Oil Price Risk Research Articles

COVID−19 and Oil Price Risk Exposure

This study investigates oil price risk exposure of financial and non-financial industries around the world during the COVID–19 pandemic. The empirical results show that oil and gas industries exhibit the highest positive exposure to oil price risk, benefiting most when there is an increase in oil price. In contrast, heavy users of oil, such as airlines and defence, suffer most from increases in oil price. The COVID–19 outbreak appears to moderate the oil price risk exposure of both financial and non-financial industries. The oil price risk exposure of financial and non-financial industries remains robust to alternative asset pricing framework.

CRUDE OIL OPTION MARKET PARAMETERS AND THEIR IMPACT ON THE COST OF HEDGING BY LONG STRAP STRATEGY

This study aims to examine the impact of selected market parameters of the European crude oil options on the hedging costs and break-even points (BEPs) in the Long Strap strategy. The paper analyses the impact of the following market parameters: volatility and the future price of crude oil, the strike price and time to expiration. The theoretical aspect consisted in using the Black model to calculate the value of the option price and the Long Strap strategy BEP in the condition of ever-changing market parameters. These calculations, by determining implied volatilities of the options, have been adapted to the actual data from the exchange market for the options on WTI futures contract. It was made possible owing to the QuikStrike platform made available by a CME Group exchange. To obtain information about the impact of volatility, time and price of futures on the costs of hedging and BEPs in the Long Strap strategy, the authors calculated the Greeks (delta, gamma, vega and theta) for the crude oil options. Having done that, not only could they determine the direction but also the power of impact that the parameters had on the final results in the Long Strap strategy. Keywords : commodity options, crude oil price risk, Long Strap option strategy JEL Classifications : G13, G32 DOI: https://doi.org/10.32479/ijeep.8613

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.

Google Index-Driven Oil Price Value-at-Risk Forecasting: A Decomposition Ensemble Approach

The oil price is influenced not only by the fundamentals of supply and demand but also by unpredictable political conflicts, climate emergencies, and investor intentions, which cause enormous short-term fluctuations in the oil price. The proposition of the Google index-driven decomposition ensemble model to forecast crude oil price risk uses big data technology and a time series decomposition method. First, by constructing an index of investor attention for the market and emergencies combined with a bivariate empirical mode decomposition, we analyze the impact of investor attention on oil price fluctuations. Second, we establish a vector autoregression model, and the impulse responses define the impact of emergencies on the crude oil price. Finally, with the help of machine learning and historical simulation methods, the risk of crude oil price shocks from unexpected events is predicted. Empirical research demonstrates that concerns related to the oil market and emergencies that appear in Google search data are closely related to changes in oil prices. Based on the Google index, our model’s prediction of crude oil prices is more accurate than other models, and the prediction of value-at-risk is closer to the theoretical value than the historical simulation with the ARMA forecasts method. Considering the impact of emergencies in the prediction of crude oil price risk can help provide technical guidance for investors and risk managers and avoid economic risks caused by climate disasters or political conflicts.

A Comparative Study on the Behavior of Islamic and Conventional Stocks in the Presence of Oil Price, Gold Price, and Financial Risk Factors: Evidence from Dow Jones Indices

The research aims to analyze the influence of the gold price, oil price and financial risk on Islamic and conventional securities on comparative as well as on individual bases. Monthly prices of oil and gold are extracted from the websites of West Texas Intermediate and World Gold Council, whereas time series data for financial risk is derived from the Volatility Index of S&P 500. All these variables are found to be cointegrated at the first difference with both the Dow Jones indices, which means that gold, oil and financial risk have long term association with Islamic and conventional stocks. In order to find the direction and magnitude, this study applied the Newey-West HAC test, which also handles autocorrelation and heteroscedasticity issues in the time series data. The findings of the study suggest that gold prices are positively associated whereas oil prices and financial risk are negatively associated with both types of securities. Though the direction of the nexus is similar for Islamic and conventional stocks, but the magnitude differs especially in case of oil and financial risk. Nevertheless, it can be concluded that there is no diversification prospect between conventional and Islamic stocks under the influence of oil prices, financial risk, and gold prices.

Inflation Risk Factors and Contributory Pension Wealth: Reflections on Nigeria’s Old-age Poverty

Purpose of the article: Nigeria’s shift to the Defined Contributory Scheme (DCS) is more or less questionable in terms of providing economic security for the senior citizens and preventing old age poverty. The 2004 and 2014 pension reforms sought to conform to pension theory; yet, there could be greater critical risks such as inflation risk factors. This study therefore examined how inflation risk factors impact pension wealth in Nigeria. Methods: The Structured Autoregressive Distribution Lag (ARDL) to the co-integration approach is employed to determine the relationships between inflation risk factors and investment returns of selected Nigerian pension fund administrators for the period 2009–2014. Scientific aim: This study sought the evidence to support the impact of inflation risk factors on pension wealth in Nigeria following the switch to the DCS based on the theoretical link between the investment returns and inflation rate. Findings: Inflation and world oil prices adversely affected the asset returns of pension funds management bourgeoning vulnerabilities to old age poverty. Conclusions: Pension wealth of Nigerians at the cumulating phase is at high risk to inflation risk factors, and is reasonably justified with the negative relationship with the exchange rate factors and world oil prices. The study suggests that inflation, exchange rate risk, and world oil price risk must be strategically tackled with farsighted multi-faceted fiscal and monetary measures to reduce social insecurity and vulnerability to old age poverty. The government should consider promoting the flotation of inflation-indexed bonds and urgently consider imposing a guaranteed returned pension plan (GRPP) to reduce the inflation risk in the accumulating phase of the DCS, while workers should consider increasing their contributions to mitigate inflation risk factors. Insurance life annuities can also provide inflation insurance linked to inflation-indexed bonds.

Oil Price and Stock Prices of EU Financial Companies: Evidence from Panel Data Modeling

Crude oil is an indispensable resource for the world economy and European Union (EU) countries are strongly dependent on oil imports. In a framework defined by generally positive correlations between oil and stock prices, the paper investigates the relationship between financial companies’ stock prices and crude oil price using a sample of major financial companies headquartered in the EU. The link between stock prices and oil price risk is modelled using a set of macroeconomic variables that includes local stock market indices, the EUR/USD exchange rate, the oil imports dependency, inflation rate, and global volatility indices. We employ panel data as the base econometric model and an ARDL extension that is more appropriated for our research objectives. Our findings show that the EU financial sector is pervasively exposed to oil price changes over the long-run and this exposure is a component of financial companies’ exposure to real economy risk factors, which points towards the key role of the financial sector in the EU economy in transmitting systemic shocks. At the same time, we detect signs of a different behavior of market investors over the short-versus the long-run concerning the valuation of financial companies’ stock prices in relation to oil price and other macroeconomic variables, which raises distressing challenges for financial authorities.

Quantile relationship between oil and stock returns: Evidence from emerging and frontier stock markets

This study extends the literature on the asymmetric effect of oil price fluctuations on emerging and frontier stock markets via a quantile-on-quantile approach that allows to capture normal and extreme states in each respective market. We find that oil risk exposures are heterogeneous across the emerging and frontier stock markets and indeed display quantile-specific characteristics. Observing uniform patterns of oil risk exposures within groups of countries that include both importers and exporters, we argue that oil price risk serves as a systematic risk proxy, capturing the market's concerns regarding global growth expectations, rather than a simple import/export commodity. Our findings suggest that signals from the oil market, either via measures of trading activity in oil futures or changes in basis values, could be utilized by policy makers to improve models of stock market volatility.

Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach

The rapid fluctuations in global crude oil prices are one of the important factors affecting both the sustainable development and the green transformation of the global economy. To accurately measure the risks of crude oil prices, in the context of big data, this study introduces the two-layer non-negative matrix factorization model, a kind of natural language processing, to extract the dynamic risk factors from online news and assign them as weighted factors to historical data. Finally, this study proposes a giant information history simulation (GIHS) method which is used to forecast the value-at-risk (VaR) of crude oil. In conclusion, this paper shows that considering the impact of dynamic risk factors from online news on the VaR can improve the accuracy of crude oil VaR measurement, providing an effective tool for analyzing crude oil price risks in oil market, providing risk management support for international oil market investors, and providing the country with a sense of risk analysis to achieve sustainable and green transformation.

Further insights into the oil and equity market relationship

Purpose This paper aims to assess the asymmetric effects of oil price shocks and the impact of oil price volatility on the Eurozone’s supersector returns, with a particular emphasis on the impact of the subprime crisis and the euro debt crisis (EDC) on this relationship. Design/methodology/approach Empirical data consist of daily observations of the 19 EURO STOXX supersector indices and the Brent crude oil price index for the period January 2001 to August 2015. This paper uses a non-linear multifactor market model. This model accounts for heteroscedasticity and breakpoints that are identified by the Bai and Perron (1998, 2003) tests. Findings The results show that supersector returns are sensitive to oil price shocks. However, in most cases, their responsiveness to oil price volatility is not significant. The relationship between oil price shocks and supersector returns changes through time and depends on the sector. Financial turbulence affects the oil-stock market nexus. In most cases, the subprime crisis has had a positive impact on the oil-stock market relationship, whereas the EDC has had an overall negative effect. Before the subprime crisis, there is an evidence of asymmetric effects for some supersectors. Meanwhile, for most sectors, the asymmetric effects disappear after 2008. Originality/value The study improves understanding of the interaction between oil price risk and the Eurozone sector indices returns. Furthermore, it enables global investors to manage the risk inherent to the portfolio managers’ positions.

Crude oil price shocks and hedging performance: A comparison of volatility models

From a practical perspective, it is crucial to hedge the crude oil price risk in periods of dramatic price change. In this study, we directly investigate the performance of crude oil hedge portfolios in the five periods in which the largest oil price shocks in history occurred. We use stochastic volatility (SV), GARCH, and the diagonal BEKK model to estimate the minimum variance hedge ratio of hedge portfolios. Our empirical results provide evidence that hedging strategies based on the SV model are able to outperform the GARCH and BEKK models in terms of variance reduction. Our results are also consistently valid for various hedge horizons. Interestingly, although it is important to estimate variance and covariance accurately when constructing minimum variance portfolios, we find that reducing the mean squared and mean absolute errors does not guarantee superior hedge performance.

Oil price risk evaluation using a novel hybrid model based on time-varying long memory

The volatility of crude oil price has a great influence on the world economy. In order to measure the crude oil price risk (VaR) and explain the dynamic relationship between investment income and risk in the oil market more clearly, this paper uses a variety of fractional GARCH models to describe typical volatility characteristics like long memory, volatility clustering, asymmetry and thick tail. The autoregressive conditional heteroscedasticity in the mean model (ARCH-M) and peaks-over-threshold model of extreme value theory (EVT-POT) are taken into account to develop a hybrid time-varying long memory GARCH-M-EVT model for calculation of static and dynamic VaR. Empirical results show that the WTI crude oil has a significantly long memory feature. All the fractional integration GARCH models can describe the long memory appropriately and the FIAPARCH model is the best in regression and out of sample one-step-ahead VaR forecasting. Back-testing results show that the FIAPARCH-M-EVT model is superior to other GARCH-type models which only consider oil price fluctuation characteristics partially and traditional methods including Variance-Covariance and Monte Carlo in price risk measurement. Our conclusions confirm that considering long memory, asymmetry and fat tails in the behavior of energy commodity return combined with effectively dynamic time-varying risk reflection such as the ARCH-M model and reliable tail extreme filter processes such as EVT can improve the accuracy of crude oil price risk measurement, provide an effective tool for analyzing the extreme risk of the tail of the oil market and facilitate the risk management for oil market investors.

The effect of productivity and country risk on development in the Brazilian Pre-salt Province

ABSTRACTTwelve years after the first significant discovery in the pre-salt layer, pre-salt fields represent about 55% of Brazil’s oil production. Following an economic recession, an increase in the country credit risk, and the 2014 oil price collapse, we analyze the production of petroleum in the pre-salt zones by identifying: the break-even price (BEP), and the effect of oil price, price volatility, productivity, and country risk on wells drilled. To identify these effects on the number of wells drilled into the pre-salt zone, we estimate 100 cointegrating vector autoregression models that specify 10 measures of price and 10 break-even prices. The most accurate model measures oil prices using WTI, has a BEP of $62 per barrel (2018 prices). Results indicate the negative and positive effect, respectively, of raising the country risk and productivity on the number of development wells drilled into the pre-salt zone.

Oil financialization and volatility forecast: Evidence from multidimensional predictors

AbstractUsing the generalized dynamic factor model, this study constructs three predictors of crude oil price volatility: a fundamental (physical) predictor, a financial predictor, and a macroeconomic uncertainty predictor. Moreover, an event‐triggered predictor is constructed using data extracted from Google Trends. We construct GARCH‐MIDAS (generalized autoregressive conditional heteroskedasticity–mixed‐data sampling) models combining realized volatility with the predictors to predict oil price volatility at different forecasting horizons. We then identify the predictive power of the realized volatility and the predictors by the model confidence set (MCS) test. The findings show that, among the four indexes, the financial predictor has the most predictive power for crude oil volatility, which provides strong evidence that financialization has been the key determinant of crude oil price behavior since the 2008 global financial crisis. In addition, the fundamental predictor, followed by the financial predictor, effectively forecasts crude oil price volatility in the long‐run forecasting horizons. Our findings indicate that the different predictors can provide distinct predictive information at the different horizons given the specific market situation. These findings have useful implications for market traders in terms of managing crude oil price risk.

Minimum-Variance Hedging Oil Price Risk for US Energy ETF Investors: Out-of-Sample Investigation

Oil price experienced crashes during the period of the 2008 global financial crisis and the recent period of oil price crisis in 2014. Hedging oil price risk becomes more important for energy industry investors. In this paper, we investigate the out-of-sample dynamic hedging performance by using the oil futures based on 11 methods for estimating the minimum-variance optimal hedge ratio for 5 US energy ETFs. Except variance, we also study the difference in average returns and utilities between the unhedged strategy and the hedged strategy by employing statistical tests. Finally, we study explore the performance of the hedged strategy in terms of cumulative return and variance under different oil market regimes.

Oil Price Risk and Financial Contagion

In this paper we test for the existence of equity market contagion, originating from oil price fluctuations, to regional and domestic stock markets. The data are collected over the period from April 1993 to April 2015. We apply an empirical multifactor asset pricing model with three-factor setting to capture the unexpected return and disentangle simple correlation due to fundamentals and contagion. We investigate four regions: the European Monetary Union (EMU), Asia-Pacific (AP), the Non-European Monetary Union (NEMU) and North America (NA). We define contagion as the excess correlation that is not explained by fundamental factors. Oil price risk is shown to be a factor as important as contagion. In addition, oil price fluctuations amplify contagion in the context of regional markets strongly interlinked with the USA.

On the time‐varying links between oil and gold: New insights from the rolling and recursive rolling approaches

AbstractThis study analyses the dynamic linkages between oil and gold prices for the spot and 1‐ to 12‐month futures markets using monthly data over the period 1983–2016. To do this, we use the rolling and recursive rolling Granger causality approaches. The distinguishing feature of this study from the previous studies is that this is the first study investigating the causal links between oil and gold using time‐varying causality tests. The findings show that the causality links between oil and gold display strong time variation. Although causal links are not detected for most of the study period, strong bidirectional or unidirectional causality is found in several subsamples. The duration of the periods with causality links varies from a few months to 3 years, whereas the duration for the noncausality periods might be 15 years long. By date stamping the causality links between oil and gold, our paper discovers that causality from oil to gold is related to large oil price changes, whereas causality from gold to oil is related to large financial crises. The evidence obtained in the paper points out the dangers of assuming a constant causality link between oil and gold markets because these links might break down unexpectedly. Our findings point out to the dangers of assuming noncausality between oil and gold particularly in hedging oil price risk using gold.

Impact of oil price change on airline's stock price and volatility: Evidence from China and South Korea

Generally, the influence of crude oil price on the industries (enterprises) varies because they have different levels of reliance on crude oil. For airlines, the expenditure on fuel accounts for a considerable proportion of their gross costs; thus, airlines are unusually sensitive to changes in the crude oil price. The discussion on the relationship between crude oil price and airlines will help the airlines improve their ability to cope with the crude oil price risk. In addition, the responses of South Korean and Chinese airlines in the event of a price shock, that take, are also very important as the airplane is a basic form of transportation in many countries. This study investigates the impact of three crude oil price (WTI, Brent, Dubai) change on the stock price and volatility of four airlines (Korean Air, Asiana Airlines, Air China, and China Eastern Airlines) using VAR-GARCH-BEKK model. The main findings are as follows. There is return and volatility spillover effect between crude oil price and the stock prices of airlines. The volatility spillover effect between the crude oil price and airlines' stock price is more significant than the return spillover effect. Compared with the transportation industry, the stock prices of smaller airlines of South Korea and China are relatively more sensitive to the change in oil price. In addition, compared with Korea's airlines, China's airlines are influenced more by the oil price change, implying that spillover effects owing to oil price are closely related to the different characteristics of the air transport markets of the two countries.

Practical Applications of Managing U.S. Stock Market Oil Price Risk Using Exchange-Traded Funds

Practical Applications Summary The effects of oil price fluctuations on stock market performance are relatively poorly understood within the investment community. In Managing U.S. Stock Market Oil Price Risk Using Exchange-Traded Funds , published in the Fall 2017 issue of The Journal of Investing , Robert Patterson, Richard Haskell , and Ronald Mano use robust analytical scrutiny to shed light on this issue. They find positive correlations between oil prices and the S&P 500, as well as the majority of subsector ETFs. In the case of the energy subsector ETFs, these correlations are strong enough to suggest that they could be used to manage oil price risk.

Impact of oil price risk on sectoral equity markets: Implications on portfolio management

Structure and degree of oil price impact on sectoral indices are examined using Quantile Regression Analysis (QRA). Nine sectors are found to provide diversification opportunities during a bull market (i.e. 90th quantile of the return distribution) and three sectors during a bear market (10th quantile) to hedge oil price risk. Further, the contagion effect and interdependency between oil price and sectoral equity are assessed through frequency domain causality. The causality from oil price in the long run determined that there is interdependence between three sectors and oil price changes. The direction of causality from oil price is mixed in both the short run (high frequency) and long run (low frequency) for nine sectors. Overall, the carbon sector is the only sector that is immune to oil price risk, thereby providing investment and hedging opportunities for portfolio managers.