Market efficiency and information flow between the crude palm oil and crude oil futures markets

This study analyzes the efficiency of the crude palm oil (CPO) futures market by conducting a variance ratio test and comparing it to the West Texas Intermediate (WTI) futures market. We discover that the weak-form efficient market hypothesis holds for both the CPO and WTI futures markets despite the significant difference in their liquidity. Using a scaling exponent, we investigate speculative trading activities and find that trading CPO futures in expectation of significant returns does not strongly involve a high level of risk unlike WTI futures. Our findings regarding market efficiency of the two futures markets are supported by the significant integration of the two with similar level of information flow from each market to the other. To explore the role of speculation in their market integration, we introduce a natural experimental setting using the coronavirus disease 2019 (COVID-19) pandemic, which caused a sudden decrease in the demand for fuel. The bidirectional information flow between the two markets is intensified after the COVID-19 pandemic due to lower level of speculation. The findings suggest that (i) stakeholders in the CPO market need to pay attention to the crude oil markets to anticipate its price changes, (ii) investors can use WTI futures as a hedging tool against CPO futures as long as there is mutual information flow, and (iii) regulators should carefully implement new CPO futures market policy, as either asymmetric changes in speculation or unbalanced regulation with the WTI futures market can create market distortion and regulatory arbitrage. © 2022 The Authors

Price discovery in China's crude oil futures markets: An emerging Asian benchmark?

We examine the price discovery performance of China's crude oil futures traded on the Shanghai International Energy Exchange (INE) for the spot prices of 19 types of deliverable and nondeliverable Asian crude oil. We find evidence for the INE crude oil futures price discovery function even at the early stage for almost all the deliverable crudes and some nondeliverable crudes. Both the INE crude oil futures price and the spot price significantly contribute to the price discovery process, with substantially time‐varying informational roles. While the price discovery performance was severely damaged around the period of COVID‐19 pandemic shock intensification in China with the temporary cancellation of nighttime trading, it improved to some extent after China started the recovery from the shock. But such improvement deteriorated drastically and disappeared since early 2021. Further analysis reveals that both economic fundamentals (e.g., the warehouse inventory) and trading‐related characteristics of the futures market are significant determinants of the price discovery performance. The overall findings imply that the INE crude oil futures market has evolved into a useful and important information source in pricing Asian crudes, and is on the path to emerge as an Asian benchmark.

Analyzing pure contagion between crude oil and agricultural futures markets

Crude oil and agricultural product prices are important factors affecting a country's economic and social stability. The pure contagion between these two markets may lead to excessive price linkage, increasing the fragility of the financial system. This paper uses the CEEMDAN method, fine-to-coarse reconstruction method, and TVP-VAR model to study the pure contagion between oil and agricultural futures markets. The empirical results show that there always is significant pure contagion between agricultural futures markets. However, pure contagion between crude oil and agricultural futures markets only exists in some specific periods. The crude oil futures market has obvious pure contagion to the agricultural futures markets in most periods. Only a few periods the agricultural futures have pure contagion to the crude oil futures. It is worth noting that the COVID-19 epidemic aggravates the pure contagion between crude oil and the agricultural futures markets. Based on the research conclusions, this paper puts forward corresponding policy recommendations, hoping to provide reference and theoretical basis for the government to formulate corresponding policies.

Forecasting the Asian stock market volatility: Evidence from WTI and INE oil futures

This study investigates whether China's crude oil futures (INE) and West Texas Intermediate (WTI) markets hold valuable information for estimating the realized volatility of seven Asian stock markets. This study has several notable findings. First, China's oil futures can trigger forecast accuracy for three equity indices (Nikkei 225, NSEI, and FT Straits Times), whereas WTI helps forecast the volatility of the two indices (KSE 100 and KOSPI). Second, comparing China's crude oil futures with WTI's crude oil futures, we find that the former could be an effective indicator for all seven Asian stock markets during a high-volatility period, while WTI information is helpful in forecasting the volatility of the KSE 100, NSEI, and FT Strait Times during the low-volatility period. Further, information of both oil futures is ineffective for the Hang Seng and SSEC equity indices. Our results are robust in several robustness checks, including alternative evaluation methods, recursive window approach, and alternative realized measures, even during the COVID-19 pandemic.

The Short-Term Effect of COVID-19 Pandemic on China’s Crude Oil Futures Market: A Study Based on Multifractal Analysis

The ongoing COVID-19 shocked financial markets globally, including China’s crude oil future market, which is the third-most traded crude oil futures after WTI and Brent. As China’s first crude oil futures are accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore, this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare the market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly used random walk tests. Then, we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring. [ FROM AUTHOR]

An oil futures volatility forecast perspective on the selection of high-frequency jump tests

This paper examines the forecasting performances of high-frequency jump tests for oil futures volatility from a comprehensive perspective. It contributes to the literature by investigating which jump test is the best for oil futures volatility forecasting under different circumstances and whether the jump component extracted from multiple alternative tests is useful for further improving forecasting performance. Our results show that the jumps of the TOD test (Bollerslev et al., 2013) have satisfactory performance over the medium-term and especially the short-term forecasting horizons. Most importantly, the jump components from the intersection of multiple intraday tests further improve the forecasting performance. A variety of further discussions, including models controlling for stock market effects and considering periods of high (low) volatility and the COVID-19 pandemic period, confirm the conclusions. This paper attempts to shed light on oil futures volatility prediction from the perspective of jump test selection.

Price discovery in China's crude oil futures markets: An emerging Asian benchmark?

We examine the price discovery performance of China's crude oil futures traded on the Shanghai International Energy Exchange (INE) for the spot prices of 19 types of deliverable and nondeliverable Asian crude oil. We find evidence for the INE crude oil futures price discovery function even at the early stage for almost all the deliverable crudes and some nondeliverable crudes. Both the INE crude oil futures price and the spot price significantly contribute to the price discovery process, with substantially time-varying informational roles. While the price discovery performance was severely damaged around the period of COVID-19 pandemic shock intensification in China with the temporary cancellation of nighttime trading, it improved to some extent after China started the recovery from the shock. But such improvement deteriorated drastically and disappeared since early 2021. Further analysis reveals that both economic fundamentals (e.g., the warehouse inventory) and trading-related characteristics of the futures market are significant determinants of the price discovery performance. The overall findings imply that the INE crude oil futures market has evolved into a useful and important information source in pricing Asian crudes, and is on the path to emerge as an Asian benchmark.

The impacts of infectious disease pandemic on China’s edible vegetable oil futures markets: A long-term perspective

For the extremely important role of China in global edible vegetable oil market and its decisive measures in the epidemic controlling and stable economic recovery during the COVID-19 pandemic, the aim of this article is to inspect the quantitative impacts of infectious disease pandemic on the returns, volatilities and correlations of China's edible vegetable oil futures markets by using a DCC-MVGARCH-X model incorporating Baidu searching index as the proxy of pandemic severity. Our empirical results show that infectious disease pandemic does have significantly positive impacts on the returns and volatilities of China's soybean, canola and palm oil futures markets. Second, there are significant volatility spillover effects among the three vegetable oils, suggesting strong contagion effect from one oil market to the others. Third, soybean oil and palm oil show the largest correlation, while the dependence between canola oil and palm oil is the smallest one among the three pairwise correlations. Moreover, no matter to consider epidemic situation in China or in global environment, infectious disease pandemic has significant effects on these correlations.

Forecasting Crude Oil Future Volatilities with a Threshold Zero-Drift GARCH Model

The recent price crash of the New York Mercantile Exchange (NYMEX) crude oil futures contract, which occurred on 20 April 2020, has caused history-writing movements of relative prices. For instance, the West Texas Intermediate (WTI) experienced a negative price. Explosive heteroskedasticity is also evidenced in associated products, such as the Intercontinental Exchange Brent (BRE) and Shanghai International Energy Exchange (INE) crude oil futures. Those movements indicate potential non-stationarity in the conditional volatility with an asymmetric influence of negative shocks. To incorporate those features, which cannot be accommodated by the existing generalized autoregressive conditional heteroskedasticity (GARCH) models, we propose a threshold zero-drift GARCH (TZD-GARCH) model. Our empirical studies of the daily INE returns from March 2018 to April 2020 demonstrate the usefulness of the TZD-GARCH model in understanding the empirical features and in precisely forecasting the volatility of INE. Robust checks based on BRE and WTI over various periods further lead to highly consistent results. Applications of news impact curves and Value-at-Risk (VaR) analyses indicate the usefulness of the proposed TZD-GARCH model in practice. Implications include more effectively hedging risks of crude oil futures for policymakers and market participants, as well as the potential market inefficiency of INE relative to WTI and BRE.

Information Flow Between Crude Palm Oil and Crude Oil Futures

This study finds asymmetric information flow from the crude palm oil (CPO) futures to the West Texas Intermediate (WTI) crude oil futures market despite the CPO futures market's low liquidity and small market capitalization. Our finding is robust regardless of the 2019 Coronavirus outbreak and the asymmetric information flow becomes even unilateral considering the exchange rate risk on the Malaysian Ringgit. Finally, we explain the asymmetric information flow from the CPO futures to WTI futures market given that the impact of speculation on market efficiency crowds out that of liquidity. © Published under licence by IOP Publishing Ltd.

Intra-day co-movements of crude oil futures: China and the international benchmarks.

Investigating the co-movements between crude oil futures helps to understand the integration of the global markets. This paper focuses on Shanghai crude oil futures (INE) and study its co-movements with the international benchmarks of WTI and Brent crude oil futures in intra-day day and night trading sessions. A complex network model framework is proposed to analyse the intra-day co-movement patterns labelled by a functional data clustering approach on intra-day return curves. Our findings indicate INE is more integrated with the global market during the night session, but it shows a regional fractional effect during the day session. Based on the revealed dynamics of co-movement patterns, we further design a pairs trading strategy between INE crude oil futures and the international benchmarks. The simulation results show that the pairs trading strategy can be promisingly profitable, even during market turmoil phases.

The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic

The purpose of this article is to investigate whether various uncertainty measures provide incremental information for the prediction the volatility of crude oil futures under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, by considering the information overlap among various uncertainty measures and fully using of the information in various uncertainty measures, this paper uses two prevailing shrinkage methods, the least absolute shrinkage and selection operator (lasso) and elastic nets, to select uncertainty variables during the entire sampling period, before the COVID-19 pandemic and during the COVID-19 pandemic and then uses the HAR model to predict crude oil volatility. The results show that (i) uncertainty measures can be utilized to predict crude oil volatility under the high-frequency framework in both in-sample and out-of-sample analyses. (ii) Because of the information overlap between various uncertainty measures, adding a large number of uncertain variables to the HAR model may not significantly improve the volatility prediction. (iii) Before and during the COVID-19 pandemic, Chicago Board Options Exchange (CBOE) crude oil volatility (OVX) has the greatest impact on crude oil volatility, infectious disease equity market volatility (EMV) exerts a significant influence on crude oil futures volatility forecasts during the COVID-19 period, and CBOE implied volatility (VIX) and the financial stress index (FSI) have substantial impacts on crude oil futures volatility forecasts before COVID-19.

The Time-Varying Connectedness Between China's Crude Oil Futures and International Oil Markets: A Return and Volatility Spillover Analysis.

This paper examines the relationship between world crude oil markets following the introduction of Shanghai crude oil futures from the perspective of network connectedness based on the vector autoregressive model. The connectedness measurement method proposed by Diebold and Yilmaz (Econ J 119(534):158-171, 2009, Int J Forecast 28(1):57-66, 2012. 10.1016/j.ijforecast.2011.02.006, J Econom 182(1):119-134, 2014. 10.1016/j.jeconom.2014.04.012) is adopted to study a time-varying interdependence relationship. The empirical results show that the world crude oil markets exhibit a high degree of integration from both returns and volatility; however, the direction and magnitude contributed by each market varies significantly. Specifically, the West Texas Intermediate futures and Brent spot and futures markets were found to have the highest contributions to the world oil market over the entire sample period and take leading roles, whereas Dubai futures market was found to be the most important receiver, and has received the most spillover from other markets and passed it throughout the system. Shanghai crude oil futures is not yet highly connected with other markets. Moreover, heterogeneous changes in the direction, intensity, and persistence of the spillover were observed across markets after the outbreak of the COVID-19 pandemic in 2020. This study reveals the integration level of Shanghai crude oil futures and the dynamics of linkages between regional crude oil markets, which is of great significance for market participants, policymakers, and future researchers. Supplementary Information: The online version contains supplementary material available at 10.1007/s12076-021-00288-z.

Extreme co-movements between infectious disease events and crude oil futures prices: From extreme value analysis perspective

This paper examines the extreme co-movements between infectious disease events and crude oil futures through extreme value analyses. We contribute to the literature by providing a novel framework of tail risk early warning and considering infectious diseases as a systemic risk factor for crude oil futures. The results provide evidence that: (1) when an extreme event occurs, the tail index of the infectious disease reaches its empirical lower threshold, which is approximately 2.30;(2) when a jump in volatility corresponding to the severeness of the epidemic is observed, the tail index reaches the lower bound, but not reversely;(3) both upside and downside extreme co-movements exist, whereas they are asymmetric;and (4) each tail quotient correlation coefficient keeps rising and reaches a peak before crises and fall sharply with the collapse of crude oil markets. The findings can offer implications for government officials, investors, portfolio managers, and policymakers, respectively.

The impact of COVID-19 on the interdependence between US and Chinese oil futures markets

The oil futures market plays a vital role in the global financial system, especially after the negative future oil price rose during the COVID-19 pandemic. This paper investigates the COVID-19 impact on the interdependence between the US and Chinese oil futures markets by extending the dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (DCC-GARCH) models with incorporating COVID-19 variables and by applying vector autoregression (VAR) models. Our study reveals that the COVID-19 pandemic enhanced the long-run correlation between the two oil markets. In contrast, daily changes in pandemic severity had a negative effect on the short-term transient correlation. Our results show that COVID-19 changed the one-direction causality from the US oil market to the Chinese market in the pre-COVID period to a bidirectional causal relation between the two markets during the COVID period. It strengthened the volatility spillover effect from the Chinese to US markets. These findings are helpful to regulars' monitoring oil supply chain risk and investors' cross-market hedging of spillover risks from a systematic risk perspective.

Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?

In this paper, we try to forecast the volatility of Chinese crude oil futures (COF) using multiple economic policy uncertainty indicators. MIDAS-RV model is combined with the principal component analysis (PCA), scaled PCA (SPCA) and partial least squares (PLS) techniques in this work, construct the newly MIDAS-RV-PCA, MIDAS-RV-PLS and MIDAS-RV-SPCA models, their prediction performance is compared with the common combination forecasting methods. The in-sample estimation analysis on MIDAS-RV-X models show the that it is necessary to consider multiple economic policy uncertainty indices when predicting the Chinese COF volatility and the in-sample analysis on dimensionality reduction model further demonstrate the rationality of using dimensionality reduction techniques. The out-of-sample evaluation results show that the MIDAS-RV-SPCA is a superior model when forecasting the short-term volatility of Chinese COF using multiple economic policy uncertainty indicators, especially during the periods of high volatility and COVID-19 pandemic. The results also indicates that the DMSPE(0.9) method in the combination forecasting method shows its superior forecasting ability in long-term volatility of Chinese COF, especially during the low volatility and pre-pandemic period.

Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic.

In this study, we focus on the role of jumps and leverage in predicting the realized volatility (RV) of China's crude oil futures. We employ a standard mixed data sampling (MIDAS) modeling framework. First, the in-sample results indicate that the jump and leverage effects are useful in predicting the RV of Chinese crude oil futures. Second, the out-of-sample results suggest that jump has very significant predictive power at the one-day-ahead horizon while the leverage effect contains more useful information for long-term predictions. Moreover, our results are supported by a number of robustness checks. Finally, we find new evidence that the prediction model that considers the leverage effect has the best predictive power during the COVID-19 pandemic.

The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China.

Based on the high-frequency heterogeneous autoregressive (HAR) model, this paper investigates whether coronavirus news (in China and globally) contains incremental information to predict the volatility of China's crude oil, and studies which types of coronavirus news can better forecast China's crude oil volatility. Considering the information overlap among various coronavirus news items and making full use of the information in various coronavirus news items, this paper uses two prevailing shrinkage methods, lasso and elastic nets, to select coronavirus news items and then uses the HAR model to predict China's crude oil volatility. The results show that (i) coronavirus news can be utilized to significantly predict China's crude oil volatility for both in-sample and out-of-sample analyses; (ii) the Panic Index (PI) and the Country Sentiment Index (CSI) have a greater impact on China's crude oil volatility. Additionally, China's Fake News Index (FNI) have a significant impact on China's crude oil volatility forecast; and (iii) global coronavirus news provides more incremental information than China's coronavirus news for predicting the volatility of China's crude oil market, which indicates that global coronavirus news is also a key factor to consider when predicting the market volatility of China's crude oil.

Multidimensional risk spillovers among crude oil, the US and Chinese stock markets: Evidence during the COVID-19 epidemic.

This paper investigates the multidimensional risk spillovers among crude oil, the US and Chinese stock markets during the COVID-19 epidemic through a GARCHSK-Mixed Copula-CoVaR-Network method. Firstly, we find that during the COVID-19 period, the oil-stock risk spillovers are obviously stronger than those during the normal period. And there are significant risk spillovers from the US and Chinese stock markets to the oil markets. It is also discovered that the oil markets are greatly influenced by the second board stock markets, also known as the growth enterprise markets, especially during the COVID-19 outbreak. Furthermore, the bidirectional China-oil risk spillovers during the COVID-19 pandemic have rapidly increased. Besides, it is reported that the relationships across oil futures, main board and second board stock markets in the US and China are stable under different TSI levels and extreme events. Finally, the GARCHSK-Mixed Copula-CoVaR-Network outperforms the control groups in terms of marginal distribution and dependence structure. Our study not only offers new method and insight into the oil-stock relationship, but also has economic implications for investors and policymakers.

Examining the predictive information of CBOE OVX on China's oil futures volatility: Evidence from MS-MIDAS models.

This study evaluates whether CBOE crude oil volatility index (OVX) owns forecasting ability for China's oil futures volatility using Markov-regime mixed data sampling (MS-MIDAS) models. In-sample empirical result shows that, OVX can significantly lead to high future short-term, middle-term and long-term volatilities with regard to Chinese oil futures market. Moreover, our proposed model, the Markov-regime MIDAS with including the OVX (MS-MIDAS-RV-OVX), significantly outperforms the MIDAS and other competing models. Unsurprising results further confirm that OVX indeed contain predictive information for oil realized volatility (especially significant and robust in middle-term and long-term horizons) and regime switching is useful to deal with the structural break within the energy market. We carry out economic value analysis and discuss OVX's asymmetric effects concerning different trading hours and good (bad) OVX, and find OVX performs better in day-time trading hours and the good OVX is more predictive for the oil futures RV than the bad OVX. The further discussion also confirms our previous conclusions are robust during the highly volatile period of the COVID-19 pandemic.