Oil Price Returns Research Articles

Oil, natural gas and BRICS stock markets: Evidence of systemic risks and co-movements in the time-frequency domain

This paper uses the wavelet method to investigate co-movements between the five emerging stock markets of Brazil, Russia, India, China, and South Africa (BRICS), and the oil and natural gas markets. The results show co-movements between oil price and stock market returns at the lower scale or in the long-term. Coherence between oil and stock markets reaches maximal levels during the period of the 2008 financial crisis and the ensuing slow economic recovery from 2009. The strong co-movements between crude oil price returns and BRICS stock market returns take place after the removal of the effects of natural gas price returns. Multivariate coherency shows strong contributions of stock market returns and natural gas price returns to crude oil price returns. Given the less co-movements between natural gas and stock markets, natural gas provides higher diversification benefits. The risk is highest in the short-run compared to the medium-and long-term.

Re-examining oil and BRICS’ stock markets: new evidence from wavelet and MGARCH-DCC

ABSTRACT This study examines how the relationship between oil and stock market return of BRICS behaves at different investment horizons. Using data ranging from 2006 to 2020, the wavelet and MGARCH-DCC found that the stock markets’ return of Russia, Brazil, and South Africa are comparatively more correlated with oil price return across the investment horizons and more volatile particularly during the Covid-19 period. However, the stock markets’ return of China and India is less correlated with oil price return and less volatile. It is also revealed that oil price return leads the BRICS’ stock markets’ return and both are positively correlated.

The skewness of oil price returns and equity premium predictability

We show that the three-order moment of oil price returns can predict the aggregate stock market returns. Empirical results indicate the stock market returns forecasts generated by the skewness of oil price returns are statistically and economically significant for out-of-sample performance. We add the skewness of oil price returns as an additional predictor into the univariate macro model, and obtain greater forecast gains. When using multivariate information method, this prediction improvement also exists. Strong evidence demonstrates that the forecasting power is higher in recession. In addition, our finding is robust when considering alternative aversion coefficient and transaction cost.

Understanding cryptocurrency volatility: The role of oil market shocks

This study employs the generalized autoregressive conditional heteroscedasticity-mixed data sample framework to examine the role of oil market shocks in determining the long-term volatility of cryptocurrencies. We analyzed three cryptocurrencies, namely, Bitcoin, Ethereum, and XRP, and six oil market shocks, namely, oil price return (OR), oil realized volatility (ORV), oil realized skewness (ORSV), oil supply shocks (OSS), and oil demand shocks [global aggregate demand for industrial commodities (OADS) and crude oil market demand shocks (OSDS)]. The empirical results of the study reveal that OR, ORV, ORSV, and the two types of oil demand shocks (OADS and OSDS) and OSS have negative and positive impacts, respectively, on the long-term volatility of cryptocurrencies. We further gained insights on the economic underpinnings and observed that the ability of the oil market shocks to signal the uncertainty of the macroeconomic environment is clearly the most important source of its negative and significant impact on the volatility of cryptocurrencies. Moreover, an adverse oil market shock increases the attractiveness of cryptocurrencies, as they could provide shelter from sovereign risk and weakness. Overall, our study highlights a clear and comprehensive understanding of the association between the oil market risk and the cryptocurrency volatility dynamic and the possibility for cryptocurrencies to act as a hedge under specific uncertain economic conditions.

Spillover and quantile linkage between oil price shocks and stock returns: new evidence from G7 countries

The link between crude oil price and stock returns of the Group of Seven (G7) countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) was analyzed in this study using monthly data from January 1999 to March 2020. We adopt a similar approach to Kilian (Am Econ Rev 99(3):1053–1069, 2009) and construct a structural vector autoregression framework to decompose crude oil price shocks into oil supply shock, oil aggregate demand shock, and oil-specific demand shock. We then explore the distinct effects of different kinds of oil price shocks from various sources.Based on the decomposed oil price shocks, we apply the connectedness approach and QQ regression to find time-varying co-movements and tail dependence between oil price shocks and G7 stock returns.There is no general correlation between the decomposed oil prices and stock returns in these countries. The effects of oil price shocks on stock returns across different stock market conditions appear to be heterogeneous. Oil supply shock appears to be a net transmitter of spillover effects for all G7 countries within the sample period.

Evidence of speculative bubbles and regime switch in real estate market and crude oil price: Insight from Saudi Arabia

AbstractThe sector performances of many economies especially the oil‐producing ones, are consistently linked with the volatility of the international crude oil prices. Considering the significance of the international crude oil price to the economy of the Saudi Arabia, the current study investigates the dynamics of the crude oil price and the country's real estate market. This is achieved by investigating the evidence of both speculative bubbles and regime switching in the crude oil and real estate market from October 3, 2005, to March 29, 2018. As such, the Supremum Augmented Dickey–Fuller (SADF) and Markov switching regression model were employed. Although there is no significant evidence of speculative bubbles in the Saudi Arabia's real estate market, the result reveals statistical significant evidence of price bubbles in the Brent crude oil price especially between the period of 2007 to 2008, which coincides with the Global Financial Crisis period. However, the Markov switching investigation reveal that there is evidence of significant and positive impact of crude oil price return on the real estate market in the regimes. Importantly, empirical evidence shows that the impact is higher in Regime 2 even as the global factor (proxy volatility index) is evidently significant. Although the regimes are persistent, the expected regime duration of the stable regime is of the higher quarter. This investigation is statistically significant and robust, especially when Organization of the Petroleum Exporting Countries (OPEC) price of crude oil is used in lieu of the European Brent price. The estimation result encourages more strict energy policies that tackle the Saudi Arabia's energy and the real estate challenges.

The dynamics of crude oil price and the real estate market in Saudi Arabia: A Markov‐switching approach

The volatility evidence of the International crude oil prices has overtime been linked to the dynamics and sector performances of states' economies. By employing the Markov switching regression model over daily time series data from October 3, 2005 to March 29, 2018, this study examines the response of the Saudi Arabia real estate market to the crude oil price dynamics. An empirical observation revealed that there is statistical evidence of significant and positive impact of crude oil price return on the real estate market in the regimes. Am empirical evidence shows that the impact is higher in Regime 2 even as the global factor (proxied by volatility index) is evidently significant. Although the regimes are persistent, the expected regime duration of the stable regime is of the higher quarter. This investigation is statistically significant and robust, especially when OPEC price of crude oil is used in lieu of the Europe Brent price. By a further estimation techniques, it affirms that the model exhibits nonlinearity rather than a linear relationship. The estimation result encourages more strict energy policies such as the implementetion of energy diversification policy among others, thus rendering a panacea to Saudi Arabia's energy and the real estate challenges.

Is Exchange Rate Volatility Symmetric to Oil Price Volatility? An Investigation for India

The studies involving finding a relation between oil prices and the exchange rate have often looked the relationship when the oil price was rising. Will the impact mirror for declining oil prices too? Or how the exchange rate behaves when oil prices are just volatile without any appreciable change in price. This study contributes to the literature to see the effect of oil prices on the exchange rate for different episodes for India using daily data for twenty years period. The study finds that the return of oil price and exchange rate relationship exhibit time-varying volatility in five of the total ten sub-periods in the last 20 years. GARCH and EGARCH models are then employed to study the impact of oil price shock on the nominal exchange rate for those periods. The study finds (a) not all periods have varying volatility, (b) for two of the volatile period, an increase in the oil price return leads to depreciation of the Indian currency vis-a-vis US dollar, (c) in line with other studies, we find that shocks to exchange rate have an asymmetric effect, and (d) oil price shocks have a permanent effect on exchange rate volatility.

THE RELATIONSHIP BETWEEN CRUDE OIL PRICES AND STOCK MARKET: THE INDIAN CASE

Crude oil is influencing every productivity activity of human life either directly or indirectly. The prices of crude oil also influence the international financial markets. This influence connects the oil market with that of capital market since stock market provides it necessary resources for investment and financing the production. In this study, the objective was to test the relationship between crude oil prices and selected sectors of Indian economy. Furthermore, the study also tests the effects of international crude oil prices on the Indian stock exchange market. The main objective was to test the conditional correlation of the crude oil price and equity returns of selected sectors of the Indian economy as well as the performance indicators of the Indian stock market using bivariate volatility models.

PETROL FİYATLARI OYNAKLIĞININ OTOREGRESİF KOŞULLU DEĞİŞEN VARYANS MODELLERİ (ARCH/GARCH) İLE İNCELENMESİ

Oil prices have had a significant volatility over the past century as a result of changes in international economic and political balances. Because oil is a major source of energy and is not evenly distributed among countries, it now has a strategic importance for each country. The aim of this study is to analyze the volatility of global oil prices with the Autoregressive Conditional Variance Models (ARCH/GARCH). In this direction, European Brent oil prices based on June 1987- June 2018 business day basis were used as data in the study. According to the results of analysis, it is seen that TARCH (1,1) model is the best volatility estimation model among different ARCH/GARCH type models. According to the model: I) Oil prices are positively affected by the previous period. II) The impact of shocks on oil price return does not spread over a long period. III) Volatility is generally high, so instability is dominant in prices. IV) Negative shocks on oil price return are more effective than positive shocks.

Product market competition, oil uncertainty and corporate investment

PurposeThe purpose of this study is to examine the effect of product market competition on the oil uncertainty–investment relation.Design/methodology/approachThe authors use firm-level financial data from the COMPUSTAT database, competition proxies from Hoberg and Phillips (2016) and macroeconomic data on crude oil price uncertainty. Corporate investment is measured as capital expenditure scaled by total assets or as the annual change in (net) total fixed assets plus depreciation. Since our panel data covers a short period (22 years) and the regressions include a combination of a lagged dependent variable and firm fixed effects, the authors apply Blundell and Bond’s (1998) GMM system when regressing corporate investment on the interaction between oil uncertainty and competition.FindingsConsistent with the theories in the irreversible investment literature, the authors first show that investments are negatively related to oil uncertainty. Second, they show that firms in competitive industries decrease their investments in response to heightened uncertainty by a higher degree than firms in concentrated industries, suggesting that competition can exacerbate negative investment outcomes when success is uncertain. The authors also examine how competition relates to investment asymmetric reactions to positive and negative oil price return volatilities and find a stronger negative relationships between competition and investment-positive oil price volatility, indicating that increasing the probability of a negative outcome due to uncertainty leads firms to reduce investment to a larger extent.Practical implicationsThe findings provide useful insights to guide corporate investment decisions under oil price change uncertainty. In particular, if firms can wait for the resolution of uncertainty before deciding to pursue irreversible investment in a competitive market, they can avoid potentially large losses by foregoing investment when the outcomes are unfavorable. This is because competition brings a greater uncertainty to firm performance if the investment outcome is poor, as firms in competitive industries share a large proportion of industry-wide profits with rivals and, thus, competition could erode profit margins and increases the likelihood of being driven out of the market. Hence, firms in competitive markets should balance between strategic preemptive motives and waiting for the resolution of uncertainty before deciding to pursue investment.Originality/valueThis study is the first to examine the effect of competition on the relationship between investment and oil price uncertainty. Moreover, it is the first to examine the effect of competition on the asymmetric response of investment to oil price uncertainty emanating from positive and negative changes in oil price.

Nonlinear Granger causality between oil price and stock returns in India

The nonlinear causal dimension in oil price and stock returns aspect is less explored in literature. This study provides such evidence by applying Hiemstra and Jones (1994) nonlinear Granger causality test to the VAR residuals in case of India. Our result indicates that there exists bi‐directional nonlinear causality between oil price and stock returns. It implies that the lagged information of oil price and stock returns can be able to predict each other efficiently.

Causal Relationships and Short-Term Dynamics Between Oil Price and Stock Market Returns in Malaysia

Causal Relationships and Short-Term Dynamics Between Oil Price and Stock Market Returns in Malaysia

DO DIFFERENT TYPES OF OIL PRICE SHOCKS AFFECT THE INDIAN STOCK RETURNS DIFFERENTLY AT FIRM-LEVEL? A PANEL STRUCTURAL VECTOR AUTOREGRESSION APPROACH

In this paper, we investigate the dynamic relationship between different oil price shocks and Indian stock returns at firm level, using variable-structural VAR approach for the period 1995:01 to 2018:12. We use large unbalanced panel of 1768 manufacturing energy-intensive and non-manufacturing energy-intensive firms listed in the NSE. The estimation results depict that stock returns of India deteriorate due to disruptions in oil supply. In response to aggregate demand shock, stock returns and oil price move in opposite direction, whereas for speculative demand shock, oil price and stock returns have similar reactions. We also use GMM technique since our model suffers from endogeneity, thanks to the use of panel data. Since not all oil price shocks are alike, policy makers and investors should look into all aspects and sources of oil price shocks that impact stock returns, and make appropriate policy and investment decisions. From impulse response function, the effect is again cyclical as one could witness ups and downs in stock returns. This is because domestic oil price is partially dependent upon the status of subsidiaries and taxes. Also, inflation does not depend just upon oil price shocks and its sources, but it depends on other shocks such as inflation shock as well.

Revisiting the Role of Economic Uncertainty in Oil Price Fluctuations: Evidence from An Extension of the Classic Kilian (2009) Model

We explore the role of economic uncertainty shocks in oil prices by establishing a new time-varying parameter oil market model based on Kilian (2009). The main conclusions are as follows. First, according to the two-stage nonlinear testing that we propose in this paper, the variables in the oil market model are significantly nonlinearly correlated; therefore, we extend the constant coefficient oil market model using time-varying parameterisation. Second, the time-varying impulse responses indicate that the effects of economic uncertainty shocks on crude oil price returns are time-varying and tend to run counter to the oil market cycle. Third, the time-varying forecast error variance decomposition infers that economic uncertainty shocks can contribute more than fundamental shocks to variations in oil price returns under certain market conditions, such as during the global financial crisis period in 2008. Fourth, during the recent coronavirus crisis of 2020, economic uncertainty shocks and oil demand shocks are two important contributors to the variations in oil price returns, with economic uncertainty shocks contributing to about 40% and demand shocks contributing to about 50% of the variations.

Shock and Volatility Spillovers between Crude Oil Price and Stock Returns: Evidence for Thailand

This paper employs a bivariate BEKK-GARCH(1,1) model to examine shock and volatility spillovers between crude oil and stock markets by taking into account the impact of the 2008 global financial crisis. Daily data from crude oil market and the Thai stock market during February 6, 2004 and September 14, 2015 are used in the analyses. The whole sample is divided into the pre- and post- crisis periods. The results show that there are no spillover effects between oil price and stock returns in the pre-crisis period. In the post-crisis period, there are unilateral spillover effects from oil price to some equity sector returns. In the market level, there are unilateral spillovers of shock and volatility from oil price to stock market return. The findings in this paper are crucial for financial market participation to understand shock and volatility transmissions from oil to stock markets such that portfolio management should take into account the presence of oil price risk.

Summary of the Paper Entitled: Forecasting Fuel Prices with the Chilean Exchange Rate

This draft is a summary of the paper entitled: Forecasting Fuel Prices with the Chilean Exchange Rate. In that paper we show that the Chilean exchange rate has the ability to predict the returns of oil prices and of three additional oil-related products: gasoline, propane and heating oil. The theoretical underpinnings of our empirical findings rely on the present-value theory for exchange rate determination and on the strong co-movement displayed by some commodity prices. The Chilean economy is heavily influenced by one particular commodity: copper, which represents nearly 50% of total national exports and attracts a similar share in terms of Foreign Direct Investment. As a consequence, the floating Chilean exchange rate is importantly affected by fluctuations in the copper price. As oil-related products display an important co-movement with base metal prices, it is reasonable to expect evidence of Granger causality from the Chilean peso to these oil-related products. We find substantial evidence of predictability both in-sample and out-of-sample. Our paper is part of a growing literature that in the recent years has explored the linkages between commodity prices and commodity currencies.

Asymmetric effects of oil price uncertainty on corporate investment

Using a comprehensive data set of a large panel of US firms over 1984–2017, we show that the negative effect of crude oil price return uncertainty on investments is asymmetric. In particular, we show that investment is more significantly reduced following the volatility of positive oil price changes than that of negative changes, and this asymmetric effect is more pronounced in small firms. In addition, we find that the asymmetric effect is stronger in the crude oil- and gas-producing companies where the effect of the volatility of negative oil price changes is greater than that of positive oil price changes. We also find that the impact of positive or negative oil price change uncertainty on investments substantially differs across different industries. The results are quite robust to the way in which we measure the investment and oil price uncertainty and to alternative regression methods.

Global energy and geopolitical risk: behavior of oil markets

PurposeThe question being assessed is whether changes in the degree of global geopolitical risk (GPR), as defined by the framework developed by Iacoviello (2018), can be used to improve allocative efficiency, thereby increasing investment returns on oil commodities.Design/methodology/approachUsing the linear and nonlinear model, this paper analyzes the impact of GPR on returns of oil prices (BRENT, WTI and Organization of Petroleum Exporting Countries), as well as the short- and long-run relationship between GPR and oil prices.FindingsThe results of the impulse response function indicates that oil prices do not respond to shocks in GPR. The results of the Granger causality test show that oil returns are not caused by GPR. The regression analysis and autoregressive distributed lag results show that there is no significant impact of GPR on the returns of oil.Originality/valueThis is unique among the literature in that it identifies and isolates the relationship between GPR and oil market pricing. Insight into the lag in market response and the degree to which GPR can be used to estimate oil prices using curvilinear models are derived from the analysis.

Testing the oil price efficiency using various measures of long-range dependence

In this paper, we empirically examine the application of a host of techniques employed to measure price efficiency through long-range dependence using prices of monthly oil contracts. Using a series of methods, we analyse the volatility (daily absolute returns) of WTI and Brent oil prices for nine different contracts with maturity, ranging from 1 to 9 months, during the sample period of 1990-2017. We use bootstrapping to compute the confidence interval of the parameter of long-range dependence. Our results indicate that on an average, there is no long-range dependence in the volatility of oil price contracts at least at the 10-percent level of significance. Moreover, our results of rolling estimates suggest that the normality assumption does not affect the results considerably, and the results are robust to different rolling window sizes. While the results of the efficiency index suggest that the efficiency of oil returns vary with time, the futures contracts for Brent oil are found to be less efficient compared to WTI oil. The long-term futures contracts are more efficient than short-term contracts.