Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information

Lin, Jeng-Bau; Liang, Chin-Chia; Tsai, Wei Der

doi:10.3390/su11143906

Cited by 10 publications

(6 citation statements)

References 61 publications

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“…Notably, these results are not sensitive to lag-length defining the QAR specification, under the null hypothesis of no Granger causality. These findings are partially in line with the result of Lin et al [71], who report that oil prices have a bi-directional relation with OVX. Similarly, in a different application, Liu et al [17] find a significant bi-directional implied volatility transmission between oil and US stock markets.…”

Section: Granger Causality Test Resultssupporting

confidence: 91%

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

Raggad

Bouri

2023

Mathematics

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We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants.

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Section: Granger Causality Test Resultssupporting

confidence: 91%

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

Raggad

Bouri

2023

Mathematics

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“…Therefore, there was a highly negative relationship between these two variables (the correlation coefficient between OVX and oil price was −0.905, and that between VIX and oil price was −0.749). Similar to this study, Reference [57] focused on the rapid devaluation and revaluation of the US dollar which mainly pushed up the international Brent crude oil price in the period of 2014-2017.…”

Section: Correlation Analysismentioning

confidence: 92%

“…As stated above, if the F value is between the upper limit and lower limit, whether to accept or reject the null is inconclusive so that this test cannot be used. In addition, the use of this test is inappropriate if the time series data shows nonlinear characteristics [57].…”

Section: Ardl-ecm Cointegration Modelmentioning

confidence: 99%

The Relations of Oil Price Change with Fear Gauges in Global Political and Economic Environment

Lin

Tsai

2019

Energies

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The oil price time series data can be affected by major global political and economic events, which would result in structural changes that could lead to biased estimations. By adopting the Bai and Perron model this paper found that there were six structural breaks in the Brent oil price due to major global events and that ARDL-ECM cointegration exists only between oil price and stock market volatility index (VIX) throughout the sampling period. However, cointegration relations were found between oil price and Crude Oil Volatility Index (OVX) in the second and fourth sub-periods, and also between oil price and VIX in the second, third, fourth, sixth, and seventh sub-periods. Furthermore, the cointegration relation coupled with correlation analysis indicates a long-term equilibrium positive (negative) relation between the two variables. Such relations existed between the price and the two fear gauges, respectively, only for some specific sub-periods, implying that OVX seemed to be better than VIX in predicting oil price changes. We suggest that the investors in the global oil market must pay attention to not only the impacts of major global political and economic events on oil price, but also the positive or negative correlations between oil price and fear gauge.

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“…OVX serves as an unbiased, but not an efficient estimate of the future realized volatility and it includes information on the future realized volatility. Rising OVX had a greater negative impact on the oil prices than the declining OVX, thus indicating that a long-run, asymmetric cointegration exists between them [26]. The results of linear Granger causality showed that OVX led the oil price in one direction.…”

Section: Introductionmentioning

confidence: 92%

Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic

Echaust

Just

2021

Energies

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This study investigates the dependence between extreme returns of West Texas Intermediate (WTI) crude oil prices and the Crude Oil Volatility Index (OVX) changes as well as the predictive power of OVX to generate accurate Value at Risk (VaR) forecasts for crude oil. We focus on the COVID-19 pandemic period as the most violate in the history of the oil market. The static and dynamic conditional copula methodology is used to measure the tail dependence coefficient (TDC) between the variables. We found a strong relationship in the tail dependence between negative returns on crude oil and OVX changes and the tail independence for positive returns. The time-varying copula discloses the strongest tail dependence of negative oil price shocks and the index changes during the COVID-19 health crisis. The findings indicate the ability of the OVX index to be a fear gauge with respect to the oil market. However, we cannot confirm the ability of OVX to improve one day-ahead forecasts of the Value at Risk. The impact of investors’ expectations embedded in OVX on VaR forecasts seems to be negligible.

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Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information

Cited by 10 publications

References 61 publications

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

The Relations of Oil Price Change with Fear Gauges in Global Political and Economic Environment

Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic

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