The Return‐Implied Volatility Relation for Commodity ETFs

Padungsaksawasdi, Chaiyuth; Daigler, Robert T.

doi:10.1002/fut.21592

Cited by 36 publications

(39 citation statements)

References 52 publications

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“…For oil markets this "inverse leverage effect" has been confirmed by Wei et al (2010) who found significant indications of its existence in the ICE Brent market. However, together with Wang et al (2008), Agnolucci (2009) and Padungsaksawasdi and Daigler (2013) found no evidence of such effects in the WTI futures market. Padungsaksawasdi and Daigler (2013) also conclude that although behavioral theories proved weak explanations for returnvolatility relations in commodity markets they make more sense than the leverage hypothesis.…”

Section: Daily Returnsmentioning

confidence: 89%

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Forecasting volatility of the U.S. oil market

Haugom

Langeland

Molnár

et al. 2014

Journal of Banking & Finance

235

115

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We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when forecasting realized volatility in the WTI futures market. Additionally, we study whether other market variables, such as volume, open interest, daily returns, bid-ask spread and the slope of the futures curve, contains predictive power beyond what is embedded in the implied volatility. In out-of-sample forecasting we find that econometric models based on realized volatility can be improved by including implied volatility and other variables. Our results show that including implied volatility significantly improves daily and weekly volatility forecasts, while including other market variables significantly improves daily, weekly and monthly volatility forecasts.

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Section: Daily Returnsmentioning

confidence: 89%

“…An exception is Padungsaksawasdi and Daigler (2013) who studied the return-IV relation, and concluded that IV increases with negative returns.…”

Section: Introductionmentioning

confidence: 99%

Forecasting volatility of the U.S. oil market

Haugom

Langeland

Molnár

et al. 2014

Journal of Banking & Finance

235

115

View full text Add to dashboard Cite

show abstract

“…In addition, it found an asymmetric association in this relation. Padungsaksawasdi and Daigler (2014) investigated the return-volatility relation of ETF funds of the euro, gold and oil, with daily and intraday data. Their results indicated two points: first, that the euro and gold do not have an asymmetric return-volatility relation.…”

Section: The Return-volatilily Relationmentioning

confidence: 99%

An S-Shaped Crude Oil Price Return-Implied Volatility Relation: Parametric and Nonparametric Estimations

Silva¹

2017

IJEF

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Oil market movements have important implications for portfolio management and hedge strategies for investors who negotiate this commodity. Studies involving the relation of the CBO Crude Oil ETF Volatility Index (OVX) and the United States Oil Fund (USO) return are small in number and do not explore some aspects related to the asymmetry and nonlinearity of this relation. Therefore, this article proposes an analysis about the relation between return and volatility, using parametric and nonparametric methods. To do so, a daily data series from 2007 to 2016, ordinary least squares, quantile regressions and the nonparametric B-splines methods were used. The results indicated a negative, asymmetric and nonlinear contemporary relation between the variables. The effects of negative returns were more pronounced than the positive ones in volatility. In addition, it was found that the relation is not the same for different quantiles. Nonparametric estimates suggested that the positive returns have a convex profile and the negative returns have a concave profile. It indicated the downward-sloping reclined S-curve for the 0.05, 0.90 and 0.95 quantiles of volatility.

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“…As we all known, there are many factors that can improve the forecasting performance, for example, open interest [16][17][18], bid-ask spread [18][19][20], daily returns [18,[21][22][23][24]. To the best of our knowledge, we find that there have been a few studies investigating about these influencing factors in forecasting multifractal volatility.…”

Section: Introductionmentioning

confidence: 87%