Oil Price Returns Research Articles

A threshold effect of COVID-19 risk on oil price returns

Using U.S. data, we investigate how the COVID-19 pandemic influences oil price returns in an asset pricing framework. Unlike earlier studies, we consider a threshold model to allow for the possibility that COVID-19 risk may not play a role until it reaches a certain level. Based on WTI crude oil spot price data from January 2020 to December 2021, our findings show that oil returns significantly decline with the daily number of COVID-19 deaths but only if the daily death toll exceeds approximately 2100. In addition, a more severe COVID-19 pandemic can substantially increase the exposure of oil returns to various systematic risk factors, which has not been documented in previous literature.

Forecasting volatility in oil returns using asymmetric GARCH models: evidence from Tanzania

Crude oil is, without a doubt, one of the most significant commodities in the modern world. The highly contagious coronavirus, the conflict between Ukraine and Russia, and not to mention the unusual turn of events worldwide have all significantly impacted crude oil prices. Since oil is required for all critical economic activities, such as production and transportation, a forecast for crude oil prices is essential. Using a range of GARCH models at such an intense time, this study attempted to close this gap by forecasting crude oil volatility. To forecast the returns of Brent crude oil prices from January 2002 to February 2022, this study uses a family of GARCH models. In the respective family of models, GJRGARCH (1,1) was the most effective in predicting the volatility of crude oil prices. The GJRGARCH model was chosen since it had a higher likelihood value and a lower information criteria value. A diagnostic check was done to evaluate the produced model further to ensure that the proposed model was good enough for forecasting crude oil volatility. The study suggests employing the GJRGARCH technique to predict future fluctuations in exceptional circumstances.

Effects of Crude Oil Price Shocks on Stock Markets and Currency Exchange Rates in the Context of Russia-Ukraine Conflict: Evidence from G7 Countries

The present study examines the effects of the steep surge in crude oil prices which has also been considered as an oil price shock on the stock price returns and currency exchange rates of G7 countries, namely Canada, France, Germany, Italy, Japan, the United Kingdom (UK) and the United States (US), in the context of the Russia–Ukraine conflict. Due to the outbreak of the war, the steep surge in Brent crude oil price returns is seen as an exogenous shock to stock price returns and exchange rates during the period from 2 January 2017 to 29 June 2022. The paper applies the Fractionally Integrated GARCH (FIGARCH) model to capture the effect of the crude oil price shock and the Breakpoint unit root test to examine the structural breaks in the dataset. Structural breakpoints in the dataset for the entire stock price returns and exchange rates are observed during the period commencing from the last week of February, 2022, to the last week of March, 2022. Except for TSX, NASDAQ and USD, noteworthy long memory effects running from Brent crude oil price to all the stock price returns along with the currency exchange rates for all G7 countries were also found.

Do U.S. economic conditions at the state level predict the realized volatility of oil-price returns? A quantile machine-learning approach

Because the U.S. is a major player in the international oil market, it is interesting to study whether aggregate and state-level economic conditions can predict the subsequent realized volatility of oil price returns. To address this research question, we frame our analysis in terms of variants of the popular heterogeneous autoregressive realized volatility (HAR-RV) model. To estimate the models, we use quantile-regression and quantile machine learning (Lasso) estimators. Our estimation results highlights the differential effects of economic conditions on the quantiles of the conditional distribution of realized volatility. Using weekly data for the period April 1987 to December 2021, we document evidence of predictability at a biweekly and monthly horizon.

THE CO-MOVEMENT OF CHINA AND US STOCK INDICES: A PORTFOLIO DIVERSIFICATION ANALYSIS

The aim of this article is to find diversification opportunities by examining the time-varying and time-scale-based volatility and correlation of the US and Chinese stock market indices with crude oil, gold and Bitcoin price returns, as well as the exchange rate of the Chinese Yuan Renminbi against the US Dollar (CNY/USD) using a vector error correction model (VECM), namely, maximum overlap discrete wavelet transformation (MODWT). Furthermore, individual and institutional investors may also reduce the risk of their investment portfolio by investing in commodities and stock markets from countries with a negative or substantially low correlation. Our VECM result shows that Bitcoin, crude oil and CNY/USD lead the other variables under consideration, indicating that changes in the prices of Bitcoin, crude oil and CNY/USD affect the US and Chinese stock market indices, as well as gold. Our research utilising the MODWT technique shows that Bitcoin leads crude oil at almost all levels, indicating that crude oil prices will respond to Bitcoin price movement in the long and medium term. However, investors may be deterred from using Bitcoin as a diversification tool due to its extreme volatility. The research also indicates that diversification with gold may help US investors. However, the continuous wavelet transformation finding shows that the diversification benefit effects will persist for a holding period of little more than 64 days. Our study results tend to emphasise the significance of using reasonably modern methods to identify diversification possibilities for investors with diverse investment horizons or holding stocks for various periods.

Can implied volatility predict returns on oil market? Evidence from Cross-Quantilogram Approach

This paper employs the Cross-Quantilogram methodology proposed by Han et al. (2016) to investigate whether the implied volatility of crude oil (OVX) ameliorates the directional predictability of the oil price returns. Our main result documents the existence of a quantile predictability from OVX to WTI returns when the crude oil implied volatility is in the upper conditional quantile. On the other hand, no sufficient evidence in directional predictability is found when the OVX is at the lower to intermediate level. As a part of robustness check, the same analysis was conducted to the Brent returns. The pattern of directional predictability (similar signs) broadly corresponds with those observed on the WTI returns, however, it seems that OVX is more predictive for Brent than WTI. Particularly, the predictive power of OVX is found to be significant during periods of moderate to high OVX and mainly concentrated on lower to intermediate variations in Brent returns. As a result, implied volatility can be considered as a driver with respect to the forthcoming variations of the returns in the spot oil market. Insights gleaned from this study could have important implications for investors and policymakers in terms of portfolio and risk management decisions.

Inventory information arrival and the crude oil futures market

AbstractThe objective of this paper is to provide a deeper insight into the impact of weekly inventory announcements and especially their associated surprises on crude oil returns and volatility during the period from 27 March 2012 to 2 October 2018. The results can be summarized as follows. First, we find that all inventory surprises exert an inverse impact on the oil price returns. Such impact appears more pronounced during the collapse and post‐collapse periods, than during the pre‐collapse period. Second, we show that the reducing volatility effects of inventory shocks that we establish for the whole period are entirely attributed to those observed in the pre‐collapse period. Third, the different effects obtained for positive and negative shocks support evidence that our split of inventory news by nature matters. Fourth, compared to macroeconomic and unconventional monetary news, the inventory statements appear as the most pertinent news during the event window. Fifth, we find evidence also that the effect of the uncertainty about economic stance, when it is significant, changes the way the oil prices and volatility react to inventory surprises, and hence weakens the effectiveness of the inventory policy. Lastly, we make an original use of the information content of the implied volatility to assess the persistence of the link between the conditional variance and the inventory news. The central finding is that the implied volatility enters with a significant effect on the conditional variance for WTI oil returns and namely with a reduced volatility persistence. Also, a significant effect from inventory surprises namely when we split the announcements into negative and positive news is detected even the implied volatility is introduced exogenously in the GARCH‐type variance specification highlighting the predominance of our main variable.

Oil prices, news-based uncertainty measures and exchange rate returns in BRICS countries

Purpose This paper aims to accommodate the influence of both economic policy uncertainty and geopolitical risks in the relationship between oil price and exchange-rate returns in the Brazil, Russia, India, China and South Africa (BRICS) countries through an interaction term (EPGR). Design/methodology/approach The authors use continuous wavelet transform (CWT), wavelet coherence (WC) and partial wavelet coherence (PWC). First, the authors apply the CWT to examine the evolution of oil prices, EPGR and exchange rate returns. Second, the authors use WC to investigate the relationship between oil price and exchange rate returns (excluding EPGR). Third, the authors use PWC to account for EPGR’s impact on the oil exchange rate returns dynamics and explore partial correlations in the oil and exchange rate returns dynamics. Findings The empirical results generally show that EPGR is a key driver in the oil and exchange rate returns nexus. Practical implications The relevance of EPGR in influencing exchange rate volatility is confirmed by the findings. As a result, it is critical for government officials and foreign exchange investors to use EPGR as a leading indicator when establishing foreign exchange trading strategies and economic forecasts. Originality/value This study is the first, to the best of the authors’ knowledge, to apply a wavelet-based technique to account for EPGR in the relationship between oil and exchange rate returns in the BRICS countries.

Oil shocks and investor attention

In this paper, we examine the existence of sentiment exposure in oil price returns. We augment the SVAR model of Kilian and Park (International Economic Review, 2009, 50, 1267–1287) by including the effects of (1) investors sentiment proxied by Google’s search volume index, (2) economic policy uncertainty (EPU) and (3) time variation in both coefficients and the variance-covariance matrix. Our empirical results show that changes in investor attention do exhibit a significant long-lasting impact on oil and stock market returns. Aggregate oil demand and supply shocks have a transitory effect on investor sentiment. We reveal that the impact of EPU is temporary and significant, while EPU responds strongly to shocks on oil prices and stock market returns. In all cases, the magnitude and sign of responses are affected by the timing of the shock. Our findings are robust to an alternative sentiment indicator and once the role of oil inventories is considered.

The impact of institutional analyst forecast divergence on crude oil market: Evidence from the mixed frequency models

As important information intermediaries, analysts play a non-negligible role in the crude oil market. Existing research often focuses on analysts' collection and interpretation of firm-specific information, but neglects the impact of analysts' forecasts on specific markets such as the crude oil market, which is crucial to the safe and stable development of the crude oil market. Therefore, this study uses historical data from January 2011 to December 2020 as a sample to construct analysts' forecast divergence indicators from 15 institutional analysts' forecast data on international crude oil futures prices. It then explores the impact of institutional analysts' forecast divergence on oil price return volatility, crude oil market jumps and crude oil futures trading volume, based on various mixed-frequency models. The results are as follows: First, volatility in oil price returns increases with a growing divergence in analysts' forecasts. Second, analysts' forecasts do not trigger jump in the crude oil market on the first six days after the information is released, but trigger a significant positive jump in the market on the seventh day. Third, the impact of analysts' forecast divergence on trading volume is weak; however, the reverse effect is significant, while the static and dynamic spillover results are consistent.

Oil prices and sectoral stock returns in the BRICS-T countries: A time-varying approach

This paper investigates how exchange rates and oil prices have affected sectoral stock returns in the BRICS-T countries over the period from 2 January 2001 to 22 March 2021. Following the estimation of a benchmark linear model, Bai and Perron (2003) tests are carried out in each case to identify structural breaks, and then a state-space model with time-varying parameters is also estimated. The analysis shows that oil prices have a significant, positive effect on the energy sectors of all BRICS-T countries except India; a negative one on the industrial sectors of all countries except Turkey; a negative one on the financial sectors of Brazil, Russia, India, and South Africa; a negative one on the transportation sectors of India and Turkey and a positive one on that of Russia; finally, the most significant effect is on the chemicals sector, though it varies across countries. The subsamples and time-varying estimates indicate that exchange rate returns have a larger influence than oil price returns. Because energy-dependent sectors are vulnerable to global volatility, appropriate energy regulations should be implemented to reduce risk.

Dynamic relationships among green bonds, CO2 emissions, and oil prices

Green bonds play a pivotal role in the financing of sustainable infrastructure systems. Likewise, CO2 emissions and oil prices can cause an impact on the green bonds market. In order to better understand this issue, this study analyzes the relationship among green bonds, CO2 futures’ prices, and oil prices using a daily data set that includes 2,206 observations corresponding to daily information from 1 January 2014 to 15 June 2022. The Granger Causality Test and the Dynamic Conditional Correlation (DCC-Garch) Model were employed to conduct this analysis. Furthermore, a sensitivity analysis was performed to identify crisis periods concerning the sample period and provide an analysis of DCC-Garch results during extreme market conditions like the COVID-19 pandemic and the Russian invasion of Ukraine. The Granger Causality Test results present a unidirectional causality running from the Green Bond Index to the oil price returns. Also, there is a unidirectional causality running from the Green Bond Index to the CO2 futures’ returns. Additionally, a unidirectional causality runs from the oil price returns to the CO2 futures’ returns. The results for the DCC-Garch indicate a positive dynamic correlation between the Brent oil price return and the CO2 futures’ returns. Finally, the Green Bond Index shows a negative dynamic correlation to the oil return and the CO2 futures’ returns presenting a strong correlation in uncertainty periods.

Forecasting crude oil price returns: Can nonlinearity help?

The nonlinear components of predictors could be informative, while conventional predictive regression models only consider the linear components in the time-series prediction of crude oil price returns. Inspired by this, we propose a novel method to obtain useful information embedded in the predictors’ nonlinearity. We incorporate not only the original predictors (i.e., the linear components) but also their quadratic terms (i.e., the nonlinear components) when generating diffusion indices. The diffusion indices with nonlinearity can improve the crude oil return predictability both in- and out-of-sample. We also consider alternative nonlinear measures, including exponential, absolute, higher-power, and interactive forms. The loading results with supervised learning suggest that squared-form variables are informative. Furthermore, the diffusion indices with nonlinearity are better equipped to explain the supply and demand of crude oil, which is the economic source of predictability. The predictability is also significant in a market-timing exercise and survives a series of robustness checks and extensions.

Analysing Time-frequency Relationship between Oil price and Sectoral Indices in India using Wavelet Techniques

Oil is considered an essential factor of any economy. This paper examines the time-varying correlation between oil price return, BSE SENSEX, and 14 sectoral indexes in India using multiscale wavelet decomposition and wavelet coherence analysis. The maximal wavelet discrete wavelet transform analysis shows a feedback relationship between 13 sectors at higher time horizons (dC4, dC5, and dC6). Based on the wavelet coherence plot, the oil price and sectoral index return show a high co-movement at 32 to 128 weeks. The wavelet coherence plot shows that the oil price and sectoral index return show a high co-movement during the period of Mar-May 2020 (especially during the period of financial crisis widely spread due to the COVID19 pandemic and nationwide lockdown notification announced by the Government of India). We discuss the implications of these studies in detail.

How did the oil price affect Japanese yen and other currencies? Fresh insights from the COVID-19 pandemic

In this paper, we use a shock spillover index to test the hypothesis that oil prices helped shape exchange rate behavior during the COVID-19 period. We use four exchange rates (CAD, EURO, JPY, and GBP vis-à-vis the USD) together with the oil price variable to test the importance of both return and volatility spillovers. Using hourly data, we consistently discover that both oil price return and volatility spillovers have become more important in explaining exchange rates in the COVID-19 period.

On the interdependence between biofuel, fossil fuel and agricultural food prices: Evidence from quantile tests

This study investigates the long- and short-run relationships between fossil fuel, biofuel and agricultural food commodity prices by applying the cointegration and causality analysis. This study employs a quantile approach to consider the non-linearity and asymmetry in the relationship of data. To reduce a possible noise contained in the data, this study uses weekly average data of biofuel (ethanol), fossil fuel (West Texas Intermediate [WTI] oil) and agricultural food commodity (corn) futures prices from April 18, 2005 to October 30, 2020. The main findings are summarised as follows. First, we discover no evidence of cointegration between the quantiles of WTI oil, ethanol and corn prices when all quantiles are considered, although the results of the linear cointegration test are inconclusive. Second, results of the Granger non-causality test in quantiles reveal a significant short-run bidirectional causality between the returns of WTI oil, ethanol and corn prices, for all or most quantiles of the distribution. This implies asymmetric and nonlinear causal linkage between these variables. Moreover, the results of our analysis have several policy implications, including energy price stability, decrease in energy dependence on fossil fuels, promotion of agricultural industry, environmental sustainability, energy conversion and reduction of greenhouse gas emissions.

Dynamic spillovers between oil price, stock market, and investor sentiment: Evidence from the United States and Vietnam

This paper aims to examine the dynamic spillovers between oil price shocks, stock market returns and investor sentiment in the US and Vietnam during the period 2010–2020. To this aim, we consider a financial network consisting of three above variables in a time-varying parameter vector autoregression (TVP-VAR)-based spillover framework. Our results show a moderate interdependence among the variables in our networks. Further, the relationship between oil price, stock market returns and investor sentiment is time-varying and quite driven by time-specific developments and events. Overall, we find that oil price and sentiment are net transmitters of shocks in the US whereas stock market return is the net recipient. For Vietnam, however, investor sentiment is the principal net transmitter of shocks while oil price and stock return are the net recipients. Our results remain robust to alternative international benchmarks of crude oil and the choice to estimate the TVP-VAR framework.

Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables. Evidence from the USA

This paper focuses on the price determinants of gold, and on the challenges associated with gold’s safe haven property. Specifically, it analyses the interlinkages and the return spillover effect among gold, crude oil, S&P 500, dollar exchange rate, Consumer Price Index (CPI), economic policy uncertainty and Treasury bills, by employing a Vector Autoregression (VAR) and the spillover index of Diebold and Yilmaz (2012), Diebold and Yılmaz (2014). Monthly realized return series, covering the period from 2nd of January 1986 to 31st of December 2019 are used to examine the short-run linkages, and the return spillovers rolling-window estimates in analyzing the transmission mechanism in a time-varying fashion, respectively. Our findings identify gold as a strong dollar hedge, while crude oil and Treasury bills appear to drive inflation; they also indicate strong spillover effects between exchange rate and gold returns. In general, co-movement dynamics display state-dependent characteristics. Both total and directional spillovers increase significantly during market turbulence caused by severe financial crises such as the Global Financial Crisis (GFC) of 2007–2009 and the European Sovereign Debt Crisis of 2010–2012. Net spillovers switchbetween positive and negative values for all these markets, implying that the recipient/transmitter position changes drastically with market events. Economic policy uncertainty, stock market returns, and crude oil price returns are the main transmitters, while Treasury bills and CPI are the main return shock recipients. Gold and exchange rate act both as receivers and transmitters over the sample period.

Conditional Dynamic Dependence and Risk Spillover between Crude Oil Prices and Foreign Exchange Rates: New Evidence from a Dynamic Factor Copula Model

This paper proposes a dynamic factor model to accurately characterize the dynamic dependence and risk spillovers between the crude oil and exchange rate returns of oil-trading countries from 2000–2020, conditional on the common factors. To this end, we first identify the common factors related to returns of crude oil prices and exchange rates from 14 typical oil-trading countries. Then, we use the AR-GARCH model to filter the respective idiosyncratic factors and conduct a comparative study of conditional dynamic dependence between the crude oil and exchange rate returns of oil importers and exporters. Finally, we combine the dynamic factor copula model with the CoVaR method to measure the conditional risk spillover effect between crude oil and exchange rate markets. The empirical study indicates that the classical factor analysis can be used to precisely identify the common factors related to both financial markets, with the similar trend of macro-economic indicators. Furthermore, the factor copula model can capture the dynamic structure between crude oil and exchange rate markets more accurately than the traditional Copula–GARCH model. Specifically, the idiosyncratic factors related to each return series still have a significant impact on the dependence between the crude oil and exchange rate returns of oil importers, while the common factors have played an important role in the relationship of oil exporters’ exchange rates with crude oil prices. Finally, the crude oil market has enjoyed a relative risk premium to the exchange rate markets of oil-trading countries. However, there is almost no conditional risk spillover from the corresponding exchange rates to the crude oil prices. Finally, we discuss the implications for investors, policymakers and respective exchange rate regulators from oil-trading countries with further insights from the macro-economic perspective.

Do exchange rate and inflation rate matter in the cyclicality of oil price and stock returns?

The cyclicality of oil prices and stock returns in oil-exporting and oil-importing countries is investigated in this study. We considered the role of asymmetry along with the impact of inflation and exchange rate on the cyclical relationship. The cycle component of oil prices and stock returns is extracted using the Hamilton Filter, and the slope coefficient homogeneity is determined using the Swamy (1970) test. The nonlinear Panel ARDL developed by Shin et al. (2014) is employed as the estimation method to study the cyclical nexus of oil price and stock returns. Our findings indicate that when asymmetry is taken into account, the influence of inflation rate on the cyclical link between oil prices and stock returns is insignificant. However, we find the role of exchange rate significant. We find that the exchange rate transforms an initially procyclical link between the oil price and stock returns in oil-exporting nations into a short-run countercyclical relationship. It also transforms a supposedly countercyclical relationship between oil price and stock returns in oil-importing into a long-run procyclical relationship. Overall, the exchange rate is critical to the cyclicality of oil prices and stock returns, whereas inflation is negligible.