Crude Oil Spot Market Research Articles

Transformer-based forecasting for intraday trading in the Shanghai crude oil market: Analyzing open-high-low-close prices

The Shanghai crude oil futures market exudes distinct speculative attributes, underscoring the pivotal significance of precise price forecasts. Accurate forecasting of Shanghai crude oil futures prices assumes vital importance for investors to optimize their portfolios profitably, for producers to mitigate production risks in the crude oil spot market, and for providing cogent decision-making support to government entities. This study implements a groundbreaking unbiased structural forecasting for Shanghai crude oil futures' open-high-low-close (OHLC) prices leveraging the Transformer framework coupled with the model-driven and penalty term-based loss function designs. Based on OHLC forecasts, this study devises three intraday trading strategies. Notably, our results evince that the forecasting accuracy of the Transformer outperforms the Naïve method, vector autoregression (VAR) and vector error correction model (VECM), multiple linear regression (MLR), support vector regression (SVR), and long short-term memory (LSTM) neural network when applied to different temporal granularities of Shanghai crude oil futures OHLC data. Furthermore, the three proposed intraday trading strategies exhibit higher annualized return rates and Sharpe ratios, alongside lower maximum drawdowns and standard deviations of returns in comparison to the conventional close-to-close strategy relying solely on the close price. Remarkably, the forecasting process and intraday trading strategies explicated in this study can equally apply to other futures products in the energy sector, including electricity, coal, and natural gas.

An ARDL approach to study the cointegration relations between the Shanghai crude oil futures and global markets

ABSTRACT The Shanghai crude oil futures market, as the largest crude oil market in the Asia-Pacific region, is an important complement to the global crude oil market. We use the autoregressive distributed lag model (ARDL) to examine long-term and short-term cointegration relations between the Shanghai crude oil futures market and China’s crude oil spot market (Daqing), international (WTI and Brent) benchmarks. Based on the price data of the Shanghai crude oil futures contract covering a long period, we find that, in the long run, the Shanghai crude oil futures price is greatly affected by international crude oil markets. In the short run, both international and China’s crude oil spot markets have a significant influence on the Shanghai crude oil futures prices. Moreover, our dynamic simulation results show that there is a significant time lag for the Shanghai crude oil futures market to respond to the shocks from the domestic and international markets.

A Study on the Oil Price Cointegration Dynamic Process: Evidence From the Shanghai Crude Oil Futures

This work studies the integration process of the Shanghai crude oil futures market in pricing discovery mechanism of global crude oil markets by conducting cointegration analysis and lead–lag causality tests. Using the representative samples of several futures contracts covering different listing periods, we conclude a significant and gradual change of the relations between the Shanghai crude oil futures market and international benchmarks, from unidirectional Granger causality to bidirectional Granger causality. The cointegration relationships become stable after about 2 years’ market development. Moreover, the Shanghai crude oil futures market always leads domestic (Daqing) crude oil spot market since 2019. Our evidences support that it has the increasing influence on domestic crude oil market and international benchmarks.

Crude oil price and exchange rate: Evidence from the period before and after the launch of China's crude oil futures

This study investigates whether the relationships between China's exchange rate, domestic crude oil price, and the international crude oil price have a switch in the period before and after China's crude oil futures launched by Shanghai International Energy Exchange (INE) using the MS-VAR model. We find that, although China's oil price is strongly influenced by the international crude oil market, its effect on the international crude oil price is weak; Since the launch of INE crude oil futures in the new regime, the fluctuations in the US dollar against the RMB (USD/CNY) exchange rate has had a significant positive effect on China's crude oil prices. Placebo test results demonstrate that the launch of Brent or Oman crude oil futures, which are US dollar-dominated, do not have the same effects as the launch of INE crude oil futures. This implies that the positive impact of the USD/CNY exchange rate on the INE crude oil futures price may be transmitted to China's crude oil spot market.

Time varying market efficiency in the Brent and WTI crude market

This paper examines time-varying market efficiency in the crude oil spot market using a recently derived measure of market efficiency: the Adjusted Market Inefficiency Model (AMIM). Analysing efficiency in the crude oil market, and its response to significant events within the global financial and commodity market, we identify that the Brent market is on average more efficient than the West Texas Intermediate (WTI). We also find that the WTI market is persistently inefficient during financial crises, with high volatility of the efficiency in such periods. In addition to confirming the adaptive market hypothesis, this study offers a new perspective by highlighting the non-uniform response of efficiency in similar markets to global events.

Crude Oil Volatility Transmission Across Food Commodity Markets: A Multivariate BEKK-GARCH Approach

This study examines the time-varying price risk transmission in the nexus between crude oil and agricultural commodity prices in the context of non-grain-based biofuel producing country. Analysis of the short- and long-run dynamics of volatility in both spot and futures markets of maize, soybean and wheat and crude oil prices using the multivariate BEKK-GARCH model, indicate volatility spillover from wheat futures to crude oil futures in the short run and from crude oil futures to futures markets of maize, soybean and wheat in the long run. The spot market linkage of selected commodities is weaker compared to futures market, wherein maize spot volatility transmits to crude oil spot market in the longer period and no spillover between crude oil-food spot market is observed in the short run. The hedge ratios indicate that a dynamic hedging strategy is crucial for efficient risk management and the portfolio weights in futures market are more than the spot market. The results reveal that cross-market volatility spillover is more evident in the futures market, while own past conditional volatility is more significant in spot price discovery and risk transmission is evident among food commodities futures markets. JEL Codes: G13, G14, Q11, Q18, Q02

The asymmetric spillover effect of the Markov switching mechanism from the futures market to the spot market

This article develops a multichain Markov switching dynamic conditional correlation ARCH model with idiosyncratic jump dynamics to investigate whether the state of the crude oil futures market can asymmetrically affect the state of the crude oil spot market. The asymmetric spillover effects are investigated after controlling the dependence structure on idiosyncratic jumps. The empirical findings show an asymmetric spillover effect from the futures market to the spot market. Moreover, the transition probabilities depend highly on the volatility of the futures market, showing the leading role of the futures market. The jump components play a relatively more important role in explaining the conditional variances than do the ARCH and regime-switching effects. Finally, both the contribution of idiosyncratic jumps on total variance and the correlation coefficient between the futures and spot returns rely on the volatility state of the futures and spot returns. The second and fourth moments of the conditional correlation coefficients will be underestimated when the common jumps and/or independent transition mechanisms are ignored. The findings of this paper have important implications for investors in accurately evaluating riskiness, hedgers in improving hedging performance, as well as market participants and government authorities in understanding the lead-lag relationship between crude oil spot and futures markets.

Effect of Crude Oil Futures Trading on Spot Market Volatility: A Panel Data–Based Counterfactual Prediction Analysis

ABSTRACTBased on panel data, a recently developed method of counterfactual prediction analysis is used in this article to analyze how the launch of Tokyo and Dubai crude oil futures influences the price volatility in the spot market whose underlying instruments are corresponding futures. Analysis results show that the launch of crude oil futures can speed up information integration into market system and reduce the volatility of the crude oil spot market, although the crude oil futures market is characterized mainly by speculative factors. The offshore underlying instrument does not have substantial influences on future contracts, while the scale of the futures market has a significant effect on the spot market volatility.

Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds

Given the emerging consensus from previous studies that crude oil and refined product (as well as crack spread) prices are cointegrated, this study examines the link between the crude oil spot and crack spread derivatives markets. Specifically, the usefulness of the two crack spread derivatives products (namely, crack spread futures and the ETF crack spread) for modeling and forecasting daily OPEC crude oil spot prices is evaluated. Based on the results of a structural break test, the sample is divided into pre-crisis, crisis, and post-crisis periods. We find a unidirectional relationship from the two crack spread derivatives markets to the crude oil spot market during the post-crisis period. In terms of forecasting performance, the forecasting models based on crack spread futures and the ETF crack spread outperform the Random Walk Model (RWM), both in-sample and out-of-sample. In addition, on average, the results suggest that information from the ETF crack spread market contributes more to the forecasting models than information from the crack spread futures market.

A metric and topological analysis of determinism in the crude oil spot market

We test whether the spot price of crude oil is determined by stochastic rules or exhibits deterministic endogenous fluctuations. In our analysis, we employ both metric (correlation dimension and Lyapunov exponents) and topological (recurrence plots) diagnostic tools for chaotic dynamics. We find that the underlying system for crude oil spot prices (i) is of high dimensionality (no stabilization of the correlation dimension), (ii) does not exhibit sensitive dependence on initial conditions, and (iii) is not characterized by the recurrence property. Thus, the empirical evidence suggests that stochastic rather than deterministic rules are present in the system dynamics of the crude oil spot market. Recurrent plot analysis indicates that volatility clustering is an adequate, but not complete, explanation of the morphology of oil spot prices.

Estimating Long-Run Relationship among Crude Oil Spot Prices, U.S. Inventory, and OPEC Production—An Error Correction Model Approach -

ABSTRACT This study focuses on how fully-developed international crude oil markets react to production and inventory variances in both short-run and long-run. We present an Error Correction Model (ECM) with for five international crude oil spot market prices, US inventory and OPEC production level. Simulation results are consistent to the usual notions of the economic theory and regionalization issue, indicating that the power of competitive market dominates in international crude oil markets. It is found that crude oil price is more elastic with respect to the OPEC crude oil production in the short-run, and more elastic with respect to the U.S. crude oil inventory in the long-run.

Crude oil spot market pricing: Pearsonian analysis of crude oil spot market prices

Crude oil spot market pricing: Pearsonian analysis of crude oil spot market prices