scholarly journals Modeling and Forecasting the Volatility of Oil Futures Using the ARCH Family Models

2012 ◽  
Vol 1 (1) ◽  
pp. 79-108
Author(s):  
Tareena Musaddiq

This study attempts to model and forecast the volatility of light, sweet, crude oil futures trading at the NYMEX during 1998–2009, using various models from the ARCH family. The results reveal that the GJR-GARCH (1,2) model is best suited to forecast purposes. The fitted models also suggest the presence of asymmetric effects in the data. The study also reveals that trading volume and open interest do not reduce the persistence of volatility for these oil futures.

2019 ◽  
Vol 80 ◽  
pp. 793-811 ◽  
Author(s):  
Robert L. Czudaj

2014 ◽  
Vol 31 (4) ◽  
pp. 426-438 ◽  
Author(s):  
Saada Abba Abdullahi ◽  
Reza Kouhy ◽  
Zahid Muhammad

Purpose – The purpose of this paper is to examine the relationship between trading volume and returns in the West Texas Intermediate (WTI) and Brent crude oil futures markets. In so doing, the paper addresses two important issues. First, whether there is a positive relationship between returns and trading volume in the crude oil futures markets. Second, whether information regarding trading volume contributes to forecasting the magnitude of return in the markets, an important issue because the ability of trading volume to predict returns imply market inefficiency. Design/methodology/approach – The paper used daily closing futures price and their corresponding trading volumes for WTI and Brent crude oil markets during the sample period January 2008 to May 2011. Both the log volume and the unexpected component of the detrended volume are used in the analysis in other to have robust alternative conclusion. The generalized method of moments (GMM) approach is used to examine the contemporaneous relationship between returns and trading volume while the Granger causality approach, impulse response and variance decomposition analysis are used to investigate the ability of trading volume to predict returns in the oil futures markets. Findings – The results reject the postulation of a positive relationship between trading volume and returns, suggesting that trading volume and returns are not driven by the same information flow which contradicts the mixture of distribution hypothesis in all markets. The results also show that neither trading volume nor returns have the power to predict the other and therefore contradicting the sequential arrival hypothesis and noise trader model in all markets. Finally, the findings support the weak form efficient market hypothesis in the crude oil futures markets. Originality/value – The findings has important implications to market regulators because daily price movement and trading volume do not respond to the same information flow and therefore the measures that control price volatility should not focused more on volume; otherwise they may not provide fruitful outcomes. Additionally, traders and investors who participate in oil futures should not base their decisions on past trading volume because it will lead to profit loss. The results also have implications for market efficiency as past information cannot assist speculators to forecast returns in all the oil markets. Finally, investors can benefit from portfolio diversification across the two markets.


Author(s):  
Hua Wang ◽  
Weige Huang

Due to increasing speculation, crude oil futures are now becoming one of the highest traded commodities. This paper studies the relationship between trading volume and serial correlation in crude oil futures returns using high frequency data. We find that volume can positively predict the serial correlation in the short run (within an hour) but negatively predict the serial correlation in the midterm. The trading volume is not able to consistently predict serial correlation in the long run (more than a day). The results from our empirical studies are robust to a variety of controls and our study gives a new insight in the relation between volume and serial correlation of crude oil futures returns.


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