Volatility persistence in crude oil markets

Charles, Amélie; Darné, Olivier

doi:10.1016/j.enpol.2013.10.042

Cited by 70 publications

(60 citation statements)

References 85 publications

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“…This paper also leaves possible extensions for future research. One novel extension is to investigate whether the long memory observed in the volatility series of spot commodity is a spurious behavior or not, since some research has been skeptical of the validity of the long memory due to the finding of structural breaks, e.g., Charles et al [15] and Charfeddine [16]. Secondly, the multivariate Archimedean Copulas and Vine Copulas can be investigated to capture the dependence structure of inventory portfolio.…”

Section: Resultsmentioning

confidence: 99%

“…Accounting for long memory yields an additional improvement in specification of volatility models and further impact on the term structure of volatility. Furthermore, numerous recent studies have shown that volatility of commodities with increasing financialization at long horizon can be more accurately forecasted through accounting for long memory via FIGARCH model especially for energy commodities and precious metals (e.g., Elder et al [11]; Cunado et al [12]; Aloui et al [13]; Arouri et al [14]; Charles et al, [15]; Charfeddine [16] and Youssef et al [17]). Thus, long memory is indispensable to model and forecast the multi-period volatility and long-term price risk of inventory in SCF.…”

Section: Introductionmentioning

confidence: 99%

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The Effects of Long Memory in Price Volatility of Inventories Pledged on Portfolio Optimization of Supply Chain Finance

He¹,

Wang²,

Xiang-lin³

2016

JMF

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Due to the illiquidity of inventories pledged, the essential of price risk management of supply chain finance is to long-term price risk measure. Long memory in volatility, which attests a slower than exponential decay in the autocorrelation function of standard proxies of volatility, yields an additional improvement in specification of multi-period volatility models and further impact on the term structure of risk. Thus, long memory is indispensable to model and measure long-term risk. This paper sheds new light on the impact of the existence and persistence of long memory in volatility on inventory portfolio optimization. Firstly, we investigate the existence of long memory in volatility of the inventory returns, and examine the impact of long memory on the modeling and forecasting of multi-period volatility, the dependence structure between inventory returns and portfolio optimization. Secondly, we further explore the impact of the persistence of long memory in volatility on the efficient frontier of inventory portfolio via a data generation process with different long memory parameter in the FIGARCH model. The extensive Monte Carlo evidence reveals that both GARCH and IGARCH models without accounting for long memory will misestimate the actual long-term risk of the inventory portfolio and further bias the efficient frontier; besides, through A sensitive analysis of long memory parameter d, it is proved that the portfolio with higher long memory parameter possesses higher expected return and lower risk level. In conclusion, banks and other participants will benefit from the long memory taken into the long-term price risk measure and portfolio optimization in supply chain finance.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effects of Long Memory in Price Volatility of Inventories Pledged on Portfolio Optimization of Supply Chain Finance

He¹,

Wang²,

Xiang-lin³

2016

JMF

View full text Add to dashboard Cite

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“…However, most research on the crude oil market has focused on the behavior of oil prices rather than on volatility. The limited studies on crude oil volatility to date focus solely on volatility persistence, i.e., the relation between current and past volatility, in this market (see, for example, Wilson, Aggarwal and Inclan, 1996;Yang, Hwang and Huang, 2002;Pindyck, 2004;Kuper and Soest, 2006;Agnolucci, 2009;and Charles and Darne, 2014). Other possible determinants of crude oil volatility are neglected in the literature.…”

Section: Introductionmentioning

confidence: 99%

Ex-ante Determinants of Volatility in the Crude Oil Market

2014

International Journal of Financial Research

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This paper examines the causes and behavior of price volatility in the US crude oil market. Although crude oil prices are among the most volatile, they have received limited academic scrutiny heretofore. This study shows that (1) the crude oil market is characterized by volatility persistence, (2) a negative shock has more impact on future volatility than an equal positive shock, (3) crude oil volatility is lower at higher prices, (4) OPEC meeting announcements and the Petroleum Status Report releases cause increased volatility, and (5) there is a day-of-the-week pattern in this market. I develop and employ an improved procedure for testing and quantifying the hypothesized volatility determinants within GARCH type model.

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“…As Charles and Darné (2014a) state, financial markets are affected by specific incidents that can impact on modeling financial time series. These events, such as wars, natural disasters, political conflicts, etc., that are mostly unpredictable, are the so-called outliers.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Charles andDarné (2014a and2014b) estimate the price volatility of crude oil and Dow Jones industrial average index, respectively, after detecting and correcting outliers in the GARCHtype models, applying the Laurent et al (2013) outlier detection method. However, to the best of our knowledge, there is no study in the literature that takes into account the presence of outliers in volatility of non-energy commodity markets.…”

Section: Introductionmentioning

confidence: 99%

The role of outliers and oil price shocks on volatility of metal prices

Behmiri

Manera

2015

Resources Policy

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SummaryThis study investigates the price volatility of metals, using the GARCH and GJR models. First we examine the persistence of volatility and the leverage effect across metal markets taking into account the presence of outliers, and second we estimate the effects of oil price shocks on the price volatility of metals, allowing for the asymmetric responses. We use daily spot prices for the selected metals, including aluminum, copper, lead, nickel, tin, zinc, gold, silver, palladium and platinum.The main findings indicate that, returns show a high degree of volatility persistence before and after correcting outliers, outliers bias the parameters estimation of the GARCH-type models, and removing outliers improves the performance of models in capturing volatility. However in a comparison, Student-t distribution outperforms the approach of correcting outliers in capturing volatility. Moreover, we find the existence of inverse leverage effect for seven metals, the leverage effect for copper and no leverage effect for nickel and palladium. Finally, price volatility of metals differently reacts to oil price shocks and there is an asymmetric reaction of volatility to oil price shocks for seven metals.Keywords: Metals, Commodities, Volatility, Oil Price, Outliers. JEL Classification: G13, Q4, C1Acknowledgments: We thank participants to the FEEM seminars. Moreover, the authors acknowledge financial support from the "Energy: Resources and Markets" research program of Fondazione Eni Enrico Mattei (FEEM).

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Volatility persistence in crude oil markets

Cited by 70 publications

References 85 publications

The Effects of Long Memory in Price Volatility of Inventories Pledged on Portfolio Optimization of Supply Chain Finance

The Effects of Long Memory in Price Volatility of Inventories Pledged on Portfolio Optimization of Supply Chain Finance

Ex-ante Determinants of Volatility in the Crude Oil Market

The role of outliers and oil price shocks on volatility of metal prices

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