WTI Oil Research Articles

Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction

Abstract Modeling the volatility of energy commodity returns has become a topic of increased interest in recent years, because of the important role it plays in today's economy. In this paper we propose a novel copula-based stochastic volatility model for energy commodity returns that allows for asymmetric volatility persistence. We employ Approximate Bayesian Computation (ABC), a powerful tool to make inferences and predictions for such highly-nonlinear model. We carry out two simulation studies to illustrate that ABC is an appropriate alternative to standard MCMC-based methods when the state transition process is challenging to implement. Finally, we model the volatility of WTI and Brent oil futures' returns with the proposed copula-based stochastic volatility model and show that such model outperforms symmetric alternatives in terms of in- and out-of-sample volatility prediction accuracy.

Crude oil prices and clean energy stock indices: Lagged and asymmetric effects with quantile regression

Unlike previous studies examining the association between crude oil and renewable energy stock prices under average conditions, we employ a quantile-based regression approach offering a more comprehensive dependence structure under diverse market conditions. Using weekly data covering crude oil prices (WTI market) and three clean energy stock indices (the Wilderhill Energy Index, MAC Global Solar Energy Index, and S&P Global Clean Energy Index), quantile regression analyses provide solid evidence of the decreasing dependence of clean energy stock returns on crude oil returns. The lagged effect of WTI oil returns on clean energy stock returns is generally significant, which indicates that clean energy stock returns react differently to new information on oil returns under different market conditions. We further check for asymmetrical effects of oil returns on clean energy stock returns in various market conditions and find a strong effect of negative oil returns during bearish periods and an insignificant effect during bullish episodes.

DO OIL PRICE SHOCKS GIVE IMPACT ON FINANCIAL PERFORMANCE OF MANUFACTURING SECTORS IN INDONESIA?

The panel vector autoregression model is estimated using three main variables related to with profitability, financial liquidity, and financial leverage for 94 manufacturing companies from 2000 to 2017 in Indonesia. The aim is to examine the impact of oil price shocks on the ROA (profitability), CR (financial liquidity), and DER (financial leverage). The impulse reaction function of samples reveals some remarkable results. First, the response of ROA, DER, and CR appears to be consistent in many ways. Second, either Brent oil or WTI oil gives the same result for these variables. Third, financial liquidity for Indonesia manufacturing companies is not affected by the oil prices. The results obtained are robust following the GMM model in the estimation of the panel VAR.Keywords: Oil price shocks, Panel VAR, Impulse Reaction Function, GMM modelJEL Classifications: L6, Q4DOI: https://doi.org/10.32479/ijeep.9808

EFFECT OF MACROECONOMIC VARIABLES ON JAKARTA ISLAMIC INDEX: EVIDENCE THE GLOBAL TRADE WAR PHENOMENON

The development of investment in the Islamic capital market, especially the Jakarta Islamic Index (JII) as a percentage, experienced significant development, and it is because the Islamic index uses Islamic principles and procedures. The phenomenon of the trade war between the United States and China has an impact on macro variable fluctuations, which can empirically influence the growth of the sharia index. For this reason, this study aims to analyze the impact of change due to the trade war sentiment. Macroeconomic variables used in this study are the USD / IDR exchange rate, the CNY / IDR exchange rate, inflation, Crude oil WTI, and ICP Crude oil. This study uses a vector autoregression analysis (VAR) technique. Stationarity test using the Augmented Dickey-Fuller test (ADF test) and the Philips-Perron Test. The analysis shows that there is an influence between changes in exchange rates and changes in crude oil prices on the return of the Jakarta Islamic Index (JII) in the range of research data periods used.

The Dynamics of the S&P 500 under a Crisis Context: Insights from a Three-Regime Switching Model

This paper provides an econometric analysis aiming at evidencing the dynamics showed by the S&P 500 market index during the period of 4 January 2001–28 April 2020, in which the subprime crisis has taken place and the COVID-19 crisis has begun. In particular, we fit a three-regime switching model that allows market parameters to behave differently during economic downturns, with the regimes representative of the tranquil, volatile, and turbulent states. We document that the tranquil regime is the most frequent for the whole period, while the dominant regime is the volatile one for the crisis of 2008 and the turbulent one for the first four months of 2020. We fit the same model to the returns of the Dow Jones Industrial Average index and find that during the same period of investigation, the most frequent regime has been the tranquil one, while the volatile and turbulent regimes share the same frequencies. Additionally, we use a multinomial logit model to describe the probabilities of volatile or turbulent regimes. We show that, in the case of the S&P 500 index, the returns from the Volatility Index (VIX) index are significant for both the volatile and the turbulent regimes, while the gold, WTI oil, and the dollar indices have some explanatory power only for the turbulent regime.

Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis

Abstract This paper examines the flow of information and its direction between the oil volatility index (OVX) and the spot variance of WTI and Brent returns. Since OVX is an indicator of the investor sentiment about oil market performance, we aim first at evidencing whether there is an exchange of information between OVX and the spot variance of the two crudes. Moreover, since OVX is linked to WTI crude oil, it is useful to investigate whether it shares an information content with Brent. To this purpose, we propose an entropy-based approach which exploits two non-parametric methods: the mutual information and the transfer entropy. The results show an increase in the information flow between OVX and the spot variance of Brent returns and a corresponding decrease in the information flow with WTI. Furthermore, the direction of the information flow comes from OVX to both oil spot variances, thus investor sentiment about oil market performance drives uncertainty in the corresponding spot market. However, the information flow from the oil spot variances to OVX is more statistically significant for WTI than for Brent and, since transfer entropy is a measure of resolution of uncertainty, we demonstrate that the spot variance of WTI returns helps more in reducing uncertainty about OVX (than Brent).

Dynamic Transmissions Between Oil Specific Shocks and Financial Stress: Evidence From the Euro Area

In this study, we investigate dynamic transmissions between oil market and Euro area financial stress by implementing the TVP-VAR model. Our data cover monthly WTI oil price, global oil production, the Kilian Index and a measure for financial stress for the Euro area (Composite Indicator of Systemic Stress, CISS) and range from September 2000 to December 2018. Empirical results of the study verify that, the TVP-VAR model captures dynamic nature of the structural shocks arisen from the global oil market to the Euro area financial stress consistently and robustly.

GARCH-type volatility in the multiplicative quadrinomial tree method: An application to real options

This article applies the multiplicative quadrinomial tree numerical method with non-constant volatility to assess a real option of abandonment, based on an estimate of the conditional volatility for WTI oil commodity prices and their respective equivalence in a GARCH-diffusion model. The methodology refers to the use of an estimate of type GARCH (1,1) and the numerical method through a quadrinomial tree. There are two main findings: 1) when employing the quadrinomial method, the value of the real option turned out to be greater than the value estimated through the traditional multiplicative binomial method, due to underestimation of the real value of volatility that occurs in a specific period according to the latter method; and 2) a methodological contribution that demonstrates plainly way the presence of non-constant conditional volatility as well as being able to value all types of options using stochastic volatility.

Information in the Term Structure of WTI Crude Oil Futures

Nelson-Siegel factors extracted from the term structure of WTI oil futures predict subsequent WTI holding period returns both in-sample and out-of-sample. This predictability is not diminished by augmenting with macroeconomic indicators or oil market specific predictors. The term structure based predictability is distinct from the predictability in macroe- conomic fundamentals. However, controlling for information in the term structure renders oil market specific fundamentals insignificant in predict- ing holding period returns, with the exception of lagged spot returns. We also find that the most significant predictor of holding period returns is a time-varying decay factor in the term structure model.

Analisis Pengaruh Variabel Makro Terhadap Return Indeks Saham Syariah Di Indonesia: Studi Pada Fenomena Perang Dagang Global

The development of investment in the Islamic capital market, especially the Jakarta Islamic Index (JII) as a percentage experienced a significant development, it is because the Islamic index uses Islamic principles and procedures. The phenomenon of the trade war between the United States and China has an impact on macro variable fluctuations, which can empirically influence the growth of the sharia index. For this reason, this study aims to analyze the impact of change due to the trade war sentiment. Macroeconomic variables used in this study are the USD / IDR exchange rate, the CNY / IDR exchange rate, inflation, Crude oil WTI and ICP Crude oil. This study uses vector auto regression analysis (VAR) technique. Stationarity test using the Augmented Dickey-Fuller test (ADF Test) and the Philips-Perron Test. The analysis shows that there is an influence between changes in exchange rates and changes in crude oil prices on the return of the Jakarta Islamic Index (JII) in the range of research data periods used

Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms

This paper investigates hybrid time series forecasting models, which are based on combinations of ensemble empirical mode decomposition and least squares support vector machines. Several algorithms are considered: the genetic algorithm, the grid search, and particle swarm optimization. Theoretical guarantees of prediction accuracy are tested with sine curves. From a numerical testing perspective, we are interested in showing the superiority of one approach to another based on theoretical prediction and time series applications in finance (S&P 500), commodities (WTI oil price), or cryptocurrencies (Bitcoin). The superiority of hybrid models to soft- and hard-computed models is further assessed through a ‘horse race’ and trading performance, as well as through fine-tuning of the algorithms.

Can Crude Oil Price be a Predictor of Stock Index Return? Evidence from Vietnamese Stock Market

This paper tests the predictive power of crude oil price returns in forecasting Vietnamese stock index returns. We used the VN index and HNX index to calculate stock index returns and WTI and BRENT oil prices. Using a daily sample from 4th January 2006 to 31st December 2017, our analysis focused on both in-sample and out-of-sample predictability by applying the Westerlund and Narayan (2015) feasible generalized least square (FGLS) estimator which corrects persistency heteroskedasticity and endogeneity problems. We showed that the crude oil prices are reliable predictors of Vietnamese stock index returns. In terms of in-sample predictability, thirteen out of sixteen predictive regression were significant. We found that the BRENT crude oil index is slightly more powerful than the WTI crude oil price in predicting Vietnam stock index returns with seven out of eight regressions being significant compared to six out of eight from that of the WTI oil price. In terms of out-of-sample predictability, our results were also complemented by a robustness test, i.e. competing with a constant return model which used the historical average as the predictive value.

The relationship between oil prices and the Brazilian stock market

Crude oil remains a very important product not only because of its regular use, but also because it is a very important financial asset, influencing the economy as a whole. In this paper, we assess how WTI oil price shocks are related with the Brazilian economy as a whole, but also with each of the listed companies in Ibovespa, searching for the relationships with different economic activities. Based on the Detrended Cross-Correlation Analysis correlation coefficient, which allows us to analyse the impacts for different time scales, we conclude unsurprisingly that the most affected sectors are those most related with the use of oil. However, another important result is the significant correlation between oil price shocks and the returns of the financial sector, showing this particular sector’s exposure to oil, i.e., this is one of the sectors most correlated with oil returns. This is relevant not only for investors but also for authorities, because possible future oil shocks could have a high impact on the Brazilian financial sector

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

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

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.

Energy, precious metals, and GCC stock markets: Is there any risk spillover?

We analyze dynamic return and risk spillovers between commodity futures (energy & precious metals) and the Gulf Cooperation Council (GCC) stock markets. Utilizing dynamic equicorrelation (DECO) models and the spillover index of Diebold and Yilmaz (2012), we show the existence of significant return and risk spillovers between the commodities and the GCC stock markets, particularly during the onset of the 2008–2009 global financial crisis. In addition, silver, platinum, and energy futures markets are net transmitter of returns to stock markets. Precious metals (except silver) and WTI oil are net transmitter of risk to GCC markets. Abdu Dhabi and Dubai are net transmitter of returns and risk to other markets. Moreover, portfolio management analysis shows that the mix of commodities and GCC equities provides diversification opportunities for different crisis periods. Finally, precious metal markets offer superior hedging effectiveness over energy markets for all GCC markets.

Spillovers between oil and stock returns in the US energy sector: Does idiosyncratic information matter?

This paper investigates the inter-connectedness between WTI oil price returns and the returns of listed firms in the US energy sector. Specifically, we focus on the issue of whether firm-level idiosyncratic information matters. A generalised dynamic factor model is used to separate common components from idiosyncratic components in these energy stocks. Systemic connectedness is then estimated following Diebold and Yilmaz (2014). Our empirical results demonstrate the important role of industrial-level common information in understanding the oil–stock relationship. A number of interesting points include: the US energy sector is the net contributor to WTI price changes, but the effect is mainly driven by industrial-level common information; the oil and gas industry dominates other industries in the energy sector; the dynamic analysis shows that although idiosyncratic information is mostly independent of oil shocks, individual energy stock returns do respond to WTI price movements.

Modeling and Testing Volatility Spillovers in Oil and Financial Markets for the USA, the UK, and China

The main purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The prices of oil and its interactions with financial markets make it possible to determine the associated prices of financial derivatives, such as carbon emission prices. The approach taken in the paper is different from others in the literature; the purpose is to examine the usefulness of modeling and testing volatility spillovers in the oil and financial markets. The paper investigates co-volatility spillovers (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-volatility in another physical or financial asset) between the oil and financial markets. The oil industry has four major regions, namely North Sea, the USA, Middle East, and South-East Asia. Associated with these regions are two major financial centers, namely the UK and the USA. For these reasons, the data to be used are the returns on alternative crude oil markets, returns on crude oil derivatives, specifically futures, and stock index returns in the UK and the USA. Given the importance of the Chinese financial and economic systems, the paper also analyzes Chinese financial markets, where the data are more recent. The USA and China are the world’s two largest economies and the UK is the world’s sixth largest economy (and second in the existing EU) behind the USA, China, Japan, Germany, and India. Moreover, the USA and the UK are associated with WTI and Brent oil, respectively. One of the purposes of the paper is to examine how China might be different from the USA and the UK, which seems to be borne out in the empirical analysis. Based on the conditional covariances to test the co-volatility spillovers, dynamic hedging strategies will be suggested to analyze market fluctuations in crude oil prices and associated financial markets.

The Value at Risk of Selling Option on Crude Oil West Texas Intermediate

The Value at Risk (VaR) of selling option on crude oil WTI has not been widely known, whereas this trade is the largest transactions in the world. This study aimed to analyze the Value at Risk (VaR) of the far out of the money (FOTM) and the in the money (ITM) strike position of selling option on crude oil WTI investment. The monthly option premium return data ranging from April 1984 to May 2017 was analyzed by the ARCH-GARCH and VaR method to get the risk of FOTM and ITM strike position. Empirical results indicate that the risk of the FOTM strike was much lower than the ITM strike positions. It meant that selecting the FOTM strike position of the selling option on crude oil WTI investment could be considered by stakeholders because its risk was much lower than the ITM strike position.

Testing the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the crude oil prices

This paper examines the existence of the adaptive market hypothesis (AMH) as an evolutionary alternative to the efficient market hypothesis (EMH) by applying daily returns on the three benchmark crude oils. The data coverage of daily returns is from 2003 to 2018. The automatic portmanteau and generalized spectral tests is applied in this study. The results show that the Brent and the WTI oil markets possess the highest efficiency levels. In addition, the behavior of OPEC basket data represents that when we approaching toward longer window lengths (e.g. from 100 to 500-days); the degree of conformity with AMH decreases.