniquement les résultats des sous périodes 2002/10/09 – 2008/05/30 et 2008/06/02 – 2014/03/21. Il ressort qu’il existe des transmissions de volatilité, mais que celles-ci ne sont pas stables dans le temps.This thesis analyzes the transmission of volatility between oil prices, exchange rates and stock market indices in Canada and in the USA for the period 1999/01/04 – 2014/03/21. Using a multivariate GARCH – BEKK model, we find that in Canada, there is a bidirectional transmission of volatility between the exchange rate $US/$CAD and the stock market index TSX, a positive transmission from the stock market index to the oil price and a negative transmission from the exchange rate to the oil price. We find also that these relationships are not stable over time. For the USA, the model estimated does not satisfy the condition of covariance stationarity for the entire sample and the sub sample 1999/01/04 – 2002/10/08. So we consider only results for sub samples 2002/10/09 – 2008/05/30 and 2008/06/02 – 2014/03/21. Results show that there are transmissions of volatility, but here again, these relationships are not stable over time.
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Block, Alexander Souza. "A utilização do método wavelets na análise da volatilidade dos preços do petróleo." Universidade Federal de Santa Maria, 2013. http://repositorio.ufsm.br/handle/1/4698.
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Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorThis work seeks to analyze at different frequencies, the transmission of volatility in the prices of crude oil produced by OPEC members and other producing and exporting countries that are not part of this organization and to analyze the presence of structural breaks in dynamic correlation between crude oil spot and future prices. The Wavelets methodology employed aims to decompose the series to verify its behavior at different frequencies, revealing additional information or confirming trends. To check the transmission process of the volatility it is proposed the application of Granger Causality Test. This made it possible to understand the functioning of this important market and answer the following question: How behaves the volatility of oil prices when analyzed considering several time horizons in an analysis in the frequency domain? The analysis of volatility transmission shows a strong integration of the international oil market, the correlation structural breaks tests results shows that structural break point is not static for any analysis, it moves, depending the frequency scale and the time window.
Este trabalho busca analisar em diferentes frequências, o sentido e a transmissão da volatilidade nos preços do petróleo bruto produzido pelos países membros da OPEP (Organização dos Países Exportadores de Petróleo) e dos demais países produtores e exportadores que não fazem parte desta organização; bem como analisar a presença de quebras estruturais na correlação dinâmica entre os preços à vista e futuro do petróleo. A metodologia de Wavelets empregada tem por objetivo decompor as séries estudas a fim de verificar seu comportamento em diferentes frequências, revelando informações adicionais ou confirmando tendências observadas. Para a verificação do processo de transmissão da volatilidade foi proposta a utilização do método de Causalidade de Granger. Desta forma foi possível compreender o funcionamento deste importante mercado e responder a seguinte questão: Como se comporta a volatilidade do preço do petróleo quando se analisam variados horizontes de tempo em uma análise no domínio da frequência? A análise da transmissão da volatilidade aponta para uma forte integração do mercado internacional do petróleo, enquanto o resultado da análise das quebras na correlação mostra que o ponto de quebra estrutural não é estático para toda e qualquer análise, ele se move, dependendo da frequência e do horizonte temporal.
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Kaltalioglu, Muge. "Price Transmissions Between Food And Oil." Thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612708/index.pdf.
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The upward movement in oil and food prices in the 2000s has triggered interest in the information transmission mechanism between the two markets. This research investigates the volatility spillover between oil, food, and agricultural raw material price indexes for the period January 1980 to April 2008. The results of the Cheung-Ng procedure show that variation in oil prices does not Granger cause the variance in food and agricultural raw material prices. However, there is bi-directional spillover between agricultural raw material and oil markets. Besides, it examines volatility spillover between maize, wheat, soybean, rice, and oil spot prices for the period January-1998 to February-2009. The results show that volatility spillover in oil returns leads fluctuations in maize, soybean, wheat, and rice returns in 3 months. In addition, there are bi-directional spillovers between oil and soybean returns, rice and wheat returns.
This topic is essential for countries whose populations grow rapidly because forecasting of commodity prices plays an important role in instituting the economic policy. Also, understanding the dynamics of the economy leads to better economic policies. Thus, results are important for investors and policy makers interested in price shocks and transmission.
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Yeh, Yu-Chi, and 葉毓琪. "Volatility Transmission and Hedging Strategy between Oil Price and Commodity Futures." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/90861826960167431097.
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碩士中原大學
國際貿易研究所
97
The significant increase in demand from developing countries and speculations has pushed up oil price sharply from 2007 to mid-2008, which, in turn, has accelerated the development of alternative energy all over the world. Grains, which can be made into bio-fuels, inevitably became the highly-demanded goods. However, when oil price started to drop during late 2008, grain price fell as well. The phenomenon of co-movement between oil price and grain price has never happened before, and, hence, has seldomly been explored by researchers. This study applies multi-variate GARCH methods to analyze the transmission relationship among several price variables. Our results find that significant transmission effects do exist between oil price and grain price. The computed correlation coefficients between the variables also reveal that the correlation has risen apparently in recent years. With respect to hedging, we find that multi-GARCH can reduce risk in most cases. In addition, DCC-GARCH model can reduce much larger percentage of risk than BEKK-GARCH model does. Furthermore, cross hedging does not show to have better performance than direct hedging does. That is, direct hedging is good enough to reduce most of the risk.
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Chen, Yen-Ting, and 陳彥廷. "Jump Risk and Volatility Transmission Effects between Crude Oil, REITs, Gold and Exchange." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/35515762817290832817.
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碩士淡江大學
財務金融學系碩士班
100
This study discuss the interaction between crude oil price, real estate, gold spot price and U.S. dollar exchange rate since 2005. The sample data is Texas Light Crude Oil Closing Price, U.S. Real Estate Fund Index daily quotation, Gold Spot Price and U.S. Dollar Exchange Rate daily quotation, from June 17, 2005 to September 30, 2011. In this paper, the ARJI model is the tool to explore the jump intensity and volatility spillover effects of these four financial asset, and we use the VAR model to discuss the following issue. The empirical results are as follows: 1. According to the Jarque-Bera normality test, we found that there is no significant evidence to proof the normality distribution of the four financial variables exist, and the fluctuate of the rate of return might be affected by random exception events. 2. ARJI model estimation results show that the mean return of crude oil, real estate index, gold and U.S. dollar exchange are significant at the 5% statistics level. It states that the exceptional information would be the reason of the instantaneous jumping behavior of these four financial elements, and in addition to U.S. dollar exchange rate, the jumping behavior of other variables cause by exceptional information most have negative impact on the return. 3. By the VAR test, we found the T-1 return of crude oil, real estate index, gold and U.S. dollar exchange rate are all significant, indicate that larger amount would be needed for these four financial elements to reinstate the equilibrium relationship when the equilibrium become deviate. And the T-1 return coefficients of real estate index, gold and U.S. dollar exchange rate is quite large, close to 1, indicate that the impacts of the T-1 change of these three variables to the current T term is almost completely positive correlation. For the T-1 coefficient of crude oil, Kaufmann (2004) and Hansen and Lindholt (2004) state that OPEC is powerful able to affect the price of crude oil. Therefore, we speculate that the tension in the Middle East and the war frequency might affect the West Texas crude oil price indirectly, make its impact of T-1 coefficient on the current T is not as strong as the other variables.
Books on the topic "Oil volatility transmission"
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Chau, Frankie Ho-Chi. Volatility Transmission across Commodity Futures Markets. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190656010.003.0018.
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Sharp movements in crude oil prices and their impact on other commodities have renewed interest in the assessment of dynamic interactions between commodity futures markets. This chapter examines this topic by investigating the intensity and direction of volatility transmission across three major classes of commodities, including agricultural products (corn, coffee, and soybeans), energy (crude oil and gas), and metals (copper, gold, and silver). Overall, the evidence suggests that important volatility episodes and fluctuations exist across major commodity markets; the total cross-market spillovers are limited until the onset of financial crisis of 2007–2008. As the crisis intensified, so too did the commodity volatility spillovers, with substantial stress carrying over from the energy and metal markets to others. These findings are important in understanding the level and transmission mechanism of risk across commodity futures markets and are relevant to regulators in formulating policies to tackle excessive volatility, particularly during turbulent periods.
Book chapters on the topic "Oil volatility transmission"
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Atu, Nurul Nazurah, Imbarine Bujang, and Norlida Jaafar. "Shock and Volatility Transmission Between Oil Prices and Stock Returns: Case of Oil-Importing and Oil-Exporting Countries." In Proceedings of the 2nd Advances in Business Research International Conference, 111–22. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6053-3_11.
Full textConference papers on the topic "Oil volatility transmission"
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Falode, Olugbenga, and Christopher Udomboso. "Efficient Crude Oil Pricing Using a Machine Learning Approach." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207152-ms.
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Abstract Crude oil, a base for more than 6000 products that we use on a daily basis, accounts for 33% of global energy consumption. However, the outbreak and transmission of COVID-19 had significant implications for the entire value chain in the oil industry. The price crash and the fluctuations in price is known to have far reaching effect on global economies, with Nigeria hard. It has therefore become imperative to develop a tool for forecasting the price of crude oil in order to minimise the risks associated with volatility in oil prices and also be able to do proper planning. Hence, this article proposed a hybrid forecasting model involving a classical and machine learning techniques – autoregressive neural network, in determining the prices of crude oil. The monthly data used were obtained from the Central Bank of Nigeria website, spanning January 2006 to October 2020. Statistical efficiency was computed for the hybrid, and the models from which the proposed hybrid was built, using the percent relative efficiency. Analyses showed that the efficiency of the hybrid model, at 20 and 100 hidden neurons, was higher than that of the individual models, the latter being the best performing. The study recommends urgent diversification of the economy in order not for the nation to be plunged into a seemingly unending recession.