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The Effects of Oil Price Shocks on Stock Market Volatility: Evidence from European Data

Stavros Degiannakis, George Filis, and Renatas Kizys

Year: 2014
Volume: Volume 35
Number: Number 1
DOI: 10.5547/01956574.35.1.3
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Abstract:
The paper investigates the effects of oil price shocks on stock market volatility in Europe by focusing on three measures of volatility, i.e. the conditional, the realized and the implied volatility. The findings suggest that supply-side shocks and oil specific demand shocks do not affect volatility, whereas, oil price changes due to aggregate demand shocks lead to a reduction in stock market volatility. More specifically, the aggregate demand oil price shocks have a significant explanatory power on both current-and forward-looking volatilities. The results are qualitatively similar for the aggregate stock market volatility and the industrial sectors' volatilities. Finally, a robustness exercise using short-and long-run volatility models supports the findings.



Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence

Stavros Degiannakis, George Filis, and Vipin Arora

Year: 2018
Volume: Volume 39
Number: Number 5
DOI: 10.5547/01956574.39.5.sdeg
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Abstract:
Do oil prices and stock markets move in tandem or in opposite directions? The complex and time varying relationship between oil prices and stock markets has caught the attention of the financial press, investors, policymakers, researchers, and the general public in recent years. In light of such attention, this paper reviews research on the oil price and stock market relationship. The majority of papers we survey study the impacts of oil markets on stock markets, whereas, little research in the reverse direction exists. Our review finds that the causal effects between oil and stock markets depend heavily on whether research is performed using aggregate stock market indices, sectorial indices, or firm-level data and whether stock markets operate in net oil-importing or net oil-exporting countries. Additionally, conclusions vary depending on whether studies use symmetric or asymmetric changes in the price of oil, or whether they focus on unexpected changes in oil prices. Finally, we find that most studies show oil price volatility transmits to stock market volatility, and that including measures of stock market performance improves forecasts of oil prices and oil price volatility. Several important avenues for further research are identified.Keywords: Oil prices, oil price volatility, stock markets, interconnectedness, forecasting, oil-importers, oil-exporters



The (time-varying) Importance of Oil Prices to U.S. Stock Returns: A Tale of Two Beauty-Contests

David C. Broadstock and George Filis

Year: 2020
Volume: Volume 41
Number: Number 6
DOI: 10.5547/01956574.41.6.dbro
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Abstract:
We evaluate the probability that oil prices affect excess stock returns for U.S. listed firms. The probabilities are obtained from a time-varying multi-factor asset pricing framework estimated using dynamic model averaging techniques, including oil price information among several other possible risk factors. Two widely used oil price measures are considered, one based on raw oil price changes and another based on disentangling the source of oil price changes due to supply-side or demand-side effects. As far as we know our dataset, which comprises 10,118 stock price series with up to 25,372,588 observations between 1995�2018, is the most comprehensive used for this purpose. We develop two �beauty-contests� in which we estimate the multi-factor models separately for individual stocks, for each of the two oil price measures. The results suggests that, when working with daily data (beauty contest 1), oil price changes are a significant (important) determinant for around 1�3% of the sample. When using oil price shocks�as opposed to oil price changes�(beauty contest 2) this percentage increases to 27�45%, suggesting that oil supply and demand shocks (as opposed to oil price changes) can better explain firm-level excess returns, at least for monthly frequency data where such a decomposition is available. We provide evidence that the increase in percentage is only partially attributable to data-frequency, and more likely attributed to the decomposition into supply/demand driven oil price changes. We reconcile differences between our findings and those reported in previous literature on the basis of the fully dynamic nature of our adopted methodology.



Oil price volatility is effective in predicting food price volatility. Or is it?

Ioannis Chatziantoniou, Stavros Degiannakis, George Filis, and Tim Lloyd

Year: 2021
Volume: Volume 42
Number: Number 6
DOI: 10.5547/01956574.42.6.icha
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Abstract:
Volatility spillovers between food commodities and oil prices have been identified in the literature, yet, there has been no empirical evidence to suggest that oil price volatility improves real out-of-sample forecasts of food price volatility. In this study we provide new evidence showing that oil price volatility does not improve forecasts of agricultural price volatility. This finding is based on extensive and rigorous testing of five internationally traded agricultural commodities (soybeans, corn, sugar, rough rice and wheat) and two oil benchmarks (Brent and WTI). We employ monthly and daily oil and food price volatility data and two forecasting frameworks, namely, the HAR and MIDAS-HAR, for the period 2nd January 1990 until 31st March 2017. Results indicate that oil volatility-enhanced HAR or MIDAS-HAR models cannot systematically outperform the standard HAR model. Thus, contrary to what has been suggested by the existing literature based on in-sample analysis, we are unable to find any systematic evidence that oil price volatility improves out-of-sample forecasts of food price volatility. The results remain robust to the choice of different out-of-sample forecasting periods and three different volatility measures.





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