Crude Oil Returns Research Articles

Measuring the interdependence between investor sentiment and crude oil returns: New evidence from the CFTC's disaggregated reports

This paper examines information spillover between WTI returns and investor sentiment indices measured by various trader positions using a connectedness approach. Our findings show that sentiment by trader type is highly correlated with WTI returns. Among the different sentiment types, speculator sentiment makes the largest contribution to WTI returns variation over the full sample period. The dynamic results show that the influence of investor sentiment increases significantly when oil prices are in a downward movement in which hedger sentiment plays the leading role in information transmission.

The time-varying spillover effect between WTI crude oil futures returns and hedge funds

To capture the relationship change between WTI crude oil returns and hedge funds in recent years, the linear and nonlinear Granger causality test approaches and Hong's time-varying information spillover statistics are employed based on the data from 2006 to 2017. The empirical results indicate that, first, hedge funds' net positions may Granger cause crude oil futures returns in the linear manner but the nonlinear relationship appears weaker. Second, both in the linear and nonlinear manners, the influence of crude oil futures prices has been evidently strengthened when they experience high volatility whilst the influence of hedge funds' net positions appears stronger only when crude oil futures prices rise substantially. Finally, hedge funds play an important role in pushing up crude oil price bubbles which are responsible for the crash of crude oil prices in 2008, but the crash of oil prices in 2014 is more attributed to market fundamentals.

Crude oil, stock market, and foreign exchange return volatility and spillover: a GARCH DCC analysis of Indian and Japanese financial market

This research article strives to examine the return volatility, co volatility and spillover impact of stock exchange, foreign exchange as well as crude oil in Indian and Japanese financial market which are two pertinent, Asian economies by using generalised autoregressive conditional heteroskedasticity dynamic conditional correlation (GARCH-DCC) and variance decomposition techniques by considering daily trading data of ten years from 2007 to 2017. The result of GARCH-DCC analysis shows the evidence of ARCH and GARCH effect on the return of all the variables, furthermore it unearths that return of all the variables, is not moving away from its mean in long run and persistent for a long time at leaning process in both this country. The consequence of volatility spillover proves that return on foreign exchange is a net transmitter of volatility while the return of stock exchange is a net receiver of volatility in the Indian financial market. As per as Japanese financial market is concerned it can be inferred that return of stock market is dominant variable as it is a net contributor of volatility to the other variables while foreign exchange market is mostly affected variable as it is a net receiver of volatility.

Crude oil, stock market, and foreign exchange return volatility and spillover: a GARCH DCC analysis of Indian and Japanese financial market

This research article strives to examine the return volatility, co volatility and spillover impact of stock exchange, foreign exchange as well as crude oil in Indian and Japanese financial market which are two pertinent, Asian economies by using generalised autoregressive conditional heteroskedasticity dynamic conditional correlation (GARCH-DCC) and variance decomposition techniques by considering daily trading data of ten years from 2007 to 2017. The result of GARCH-DCC analysis shows the evidence of ARCH and GARCH effect on the return of all the variables, furthermore it unearths that return of all the variables, is not moving away from its mean in long run and persistent for a long time at leaning process in both this country. The consequence of volatility spillover proves that return on foreign exchange is a net transmitter of volatility while the return of stock exchange is a net receiver of volatility in the Indian financial market. As per as Japanese financial market is concerned it can be inferred that return of stock market is dominant variable as it is a net contributor of volatility to the other variables while foreign exchange market is mostly affected variable as it is a net receiver of volatility.

Association between the energy and emission prices: An analysis of EU emission trading system

Previous studies have focused on the co-movements between the prices of different types of energy and, to some extent, the co-movements between the energy and financial assets prices, falling short of analysing the co-movements between the different types of energy and emission price. In this study, using the daily data from November 2007–31st October 2017 on quotes of Brent Crude oil and Natural Gas spot returns and quotes of the EU-ETS spots, we employed a time-varying copulas connection function to assess the risk dependency relationship between ETS and energy prices. The results show that there is an asymmetry dependence change rule between ETS, oil and gas spot index, with the correlation of the lower tail significantly higher than that of the upper tail. These findings indicate that, with the use of time-varying SJC Copulas model, economic agents can control investment risk and forecast abnormal fluctuations in oil prices.

Uncertainties and extreme risk spillover in the energy markets: A time-varying copula-based CoVaR approach

In this paper, we explore the impact of uncertainties on energy prices by measuring four types of Delta Conditional Value-at-Risk (∆CoVaR) using six time-varying copulas. Three different measures of uncertainty (economic policy, financial markets and energy markets) are considered, and the magnitude and asymmetric effects of their influence are investigated. Our results suggest that there generally exists negative dependence between energy returns and changes in uncertainty. The risks of clean energy and crude oil returns are more sensitive to uncertainties in the financial and energy markets, while the impact of economic policy uncertainty is relatively weak. The upside and downside CoVaRs and ∆CoVaRs demonstrate significant asymmetric effects in response to extreme uncertainty movement. Our findings therefore have important implications for energy portfolio investment.

The Impact of Volatility on Returns Models: The Case of Crude Oil

This article demonstrates that a static model of crude oil price movements, in which the various predictive variables always have the same weights, is not the ideal approach to model crude oil prices. Rather, investors looking to invest in crude oil need to understand how volatility affects the extent to which the various predictive variables matter for crude oil prices during their forecast horizon. Specifically, the relationships between crude oil returns and crude oil production and between crude oil returns and crude oil price momentum are a function of oil price volatility; the higher crude oil price volatility, the more negative the relationships between crude oil returns and production and between crude oil returns and price momentum. In addition, the higher crude oil prices, the more negatively crude oil returns are related to changes in crude oil volatility. One conclusion is that, although production, price momentum, and volatility all matter for crude oil prices, they matter differently at different times. A second conclusion is that, because there is a greater ability to forecast large moves in crude oil prices when volatility is higher, it is sensible for investors to be more active in crude oil markets when volatility is higher. Extrapolating from the results reported for crude oil, one would expect fundamental and technical data to have greater absolute effects on returns for industrial metals, agricultural products, and other commodity markets when volatility is high. It follows that traders should be more active in these markets when volatility is higher.

The dynamic information spill-over effect of WTI crude oil prices on China’s traditional energy sectors

PurposeThe purpose of this paper is to explore the dynamic influence of WTI crude oil returns on the stock returns of China’s traditional energy sectors, including oil and gas exploitation, coal mining and processing, petroleum processing and coking, electricity, heat production and supply and mining services.Design/methodology/approachHong’s information spill-over test and the DP Granger causality test are applied to investigate the relationship between the two markets. Moreover, a rolling window is introduced into the above two tests to capture time-varying characteristics of the influence of WTI crude oil returns.FindingsThe empirical results indicate that, first, there exists significant bidirectional linear causality between WTI crude oil returns and China’s traditional energy sectoral stock returns, but the nonlinear causality appears weaker. Second, the influence of WTI crude oil returns on traditional energy sectoral stock returns has time-varying characteristics and industry heterogeneity both in the linear and nonlinear cases. Finally, the decline of WTI crude oil prices may strengthen its linear influence on the stock returns of traditional energy sectors, while the excessive rise of market values in traditional energy sectors may weaken the linear and nonlinear influence of WTI on them.Originality/valueThe general nexus between international crude oil market and China’s traditional energy stock market is explored both in the linear and nonlinear perspectives. In particular, the dynamic linear and nonlinear influence of WTI crude oil returns on China’s traditional energy sectoral stock returns and its industry heterogeneity are analysed in detail.

Using nonparametric copulas to measure crude oil price co-movements

Tail dependence of crude oil price returns between four major benchmark markets are analyzed through the lenses of nonparametric copula models. This paper illustrates that nonparametric copula is flexible to incorporate important empirical patterns of tail dependence and provides better goodness-of-fit to the data than the optimal parametric copula. Estimation results show that the level and the structure of tail dependence of crude oil returns vary significantly depending on data frequency and the time period covered.

The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk

The aim of this study is to analyze the relevance of recently developed news-based measures of economic policy and equity market uncertainty in causing and predicting the conditional quantiles of crude oil returns and risk. For this purpose, we studied both the causality relationships in quantiles through a non-parametric testing method and, building on a collection of quantiles forecasts, we estimated the conditional density of oil returns and volatility, the out-of-sample performance of which was evaluated by using suitable tests. A dynamic analysis shows that the uncertainty indexes are not always relevant in causing and forecasting oil movements. Nevertheless, the informative content of the uncertainty indexes turns out to be relevant during periods of market distress, when the role of oil risk is the predominant interest, with heterogeneous effects over the different quantiles levels.

Volatility spillover from crude oil and gold to BRICS equity markets

PurposeThe purpose of this paper is to investigate the volatility spillover from crude oil and gold to the BRICS stock markets, after removing the effect of co-movement of prices of crude oil and gold.Design/methodology/approachThree multivariate GARCH models (dynamic conditional correlation, constant conditional correlation, and Baba, Engle, Kraft and Kroner) are used to capture the dynamic relationship between the crude oil and gold returns. The innovations from gold and oil are orthogonalized, and the EGARCH model is employed for the spillover analysis. The influences of oil price shocks and gold price shocks are tested on the returns of each of the BRICS equity markets.FindingsThere is evidence of volatility spillover from both the crude oil and gold to the BRICS stock markets. A sub-sample analysis suggests that the volatility spillover from gold was not significant before the financial crisis of 2008, but became significant post-crisis. The volatility asymmetry, which was not significant before the crisis, also became significant after it.Originality/valueThis study examines the volatility spillover to the BRICS stock markets from crude oil and gold, after accounting for the co-movement in their prices. It can help equity investors to judge whether gold can provide incremental diversification benefit, if used in conjunction with crude oil. The study also provides insights into the changes caused by the 2008 financial crisis on this volatility spillover mechanism.

Volatility spillovers and hedging: Evidence from Asian oil-importing countries

In this study the volatility spillover between stock market returns (Shanghai stock exchange, Nikkei stock exchange and Bombay stock exchange) and crude oil returns in the top three Asian oil-importing countries are investigated. BEKK-GARCH, DCC-GARCH, cDCC-GARCH and GO-GARCH estimation techniques are applied using daily data from 1st January 2000 to December 27th, 2016. Further, these estimation results are used to analyze the optimal portfolio weights and hedge ratios for oil-stock portfolios. The findings reveal that shocks dependence and conditional volatility in its own market have more important role than volatility spillover. Also, the bidirectional spillover is confirmed between Nikkei stock return and oil returns. The unidirectional spillover from Indian stock returns to oil returns. There is, however, no evidence of volatility spillover in case of China. According to optimal portfolio weights and hedge ratios, the oil assets are useful instrument to minimize the portfolio risk in studied markets. The investors, however, should choose more stocks than oil assets to form an optimal portfolio. We also notice that the cDCC-GARCH model is better for risk minimization.

The Effect of Global Oil Price Shocks on China’s Chemical Markets

This paper investigates the effects of global oil price shocks on China’s chemical market and two typical markets: fuel oil and PTA. The ARJI-GARCH model is applied to extract the jump intensity of crude oil returns. Then, jump intensity and positive and negative oil price shocks are added to the ARMA-GARCH model to examine the spillover effects of the crude oil market on chemical markets. Our results indicate that global oil returns are characterized by time-varying jump behavior. In addition, the impacts of oil returns jumps on the chemical markets are different. The oil returns jumps only have significant effects on the whole market and the fuel oil market. Moreover, the oil price shocks have asymmetric effects on chemical markets, except for the fuel oil market. Specifically, negative oil price shocks have greater effects on these markets than do positive shocks.

Leverage effects and stochastic volatility in spot oil returns: A Bayesian approach with VaR and CVaR applications

Crude oil markets have been quite volatile and risky in the past few decades due to the large fluctuations of oil prices. We contribute to the current debate by testing for the existence of the leverage effect when considering daily spot returns in the WTI and Brent crude oil markets and by studying the direct impact of the leverage effect on measures of risk such as VaR and CVaR. More specifically, we model spot crude oil returns using Stochastic Volatility (SV) models with various distributions of the errors. We find that the introduction of the leverage effect in the traditional SV model with Normally distributed errors is capable of adequately estimating risk for speculative oil suppliers in both the WTI and Brent markets. Our results also show that financial regulators' model choice, both on the supply and on the demand side, would not be affected by the introduction of leverage. Focusing instead on firm's internal risk management, our results show that the introduction of leverage would be useful for firms who are on the demand side for oil, who use VaR for risk management and who are particularly worried about the magnitude of the losses exceeding VaR while wanting to minimize the opportunity cost of capital. Using the same logic, firms who are on the supply side would be better off not considering the leverage effect.

Nexus between crude oil and stock market return: case of India

Crude oil is an important driver of Indian economy and therefore, it is important to investigate whether the movement in crude oil prices affects the return of stock market indices. This paper examines the impact of return from crude oil on the return of some major Indian stock market indices. The construct was made from daily data from 2005 to 2016 to calculate the daily return of crude oil and four different stock market indices. Two different tests were carried out, namely, Granger causality test and Johansen cointegration test. Granger causality test was conducted to know the relationship between the return from crude oil and stock market indices, whereas, Johansen cointegration test was carried out to understand whether the long run equilibrium relationship is there between the dataset of returns? The result from the Granger causality test suggests that there is causality between the crude return and the return from stock market indices. The nature of causality is unidirectional in nature where crude return affects the return of the stock market indices. The results from Johansen cointegration test suggest that there exists a long run equilibrium relationship among these variables.

Volatility Spillovers in the Thai Palm Oil Market

This paper investigates the effect of global prices in the Thai palm oil market. We provide comprehensive evidence of growth and volatility spillovers for the key crop, crude palm oil in Thailand over the period 1996 to 2015. The Dynamic Conditional Correlation model (DCC) and Granger Causality tests are used, to analyze price volatility effects among Thai and Malaysian crude palm oil and World crude oil prices. Our findings delivered several significant conclusions; that higher prices indicates a positive impact on their uncertainty and also increase a correlation variance among them. Moreover, the Malaysian crude palm oil and world crude oil returns have spillover effects on each of their uncertainty, while Malaysian crude palm oil return has a strong effect on the Thai crude palm oil price and vice versa. Therefore, the government and policy makers in Thailand should pay heed to the changes and fluctuations in those global prices.

Mixed-frequency Drivers of Precious Metal Prices

Lack of intrinsic value, hybrid nature of commodities and recent financialization of commodity markets make of understanding precious metals price moves complicated. Predicting future development of precious metals market can be more feasible if we discover what drives these markets and describe nature of the drivers. The aim of the paper is to explain metal price movements by assessing an impact of multiple economic and financial factors. Based on the literature review we study 8 possible macroeconomic and financial drivers. The data are collected from Bloomberg. We use mixed-data-sampling methodology that enables me to study drivers of various frequencies (daily and monthly) simultaneously in a single model. Results show that the interest rate, the exchange rate, stock levels, stock index returns and crude oil returns are generally significant to drive precious metal markets. The stock index has the most significant impact on the metals returns that is negative. Furthermore, the results divide precious metals into two groups with gold and silver on the one hand and platinum and palladium on the other. The first group is worse explained by considered drivers. Moreover, the interest rate does not have any impact on the price development of gold and silver and crude oil returns influence the pair negatively, contrary to the second pair of platinum and palladium.

What drives natural gas prices in the United States? – A directed acyclic graph approach

This study investigates the impacts of driving factors for natural gas prices during the period from 1999 to 2017. A data-driven approach, namely the directed acyclic graph (DAG), is first employed to disclose the contemporaneous relations among natural gas, crude oil and various factors. The main results indicate that there is a stable contemporaneous causal flow from crude oil to natural gas. Unlike most of the previous research, we found a long-term equilibrium relationship between crude oil and natural gas returns when additional factors were taken into account. However, the impact of oil price returns on natural gas price volatility has decreased after the 2008 global financial crisis. Finally, storage and seasonality factors can never be ignored when analysing natural gas prices, while the impact of speculative activity on natural gas volatility is relatively weak.

Time series forecasting using functional partial least square regression with stochastic volatility, GARCH, and exponential smoothing

AbstractWe propose a method for improving the predictive ability of standard forecasting models used in financial economics. Our approach is based on the functional partial least squares (FPLS) model, which is capable of avoiding multicollinearity in regression by efficiently extracting information from the high‐dimensional market data. By using its well‐known ability, we can incorporate auxiliary variables that improve the predictive accuracy. We provide an empirical application of our proposed methodology in terms of its ability to predict the conditional average log return and the volatility of crude oil prices via exponential smoothing, Bayesian stochastic volatility, and GARCH (generalized autoregressive conditional heteroskedasticity) models, respectively. In particular, what we call functional data analysis (FDA) traces in this article are obtained via the FPLS regression from both the crude oil returns and auxiliary variables of the exchange rates of major currencies. For forecast performance evaluation, we compare out‐of‐sample forecasting accuracy of the standard models with FDA traces to the accuracy of the same forecasting models with the observed crude oil returns, principal component regression (PCR), and least absolute shrinkage and selection operator (LASSO) models. We find evidence that the standard models with FDA traces significantly outperform our competing models. Finally, they are also compared with the test for superior predictive ability and the reality check for data snooping. Our empirical results show that our new methodology significantly improves predictive ability of standard models in forecasting the latent average log return and the volatility of financial time series.

Avantium launches successful IPO

This paper examines the link between oil (gas) firm share price and crude oil (natural gas) return, volatility and drilling activity. Particular emphasis is placed on testing for real option effects through analysis of the sensitivity of oil (gas) firm share price to oil (gas) price volatility. Using the level of active exploration drilling as a proxy for exercised real options, we find some evidence of a real option effect linked to drilling in gas firms, though there is little evidence of this for oil firms. There is evidence of a strong positive oil (gas) return effect on oil (gas) firm value. Finally, there is also some evidence of wealth destruction associated with exploration drilling compared with development drilling.