Crude Oil Returns Research Articles

Analysis of EEMD-based quantile-in-quantile approach on spot- futures prices of energy and precious metals in India

By using the daily data for spot and future prices for India, we examine the frequency-dependent asymmetric relationship between futures and spot markets of crude oil, gold, and natural gas (GON). We use the novel asymmetric, noise-reducing frequency-domain EEMD-based quantile-on-quantile regression (QQR) technique. We contribute to the literature on the Indian commodity market by performing the analysis in a noise-free frequency-varying asymmetric framework where dynamic hedging and portfolio diversification is possible. The study appeals to the nature of market dynamics as well as to different investor risk and reward preferences. The empirical results show that both QR and QQR are able to adequately capture the asymmetric link between GON spot and futures prices across the short-, medium-, and long-terms. It also allows ranging from weak to very strong dependencies albeit both negative and positive across different quantiles. Specifically, we find that hedging strategies are feasible in the medium-terms and long-terms of crude oil returns at all quantiles above 0.05. Moreover, our results also show that natural gas and gold futures can only be a weak hedge for a spot in the short-term, but not in the medium-terms and long-terms. The policy implications are also discussed from the findings.

Impact of the COVID-19 Pandemic on the Crude Oil and Stock Markets in the US: A Time-Varying Analysis

This research explores the interaction among the COVID-19 pandemic, crude oil market and stock market in the U.S. by utilizing a time-varying parameter vector autoregression (TVP-VAR) model. Our results indicate that there is a negative connection between crude oil returns and stock returns. Interestingly, contrary to our intuition, we find that the COVID-19 pandemic cannot exert a negative effect but has a statistically significantly positive effect on crude oil returns and stock returns.

Dynamic spillovers and connectedness between oil returns and policy uncertainty

ABSTRACT This article investigates the dynamic connectedness and spillover effects between category policy uncertainty and WTI crude oil returns for oil-importing countries. To overcome the shortcomings of the traditional rolling-window method and obtain rigorous results, we adopt the TVP-VAR-based connectedness approach, apply it to full samples, and then estimate the dynamic spillovers between the series. Regarding the whole sample, the results reveal that economic policy uncertainty is as a net transmitter, and by employing four categorical policy uncertainty indices we reach the same conclusions. In particular, spillovers of fiscal policy uncertainty on crude oil returns are the most dominant ones, followed by exchange rate policy uncertainty, then monetary policy uncertainty, and finally trade policy uncertainty. And it is noteworthy that the network plots are employed by our article, which provides clearer evidence for the connectedness between variables. On a final note, Brent crude oil returns are utilized to increase the robustness of our conclusion.

The effects of investor emotions sentiments on crude oil returns: A time and frequency dynamics analysis

In this paper, we use wavelet coherence analysis to find that sentiment has a significant effect on crude oil returns that lasts over various investment horizons. While oil returns are positively associated with the sentiments of optimism and trust, they are negatively linked to fear and anger. These relations are more pronounced over the medium and the long term. Additionally, we find that short-term oil returns are relatively more sentiment-sensitive during turbulent periods than in normal conditions. These results highlight the importance of sentiment and investor psychology in the crude oil market.

Systemic risk spillovers between crude oil and stock index returns of G7 economies: Conditional value-at-risk and marginal expected shortfall approaches

In this study, we examine systemic risk and dependence between oil and stock market indices of G7 economies between January 2003 and November 2017. Coincidentally, this timeframe covers different distress periods in financial and energy markets. We use several time-constant, time-varying and time-varying Markov-copula models to examine the dependence. Further, we use the delta conditional value-at-risk (ΔCoVaR) of Adrian and Brunnermeier (2016) and marginal expected shortfall (MES) of Acharya et al. (2012) to captures the risk spillover effects and give evidence of systemic risk. From the copula analysis, we find dissimilar dependence structure between returns series of oil and the G7 stock markets. For France, Germany and Japan, the dependence is Markov-switching time-varying, while it is time-varying for the United States and Canada, constant for the United Kingdom and around zero for Italy. Our empirical evidence on systemic risk indicates that oil price dynamics contributes significantly more to the G7 stock market returns during volatile times than during tranquil times. In particular, the Canada stock market appears more sensitive and vulnerable to negative external shocks emerging from the crude oil market than the other markets. Further, the country risk rankings identified using MES and ΔCoVaR may not be identical. In addition, the analysis results suggest that the crude oil market can be a good diversifier for investors in Japan and France and that the investors in the rest of G7 countries must act more carefully.

Firm age and crude oil returns: Stock price sensitivity of oil-producing and consuming companies

This study aims to identify firm characteristics that affect the cross-firm variation in oil–stock interactions. A panel data analysis with a sample of U.S. and Canadian firms reveals that the stoc...

Volatility Forecasts Embedded in the Prices of Crude-Oil Options

This paper evaluates and compares the ability of alternative option-implied volatility measures to forecast the monthly realized volatility of crude-oil returns. We find that a corridor implied volatility measure that aggregates information from a narrow range of option contracts consistently outperforms forecasts obtained by the popular Black-Scholes and model-free volatility expectations, as well as those generated by a high-frequency realized volatility model. In particular, this measure ranks favorably in all regression-based tests, delivers the lowest forecast errors under either symmetric or asymmetric loss functions, and generates economically significant gains in volatility timing exercises. Our results also show that the CBOE's oil-VIX (OVX) index performs poorly, as it routinely produces the least accurate forecasts.

Dynamic complexity and causality of crude oil and major stock markets

This paper investigates the causality and complexity evolution of two major crude oil returns and eight representative stock returns globally. Utilizing multiple methodologies originated from information theory and physics, i.e., the Kernel method Granger Causality Index and the Transfer Entropy, this study aims to provide new insights into the interrelationship between oil and equity markets. The study also applies the Sample Entropy to quantify the dynamic complexity of each individual market. Overall, nonlinear bidirectional causal relationships are found to be present and the corresponding information transfers between oil and stock markets are reported. Applying rolling windows and sub samples, information transfers are found to be generally time-varying, with the most common pattern featuring more significant and stronger information flows during the period of financial crisis. Moreover, the WTI and the Brent crude oil prices are not playing identical roles in their interactions with multiple stock markets either statically or dynamically. In future research, this study can be extended to consider delayed information transfers and to incorporate sectoral stock price information.

Forecasting the Volatility of Coffee Arabica and Crude Oil Prices with a High Frequency Data

Modeling, analyzing, and forecasting volatility has been the subject of extensive research among academics, practitioners and portfolio managers. This paper estimates a variety of GARCH models using weekly closing price (in USD/barrel) of Brent crude oil and weekly closing prices (in USD/pound) of coffee Arabica, and compares the forecasting performance of these models based on a high frequency intra-day data which allows for a more precise realized volatility measurement. The study used weekly price data to explicitly model volatility, and employed high-frequency intra-day data to assess model forecasting performance. The analysis points to the conclusion that GARCH (1,1) for Arabica coffee and GARCH(1,2) crude oil returns were best models, respectively with Student’s t distributed innovation terms is the most accurate volatility forecasting models in the context of our empirical setting. We recommend and encourage future researchers studying the forecasting performance of GARCH models to pay particular attention to the measurement of realized volatility, and employ high-frequency data whenever feasible. Keywords : commodity price, volatility, forecasting, GARCH DOI : 10.7176/JESD/10-23-04 Publication date: December 31 st 2019

Time-varied causality between US partisan conflict shock and crude oil return

To investigate the causality between US partisan conflict shock and crude oil return, this paper utilizes bootstrap full-sample Granger causality and bootstrap rolling window sub-sample Granger causality tests. Although no evidence supports the causal nexus between partisan conflict shock and crude oil return with full sample data, time-varied causality is observed for particular sub-samples. Such difference can be attributed to the parameter non-constancy in the VAR system. In terms of the empirical results, the US partisan conflict shock causes the fluctuations of the crude oil market in 2008–2009 and 2014–2015. However, such influence is temporary. Inversely, the crude oil return causes US partisan shock in 1985–1986, 1991–1994, 1998–2000 and 2012–2013. These findings indicate crude oil market significantly affects political stability of the US, especially during the periods of the Gulf War and OPEC production cut. The empirical results are robust in terms of both sample setting variation and window size selection.

Co-integration Analysis Between International Macroeconomic Factors and S&P Sensex Movements

The Indian stock market is fizzy and energetic; it has been going through many economic reforms since liberalization Indian economy (LPG) 1991 to till date. The Indian economy follows free market economic system, which enhance the scope of investing into stock market. Hence it prevailing significance of international macroeconomic factor on Indian Sensex movements, the present paper has investigate the long term relation and short term dynamics between international macro economic factors Capital account to Gross Domestic Production ratio (CAPGDPR), Crude oil Return (CRUDEOILR), Foreign Direct Investment return (FDIR), Foreign Institution Investment return (FIIR), Foreign Exchange Reserves growth rate (FOREXRESR) Gold return (GOLDR) Net Current account growth rate i.e (Exports divided by Imports) (NCAR) US Dollar Exchange rate to Indian Rupee returns (USDEXR) to Sensex return (SENSEXR) . The Sensex returns and International macroeconomic factors long term and short term analyzed through time series econometric tools Augmented Dickey Fuller (ADF) test check the stationarity, Johansen co integration for investigate long term relationship, Error correction Model for identify the short term dynamics. It is found that the long term co integration exists between these select international macroeconomic variables. Whereas USDEXR and FOREXRESR leads Sensex R and Sensex R corrects faster towards long run equilibrium. On the other hand CAPGDPR, CRUDEOILR, FDIR, FIIR, GOLDR, NCAR coefficients found the weak form of co movement to adjust for long run equilibrium.

Seasonality in crude oil returns

This paper explores the efficiency issue of the oil market in order to test the seasonal behavior of oil price returns, specifically day-of-the-week effect and month-of-the-year effect for the period December 1987 to January 2016. We use a dummy variable regression estimation technique to test seasonal anomalies for the Brent and WTI crude oil returns. Our empirical results find support for the negative Monday effect. The evidence of negative Monday returns is consistent with the relevant empirical literature. Moreover, the returns on Thursday are highest in a week followed by returns on Friday for both oil markets. This study also found evidence on month-of-the-year effect as the negative returns in November and December for Brent and WTI oil markets. Finally, this study is important for energy researchers, market participants, and policy-makers because anomalous oil markets’ behavior implies return predictability and the implementation of profitable investment strategies by market players and may also impact the macroeconomic variables and stock market returns.

Volatility spillover impact of world oil prices on leading Asian energy exporting and importing economies’ stock returns

Today Asia accounts for about 40% of the world oil trade and most of the trade is transpired within the region. This study presents an investigation of the volatility spillovers among leading Asian oil exporting countries and oil importing countries’ stock exchanges and crude oil returns. The model selection from multivariant GARCH models include BEKK-GARCH, DCC-GARCH, ABEKK-GARCH and ADCC-GARCH estimation techniques which are applied using daily data from 1st September 2009 to August 31st, 2018 for the three oil exporting countries (Saudi Arabia, United Arab Emirates, Iraq) and four oil importing countries (China, Japan, India, South Korea) stock exchanges with spot crude oil. Further, these estimation results are used to analyze the optimal portfolio weights for oil-stock portfolios. The bidirectional spillover is accepted for the correlation of two oil exporting countries (Saudi Arabia, Iraq) and South Korean stock return with oil returns. the Overall the results of all the oil exporting and oil importing countries show a different level of significant correlation with oil but the influence of oil shock are more pronounced on oil exporting countries. We also present a graphical picture of the stock correlation with oil by employing DCC-GARCH model that shows great shock and volatility correlation in the period of the oil crisis of 2014–2016. According to optimal portfolio weights and hedge ratios, the oil assets are a 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 in case of oil importing countries and equal ratio for the oil exporting countries.

Linkages between global crude oil market volatility and financial market by complexity synchronization

In this paper, we investigate the linkages between global crude oil market volatilities and financial markets and the degree of synchrony of crude oil markets and stock markets. A nonlinear cross-analysis of bivariate data method called CRP is applied to study the probability distribution of occurrence of similar states and the time span of occurrence of synchronization dynamics for crude oil return series and stock return series. Further, a new composite multiscale complexity invariance distance is introduced to measure the similarity of complexity between crude oil markets and stock markets. The results of this study show that there is a synchronization in the crude oil markets and stock markets, and those two systems have similar complexity from composite multiscale perspective.

Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests

Using the Google search volume index (GSVI) to measure investor attention, this paper investigates the relationships between investor attention and crude oil prices for the main crude oil markets worldwide. To account for possible structural breaks and nonlinearity in the relation between investor attention and oil returns, Fourier unit root test and nonlinear Granger causality tests are employed. The empirical results suggest that the bidirectional nonlinear Granger causality exists only between investor attention and WTI future crude oil return. However, WTI crude oil return Granger-causes investor attention weakly. For Dubai spot, Daqing spot, WTI spot and Brent future oil markets, unidirectional nonlinear Granger causality runs from investor attention to oil returns, which is relatively weak.

The forecasting power of EPU for crude oil return volatility

Economic policy uncertainty (EPU) has important implications for crude oil market. To explore the implications, this paper investigates the impact of EPU on the crude oil return volatility and which EPU index has the most forecasting power in crude oil market. To this end, we employ the GARCH-MIDAS model which can incorporate lower frequency EPU index variable with higher frequency crude oil return variable effectively. We find that EPU has a positive and significant impact on the crude oil return volatility, but the effect is short-lived and the decay period is about one year. Particularly, our results show that the US EPU index has the best forecasting power for crude oil return volatility over the long-term, whereas China EPU index has the best forecasting performance in the past one year. Our findings have important implications on risk management for investors in crude oil market.

Volatility Transmission Among Oil Price, Exchange Rate and Agricultural Commodities Prices

The aim of this article is to examine the interdependence relationship among the volatilities of crude oil price, U.S. dollar exchange rate, and a set of agricultural commodities prices. An autoregressive (AR) with an exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model or AR-EGARCH process and vector error correction model (VECM) approach was used on monthly data spanning from Jan 1986 to Dec 2005 as the pre-crisis period and from Jan 2006 to Nov 2015 as the post-crisis period. The results show that volatility in the agricultural commodity returns for most cases are affected by the volatility of the crude oil returns in the post-crisis period. Also, the volatility of the U.S. dollar exchange rate highly affects the agricultural commodities returns in the pre-crisis than the post-crisis periods. Furthermore, crude oil returns volatility does affect the U.S. dollar exchange rate volatility in the post-crisis period, which in turn affects the volatility of the agricultural commodities returns through changes in prices. The results of impulse response function (IRFs) are significant for most agricultural commodities volatility in the post-crisis period than the pre-crisis period.

Enhancing the predictability of crude oil markets with hybrid wavelet approaches

We explore the robustness, efficiency and accuracy of the multi-scale forecasting in crude oil markets. We adopt a novel hybrid wavelet auto-ARMA model to detect the inherent nonlinear dynamics of crude oil returns with an explicitly defined hierarchical structure. Entropic estimation is employed to calculate the optimal level of the decomposition. The wavelet-based forecasting method accounts for the chaotic behavior of oil series, whilst captures drifts, spikes and other non-stationary effects which common frequency-domain methods miss out completely. These results shed new light upon the predictability of crude oil markets in nonstationary settings.

Moment spreads in the energy market

In this paper, we first extend the variance risk premium (VRP) in Bollerslev, Tauchen and Zhou (2009) into the moment spreads, including the skewness spread (SKS) and the kurtosis spread (KTS), and then investigate the return predictability of the moment spreads in the energy market. There are three main findings. First, VRP can significantly predict energy market returns at daily and weekly frequencies. For example, the weekly in-sample and out-of-sample R2 for predicting the weekly Energy Select Sector SPDR ETF (XLE) excess returns are 1.13% and 1.32%, respectively. Second, VRP can significantly predict crude oil returns (i.e., returns of both crude oil futures and crude oil ETF) at a monthly frequency with both monthly in-sample and out-of-sample R2 of around 7%. Finally, the kurtosis spread (KTS) of the energy market is an important predictor for excess natural gas returns (i.e., returns of both natural gas futures and natural gas ETF) across different horizons.

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.