Examination on the Relationship between OVX and Crude Oil Price with Kalman Filter

Chen, Yanhui; Zou, Yingchao

doi:10.1016/j.procs.2015.07.122

Cited by 17 publications

(15 citation statements)

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“…They find significant casualty relations from the oil market to gas in 'put options' based select samples. Chen and Zou (2015) examine the CBOE OVX and crude oil price in a time-varying coefficient by employing the Kalman filter framework. Unlike a strong negative correlation between VIX and SPX, they find a weak negative asymmetric relationship between OVX and oil price returns.…”

Section: Literature Analysismentioning

confidence: 99%

“…Recent studies in developed and emerging markets (e.g., Martens & Zein, 2004;Lin et al, 2013;Aboura & Chevallier, 2013;Haugom et al, 2014;Chen & Zou, 2015;Maghyereh et al, 2016;Dutta, 2017 andQin, 2017) specifically they examine the expected oil price volatility in terms of OVX. They analyze the relationship between OVX and WTI futures returns and spillover effects of crude oil price in developed and other emerging market based volatility indices.…”

Section: Literature Analysismentioning

confidence: 99%

“…The result implies an asymmetric association between volatility and crude oil futures prices, e.g. Narayan and Narayan (2007) and Chen and Zou (2015). Figure 1 is the time series plot of OVX and crude oil price futures in terms of WTI and USO.…”

Section: Quantile Regressionmentioning

confidence: 99%

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The Relation between Implied Volatility Index and Crude Oil Prices

Shaikh

2019

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Crude oil is a global commodity traded across the world market. The prices of the commodity over an extended period for crude oil have been analyzed using daily prices of crude oil futures and the implied volatility index (OVX). This paper aims to find the predictability of various parameters on the basis of time using neural network and quantile regression methods. Several estimates have been shown based on Barone, Adesi, and Whaley’s (BAW) model of neural network. Estimation parameters include opening, closing, highest and lowest price of the commodity and volumes traded for a given commodity on each trading day. The neural network estimates explain that future prices of the WTI/USO can be predicted with minimal error, and similar can be used to predict future volatility. The quantile regression results suggest that crude oil prices and OVX are strongly associated. The asymmetric association between the WTI/USO and OVX explains that the volatility feedback effect holds good for the OVX market. Bai and Perron least squares estimate evidence of the presence of a break in the time series. The main results uncover several interesting facts that implied volatility tends to remain calm during the global financial crises and higher throughout the post crisis period. The empirical outcome on the OVX market provides some practical implications for the trader and investor, in which oil futures can serve better to hedge the crude price volatility. The crude oil producer can short hedge enough through volatility futures and options to maintain the future quantity of crude to be produced.

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Section: Literature Analysismentioning

confidence: 99%

Section: Literature Analysismentioning

confidence: 99%

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The Relation between Implied Volatility Index and Crude Oil Prices

Shaikh

2019

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“…Authors in [9] found that the lead-lag relation between the oil price and its volatility is central to any type of trading strategy based on futures and options on the OVX. Authors in [10] further pointed out that there is a negative and asymmetric relationship between OVX changes and crude oil price returns. Authors in [11] also documented that there is a significant, negative relationship between OVX changes and West Texas Intermediate (WTI) returns, and that the asymmetry relationship between them means the OVX has a greater predictive effect of gauging an investor's fear rather than risk preference.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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This paper investigates the linear/nonlinear long-run and short-run dynamic relationships between oil prices and two implied volatilities, oil price volatility index (OVX) and stock index options volatility index (VIX), representing panic gauges. The results show that there is a long-run equilibrium relationship between oil prices and OVX (VIX) using the linear autoregressive distributed lag (ARDL)-bounds test. Likewise, while using the nonlinear autoregressive distributed lag (NARDL)-bounds test, not only does a long-run equilibrium relationship exist, but also the rising OVX (VIX) has a greater negative influence on oil prices than the declining OVX (VIX), thus indicating that a long-run, asymmetric cointegration exists between the variables. Furthermore, OVX (VIX) oil prices have a linear Granger causality, while for the nonlinear Granger causality test, oil prices have a bidirectional relation with OVX (VIX). In addition, we find that once major international political and economic events occur, structural changes in oil prices change the behavior of oil prices, and thus panic indices, thereby switching from a linear relationship to a nonlinear one. The empirical results of this study provide market participants with more valuable information.

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“…Also the static coefficients cannot reflect the immediate changes of two sides, which may reduce the forecasting accuracy. Many researchers have applied the Kalman filter to evaluate the dynamic evolvement (see [19][20][21][22] and [23] etc.). Using time-variant parameters obtained from Kalman filter can reveal the hidden dynamic relationship and evaluate it more accurately.…”

Section: Introductionmentioning

confidence: 99%

The Information Content of OVX for Crude Oil Returns Analysis and Risk Measurement: Evidence from the Kalman Filter Model

Chen

2015

Ann. Data. Sci.

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Crude oil volatility index (OVX) is a new index published by Chicago BoardOption Exchange since 2007. In recent years it emerged as an important alternative measure to track and analyze the volatility of future oil prices. In this paper we firstly model and analyze the dynamic relationship between OVX changes and future crude oil price returns with time-varying coefficients, modeled using the Kalman filter, in the regression models. Empirical results show a weak negative relationship between OVX changes and future crude oil price returns movement, and extremely high/low levels of OVX cannot predict future positive/negative returns well. Secondly, this paper explores whether OVX can predict future realized volatility of crude oil price returns . The empirical findings suggest that OVX serves as an unbiased but not an efficient estimate of the future realized volatility and it includes information of the future realized volatility. Finally the incorporation of information of OVX in measuring market risk is analyzed. The empirical result indicates that Kalman filter based model provides the improved performance than the linear regression model in terms of forecasting accuracy for realized volatility prediction and the reliability for VaR estimate. B Kaijian He

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Examination on the Relationship between OVX and Crude Oil Price with Kalman Filter

Cited by 17 publications

References 15 publications

The Relation between Implied Volatility Index and Crude Oil Prices

The Relation between Implied Volatility Index and Crude Oil Prices

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

The Information Content of OVX for Crude Oil Returns Analysis and Risk Measurement: Evidence from the Kalman Filter Model

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