Structural changes and volatility transmission in crude oil markets

2011 ◽  
Vol 390 (23-24) ◽  
pp. 4317-4324 ◽  
Author(s):  
Sang Hoon Kang ◽  
Chongcheul Cheong ◽  
Seong-Min Yoon
Keyword(s):  
Crude Oil ◽  
Structural Changes ◽  
Volatility Transmission ◽  
Oil Markets

Price and volatility transmission in, and between, skimmed milk powder, livestock feed and oil markets

2017 ◽  
Vol 46 (4) ◽  
pp. 248-257 ◽  
Author(s):  
Dennis Bergmann ◽  
Declan O’Connor ◽  
Andreas Thümmel
Keyword(s):  
Crude Oil ◽  
Milk Powder ◽  
Multivariate Garch ◽  
Volatility Transmission ◽  
Crude Oil Prices ◽  
Multivariate Garch Model ◽  
Oil Markets ◽  
Skimmed Milk ◽  
Derivative Markets ◽  
Skimmed Milk Powder

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.

Volatility transmission across international markets amid COVID 19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hechem Ajmi ◽  
Nadia Arfaoui ◽  
Karima Saci
Keyword(s):  
Crude Oil ◽  
International Markets ◽  
The Novel ◽  
Data Set ◽  
Health Crisis ◽  
Content Type ◽  
Volatility Transmission ◽  
Oil Markets ◽  
Novel Coronavirus ◽  
Death Cases

Purpose This paper aims to investigate the volatility transmission across stocks, gold and crude oil markets before and during the novel coronavirus (COVID-19) crisis. Design/methodology/approach A multivariate vector autoregression (VAR)-Baba, Engle, Kraft and Kroner generalized autoregressive conditional heteroskedasticity model (BEKK-GARCH) is used to assess volatility transmission across the examined markets. The sample is divided as follows. The first period ranging from 02/01/2019 to 10/03/2020 defines the pre-COVID-19 crisis. The second period is from 11/03/2020 to 05/10/2020, representing the COVID-19 crisis period. Then, a robustness test is used using exponential GARCH models after including an exogenous variable capturing the growth of COVID-19 confirmed death cases worldwide with the aim to test the accuracy of the VAR-BEKK-GARCH estimated results. Findings Results indicate that the interconnectedness among the examined market has been intensified during the COVID-19 crisis, proving the lack of hedging opportunities. It is also found that stocks and Gold markets lead the crude oil market especially during the COVID-19 crisis, which explains the freefall of the crude oil price during the health crisis. Similarly, results show that Gold is most likely to act as a diversifier rather than a hedging tool during the current health crisis. Originality/value Although the recent studies in the field focused on analyzing the relationships between different markets during the first quarter of 2020, this study considers a larger data set with the aim to assess the volatility transmission across the examined international markets Amid the COVID-19 crisis, while it shows the most significant impact on various financial markets compared to other diseases.

scholarly journals Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1534
Author(s):  
Arthur J. Lin ◽  
Hai-Yen Chang
Keyword(s):  
Crude Oil ◽  
Equity Market ◽  
Volatility Transmission ◽  
China Trade ◽  
Bulk Shipping ◽  
Energy Fund ◽  
World Energy ◽  
Oil Markets ◽  
High Volatility ◽  
Trade War

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.

Covered Calls Indices in Crude Oil Markets

2009 ◽  
Author(s):  
Berlinda Liu ◽  
Srikant Dash
Keyword(s):  
Crude Oil ◽  
Oil Markets

scholarly journals Assessing Market Risk in BRICS and Oil Markets: An Application of Markov Switching and Vine Copula

2021 ◽  
Vol 9 (2) ◽  
pp. 30
Author(s):  
John Weirstrass Muteba Mwamba ◽  
Sutene Mwambetania Mwambi
Keyword(s):  
South Africa ◽  
Crude Oil ◽  
South African ◽  
Asset Allocation ◽  
Markov Switching ◽  
Tail Dependence ◽  
Lower Tail ◽  
Oil Market ◽  
Oil Markets ◽  
Vine Copula

This paper investigates the dynamic tail dependence risk between BRICS economies and the world energy market, in the context of the COVID-19 financial crisis of 2020, in order to determine optimal investment decisions based on risk metrics. For this purpose, we employ a combination of novel statistical techniques, including Vector Autoregressive (VAR), Markov-switching GJR-GARCH, and vine copula methods. Using a data set consisting of daily stock and world crude oil prices, we find evidence of a structure break in the volatility process, consisting of high and low persistence volatility processes, with a high persistence in the probabilities of transition between lower and higher volatility regimes, as well as the presence of leverage effects. Furthermore, our results based on the C-vine copula confirm the existence of two types of tail dependence: symmetric tail dependence between South Africa and China, South Africa and Russia, and South Africa and India, and asymmetric lower tail dependence between South Africa and Brazil, and South Africa and crude oil. For the purpose of diversification in these markets, we formulate an asset allocation problem using raw returns, MS GARCH returns, and C-vine and R-vine copula-based returns, and optimize it using a Particle Swarm optimization algorithm with a rebalancing strategy. The results demonstrate an inverse relationship between the risk contribution and asset allocation of South Africa and the crude oil market, supporting the existence of a lower tail dependence between them. This suggests that, when South African stocks are in distress, investors tend to shift their holdings in the oil market. Similar results are found between Russia and crude oil, as well as Brazil and crude oil. In the symmetric tail, South African asset allocation is found to have a well-diversified relationship with that of China, Russia, and India, suggesting that these three markets might be good investment destinations when things are not good in South Africa, and vice versa.

Efficiency of crude oil markets: Evidences from informational entropy analysis

Energy Policy ◽  
2012 ◽  
Vol 41 ◽  
pp. 365-373 ◽  
Author(s):  
Alejandro Ortiz-Cruz ◽  
Eduardo Rodriguez ◽  
Carlos Ibarra-Valdez ◽  
Jose Alvarez-Ramirez
Keyword(s):  
Crude Oil ◽  
Entropy Analysis ◽  
Informational Entropy ◽  
Oil Markets
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