Oil volatility risk and expected stock returns

Christoffersen, Peter; Pan, Xuhui

doi:10.1016/j.jbankfin.2017.07.004

Cited by 102 publications

(37 citation statements)

References 75 publications

(46 reference statements)

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“…Authors employ asymmetric causality approach and find the short run causal relationship between oil price and price level. Christoffersen and Pan (2017) analyzes the oil price volatility risk and expected stock returns. They find oil price volatility risk carries a significant risk-premiums per month and also report that a rise in oil price ambiguity causes the performance of financial intermediaries.…”

Section: Literature Analysismentioning

confidence: 99%

“…The average OVX price appears to be 37.59 indicates that investors' overreaction to crude oil price uncertainty was on the higher side. The average change of OVX and returns appears to be positive signifies overburden on the hedge fund, and a higher premium for the put USO-ETF options (e.g., Christoffersen & Pan, 2017). WTI crude oil price on an average calculated 81.39 with negative average returns.…”

Section: Quantile Regressionmentioning

confidence: 99%

“…It also implies that OVX changes are subject to price fluctuation of WTI spot prices. The changes in the OVX remains on the higher side when WTI fluctuates more and options based on WTI futures causes a higher amount of premium for put options (e.g., Christoffersen & Pan 2017). This kind of market rally results in higher pressure on the put options which outcomes into higher premium and consequently high level of OVX.…”

Section: Figure 1 Times Series Plot Of Ovx Wti and Usomentioning

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: Quantile Regressionmentioning

confidence: 99%

Section: Figure 1 Times Series Plot Of Ovx Wti and Usomentioning

confidence: 99%

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The Relation between Implied Volatility Index and Crude Oil Prices

Shaikh

2019

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“…A large body of the literature examined the relationship between oil price and equity markets [9,[20][21][22][40][41][42]. Mensi et al [43] studied the volatility transmission between the Standard and Poor's 500 Index (S&P 500) and the commodity markets.…”

Section: Equity and Commodities Markets And Oilmentioning

confidence: 99%

“…Mensi et al [43] continued to verify the interdependence between oil prices and major stock indices such as S&P 500 and Dow Jones Industrial Average (DJIA). Other studies also identified the return spillover from S&P 500 to WTI [42,44]. Moreover, Zhou et al [45] evaluated the co-movement between the volatility of the equity market proxied by S&P 500 and the oil market proxied by USO from 2007 to 2016.…”

Section: Equity and Commodities Markets And Oilmentioning

confidence: 99%

Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model

Lin

Chang

2020

Mathematics

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Oil continues to be a major source of world energy, but oil prices and funds have experienced high volatility over the last decade. This study applies the generalized autoregressive conditional heteroskedasticity-mixed-data sampling (GARCH-MIDAS) model on data spanning 1 July 2014 to 30 April 2020 to examine volatility transmission from the equity, bulk shipping, commodity, currency, and crude oil markets to the United States Oil Fund (USO) and BlackRock World Energy Fund A2 (BGF). By dividing the sample into two subsamples, we find a significant volatility transmission from the equity market to the oil ETF and energy fund both before and after the 2018 U.S.–China trade war. The volatility transmission from the bulk shipping, commodity, and crude oil markets turns significant for the oil ETF and energy fund after the 2018 U.S.–China trade war, extending into the COVID-19 pandemic in early 2020. The results suggest that investors can use the equity market to predict the movement of oil and energy funds during both tranquil and turmoil periods. Moreover, investors can use bulk shipping, commodity, and crude oil markets in addition to the equity market to forecast oil and energy funds’ volatility during the turmoil periods. This paper benefits investors against the high volatility of the energy funds.

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A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information

Wahab

Chevallier

2022

Journal of Forecasting

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This study is the first to examine the impacts of overnight and intraday oil futures cross‐market information on predicting the US stock market volatility the high‐frequency data. In‐sample estimations present that high overnight oil futures RV can lead to high RV of the S&P 500. Moreover, negative overnight returns are more powerful than positive components, implying the existence of the leverage effect. From statistical and economic perspectives, out‐of‐sample results indicate that the decompositions of overnight oil futures and intraday RVs, based on signed intraday returns, can significantly increase the models' predictive ability. Finally, when considering the US stock market overnight effect, the decompositions are still useful to predict volatility, especially during high US stock market fluctuations and high and low EPU states.

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Oil volatility risk and expected stock returns

Cited by 102 publications

References 75 publications

The Relation between Implied Volatility Index and Crude Oil Prices

The Relation between Implied Volatility Index and Crude Oil Prices

Volatility Transmission from Equity, Bulk Shipping, and Commodity Markets to Oil ETF and Energy Fund—A GARCH-MIDAS Model

A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information

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