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This paper proposes a dynamic factor model to accurately characterize the dynamic dependence and risk spillovers between the crude oil and exchange rate returns of oil-trading countries from 2000–2020, conditional on the common factors. To this end, we first identify the common factors related to returns of crude oil prices and exchange rates from 14 typical oil-trading countries. Then, we use the AR-GARCH model to filter the respective idiosyncratic factors and conduct a comparative study of conditional dynamic dependence between the crude oil and exchange rate returns of oil importers and exporters. Finally, we combine the dynamic factor copula model with the CoVaR method to measure the conditional risk spillover effect between crude oil and exchange rate markets. The empirical study indicates that the classical factor analysis can be used to precisely identify the common factors related to both financial markets, with the similar trend of macro-economic indicators. Furthermore, the factor copula model can capture the dynamic structure between crude oil and exchange rate markets more accurately than the traditional Copula–GARCH model. Specifically, the idiosyncratic factors related to each return series still have a significant impact on the dependence between the crude oil and exchange rate returns of oil importers, while the common factors have played an important role in the relationship of oil exporters' exchange rates with crude oil prices. Finally, the crude oil market has enjoyed a relative risk premium to the exchange rate markets of oil-trading countries. However, there is almost no conditional risk spillover from the corresponding exchange rates to the crude oil prices. Finally, we discuss the implications for investors, policymakers and respective exchange rate regulators from oil-trading countries with further insights from the macro-economic perspective. [ABSTRACT FROM AUTHOR]

To obtain the price return and price volatility spillovers between renewable energy stocks, technology stocks, oil futures and carbon allowances under different investment horizons, this paper employs a frequency-dependent method to study the dynamic connectedness between these assets in four frequency bands. The results show that, first, there is a strong spillover effect between these assets from a system-wide perspective, and it's mainly driven by short-term spillovers. Second, in the time domain, technology stocks have a more significant impact on renewable energy stocks compared to crude oil. However, through the study in the frequency domain, we find renewable energy stocks exhibit a more complex relationship with the other two assets at different time scales. Third, renewable energy stocks have significant spillover effect on carbon prices only in the short term. On longer time scales, other factors such as energy prices, climate and policy may have a greater impact on carbon allowance prices. Fourth, the spillover effect of the system is time-varying and frequency-varying. During the European debt crisis, the international oil price decline and the COVID-19 pandemic, the total spillover index of the system has experienced a substantial increase, mainly driven by medium, medium to long or long term spillovers. [ABSTRACT FROM AUTHOR]

Crude oil and natural gas are crucial to the Russian economy. Therefore, this study examined the interconnections between crude oil price, natural gas price, and Russian economic policy uncertainty (EPU) over the period 1994–2019 using multivariate DCC-MGARCH models. The findings show that there are strong interconnections (co-movement) between the energy prices and EPU in Russia, and that it might be misleading to assume independence or neutrality between the variables. Although Russia is also a crucial player in both the natural gas and the crude oil markets, this study reveals that there is a stronger co-movement of the EPU with gas price than with the oil price. Russia is the largest exporter of natural gas and the second-largest producer; it is plausible that the natural gas price correlates with EPU more than the crude oil price. Further, the correlation between gas price and EPU and the correlation between crude oil price and EPU have similar patterns. Each declines almost in the same period and, equally, increases concurrently. In addition, the results revealed that significant global shocks and crises, such as the 2008 global financial crisis, the 2014–2017 Russian financial crisis, the 9/11 terrorist attack, and the Russo–Ukrainian conflicts, influence the interconnections between the energy prices and Russian EPU. [ABSTRACT FROM AUTHOR]

There has always been a complex relationship between uncertainty and crude oil prices. Three types of uncertainty, i.e., economic policy uncertainty, geopolitical risk uncertainty, and climate policy uncertainty (EPU, GPR, and CPU for short), have exacerbated abnormal fluctuations in the energy market, making crude oil prices volatile more and more frequently, especially from the perspective of the financial attribute of crude oil. Based on the time-series data related to uncertainties and crude oil prices from December 2001 to March 2021, this paper uses the quantile-on-quantile regression (QQR) method to explore the overall impact of various uncertainties on crude oil prices. Moreover, this paper adopts the QQR method based on the wavelet transform to investigate the heterogeneous effects of various uncertainties on crude oil prices at different time scales. The following conclusions are obtained. First, there are significant differences in the overall impact of the three types of uncertainties on crude oil prices, and this heterogeneity is reflected in quantiles of the peak impact intensity, the impact direction, and the fluctuation change. Second, the impact intensities of the three types of uncertainties on crude oil prices are significantly different at different time scales. This is mainly reflected in the different periods of significant impact of the three uncertainties on crude oil prices. Third, the impact directions and fluctuations of the three types of uncertainties on crude oil prices are heterogeneous at different time scales. [ABSTRACT FROM AUTHOR]

Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD), adaptive particle swarm optimization (APSO), and relevance vector machine (RVM)--namely, EEMD-APSO-RVM--to predict crude oil price based on the "decomposition and ensemble" framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs) and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI) to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE), mean absolute percent error (MAPE), and directional statistic (Dstat), showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price. [ABSTRACT FROM AUTHOR]

This paper aims to explore the dynamic relationships between the crude oil price (shocks) and investor sentiment. Specifically, this paper utilizes web crawler to construct Chinese investor sentiment index. The structural vector autoregression (SVAR) model is then used to decompose the crude oil price shocks into three types of oil price shocks. Finally, the wavelet coherence analysis (WTC) is employed to study the dynamic correlation between crude oil price (shocks) and investor sentiment in the time and frequency domain, and their asymmetric dynamic correlation under different trends of crude oil price. Using data from February 2013 to June 2021, our empirical results suggest the heterogeneous dynamic correlations and lead-lag relationships exist between crude oil price (shocks) and investor sentiment over different time and frequency domains. In addition, there are asymmetric dynamic correlations and lead–lag relationships between crude oil price (shocks) and investor sentiment under different trends of crude oil price. [ABSTRACT FROM AUTHOR]

In 2020, China proposed the goal of achieving carbon emission peaks by 2030 and carbon neutrality by 2060. For China, whose energy consumption structure has long been dominated by fossil energy, carbon trading and new energy are crucial for the realization of the emission target. By establishing a connectedness network model, this paper studies the static and dynamic spillovers between the Hubei carbon trading market, new energy stock market, crude oil market, coal market, and natural gas market in China, and draws the following conclusions: (1) the static spillover index of the carbon–energy–stock system is 3.57% and the dynamic spillover index fluctuates between 7.67% and 22.62%, indicating that the spillover effect of the system is low; (2) for the whole system, whether from a static or dynamic perspective, the carbon market always plays the role of net information receiver, while new energy is the net information transmitter; (3) the new energy stock market and the coal market always act as net information transmitters to the carbon market; and (4) the spillover effect of the system is asymmetric, wherein the system is more sensitive to negative information about price returns, and this asymmetry is much greater when the system is active. [ABSTRACT FROM AUTHOR]

China is a global leader in methanol production volume, while coal is a major feedstock. The country also has the world's largest commercial coal-to-methanol operations. Coal-based methanol is used widely within China and is a competitive substitute for gasoline. Owing to this, it is plausible that the price of coal may be linked to international crude oil prices, with methanol prices serving as the connecting channel. We add supporting evidence to a recently emerging area of literature and observe statistically significant relationships among the three prices, and, therefore, the influence from international crude oil and methanol prices on the coal price determination in China. This paper investigates the relationships among these prices for the period from January 2010 to December 2019 through spectral Granger causality analysis, alongside more traditional cointegration tests to develop a comprehensive picture of causal association between the price series in both the frequency and time domain. Cointegration is found in our tri-variate system while the frequency domain Granger causality tests reveal the long-run causality in all directions except from crude oil to methanol, thus, emphasizing the structure of coal price dependence. According to the generalized impulse response functions, the coal price reacts positively to shocks in crude oil prices. [ABSTRACT FROM AUTHOR]

With the continuous increase of China's foreign-trade dependence on crude oil and the accelerating integration of the international crude oil market and the Chinese finance market, the spillover effect of international oil price fluctuation on China's stock markets increasingly attracts the attention of the public. In order to explore the impact of international oil price fluctuation on China's stock markets and the time-varying spillover differences of industry sectors, this study proposes three research hypotheses and constructs a multi-time scale analysis framework based on wavelet analysis and a time-varying t-Copula model. In this paper, we use the Shanghai Composite Index as the representative of a general trend of the stock market, and we use the stock index of the China Securities Industry as the counterpart of industrial sectors. Based on the data from 5 January 2005 to 31 May 2020, this paper measures and analyzes the spillover effect of international oil price fluctuation on China's stock markets, under different volatility periods. The results show that, firstly, the spillover effect of international oil price fluctuation on the Chinese stock markets is different. In the short and medium volatility period, the changes in international oil price are ahead of the changes in the Chinese stock markets, while the latter is ahead of the former under long-term fluctuations. Secondly, the spillover effect of international oil price fluctuation on China's industry stock indexes is persistent. As the time scale increases, the tail dependency will increase. Finally, the impact of risk events aggravates the volatility of the stock markets in the short-term, while the mid- to long-term impact mainly affects the volatility trend. Investment risk control can make overall arrangement on the basis of the characteristics of oil price impact under different fluctuation stages. [ABSTRACT FROM AUTHOR]