Oil price uncertainty and Chinese stock returns: New evidence from the oil volatility index

Luo, Xingguo; Qin, Shihua

doi:10.1016/j.frl.2016.08.005

Cited by 140 publications

(83 citation statements)

References 16 publications

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“…He finds that OVX provides additional information to forecast the energy sector returns volatility than the historical returns of equity. Luo and Qin (2017) examine the impact of oil price volatility/shocks on the Chinese stock market. The empirical outcome shows that oil price shocks affect positively to the Chinese stock market.…”

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%

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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%

The Relation between Implied Volatility Index and Crude Oil Prices

Shaikh

2019

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“…Typically, research that has incorporated for oil price volatility uses a historical volatility measure. Luo and Qin (2017) used both a realized volatility measure along with the CBOE crude oil volatility index (OVX), which is a forward-looking oil price volatility measure. The results suggest that the OVX shocks have a significant negative impact on the Chinese stock market while the impact of realized volatility is negligible.…”

Section: Oil Price Volatility Measuresmentioning

confidence: 99%

“…Furthermore, consistent with the rationale introduced by Peng and Ng (2012) and Dupoyet and Shank (2018), we feel that that the OVX index provides information about future oil prices quicker than current oil prices themselves as the OVX captures market's aggregate expectation of future oil volatility. Although Luo and Qin (2017) and Dupoyet and Shank (2018) used a forward-looking oil price implied volatility measure to analyse the impact on stock returns, this is the first research paper to incorporate that forward-looking measure and study the impact on illiquidity premiums.…”

Section: Oil Price Volatility Measuresmentioning

confidence: 99%

Oil Price, Oil Price Implied Volatility (OVX) and Illiquidity Premiums in the US: (A)symmetry and the Impact of Macroeconomic Factors

Essa

Giouvris

2020

JRFM

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We examine the impact of oil price and oil price volatility on US illiquidity premiums (return on illiquid-minus-liquid stocks), using the US Oil Fund options implied volatility OVX index. We use daily data from 2007 to 2018, taking into account the structural break in June 2009 and controlling for macroeconomic factors. Both OLS and VAR models indicate that oil price has a significantly positive impact and OVX has a significantly negative impact on premiums, for the full sample and post-crisis period. These relationships are potentially driven by investor sentiments and market liquidity. Oil price has a negative impact on premiums during the crisis period. Using an autoregressive distribution lag model and an error correction model, we analyse long- and short-run elasticities. We find that oil price has a significantly positive impact on premiums both in the long- and short-run, for the full sample and post-crisis period. OVX only has a significantly negative impact in the short-run for the full sample. The reverting mechanism to establish long-run equilibrium is effective for the full sample and post-crisis period. Illiquidity premiums do not show any asymmetric responses to oil price changes but we do find evidence of asymmetric response to OVX changes.

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“…For China, Luo and Qin [14] found that oil price shocks positively influence Chinese stock returns, whereas oil price volatility shocks have a negative effect. Also, Sun et al [15] documented that fossil energy prices had a positive influence on new energy stock prices.…”

Section: Introductionmentioning

confidence: 99%

On the Linkage between the Energy Market and Stock Returns: Evidence from Romania

2019

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his paper aims to establish whether the Romanian energy market has an influence on the good running of the associated capital market. In order to achieve this objective, we approached a series of econometric techniques that allowed us to study the cointegration between variables, the presence of short-term or long-term causality relationships, and the application of impulse-response functions to analyze how the BET index responds to the shocks applied. The empirical findings from the Johansen cointegration test, ARDL model, and VAR/VECM models confirmed both the presence of a long-term and short-term relationship between the energy market and capital market. From all energy market indicators, only hard coal presented a causal relationship with the BET index. We also noticed a unidirectional relationship from the WTI crude oil to the Romanian capital market. Our findings should be of interest to researchers, regulators, and market participants.

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Oil price uncertainty and Chinese stock returns: New evidence from the oil volatility index

Cited by 140 publications

References 16 publications

The Relation between Implied Volatility Index and Crude Oil Prices

The Relation between Implied Volatility Index and Crude Oil Prices

Oil Price, Oil Price Implied Volatility (OVX) and Illiquidity Premiums in the US: (A)symmetry and the Impact of Macroeconomic Factors

On the Linkage between the Energy Market and Stock Returns: Evidence from Romania

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