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This paper examines the Efficient Market Hypothesis (EMH) in crude oil amid the "financialization of commodity markets" and the "fracking revolution". It applies the generalized spectral derivative test (Hong and Lee 2005) on both West Texas Intermediate and Brent spot and futures markets, alongside a stochastic dominance test (Linton et al., 2005) to investigate arbitrage opportunities across markets and benchmarks. The findings indicate that financialization has made each market more efficient but also created more arbitrage opportunities in spot‐futures markets at both benchmarks. The fracking revolution has fragmented oil markets but had little impact on EMH in individual markets or across markets. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the dynamic asymmetric impact of equity market uncertainty on the energy market. For that reason, we first apply the asymmetric Granger causality test to capture both positive and negative EMV and oil shocks. We find the existence of the asymmetric effect, displaying as negative EMV shocks are the primary driving factors of positive oil shocks, while positive EMV shocks can affect negative oil shocks. Further, on the basis of three rolling-window procedures, we extend the asymmetric causality test to detect the structural breakpoints and time-varying characteristics of the causal relationships running from EMV shocks to oil shocks. Results show that some extreme events may lead to a structural breakpoint in their relationships. In particular, the negative effects of EMV shocks on oil shocks seem to be more lasting than the positive ones. Our findings may provide a new perspective on asset allocation, energy portfolio construction, and risk management. • This paper investigates the asymmetric impact of EMV shocks on crude oil shocks. • We extend the asymmetric causality test based on three rolling-window procedures. • The results show a significant asymmetric impact of EMV on the crude oil market. • We find the structural breakpoints and time-varying characters of their causality. [ABSTRACT FROM AUTHOR]

Providing a scientific basis and method to ensure the smooth functioning of the Chinese crude oil market would be hugely significant to China's future economic development and security. To this end, attempts have been made to both internationalize the Shanghai crude oil market and minimize financial risks. This paper selects three crude oil markets (INE, WTI and Brent) and five Chinese financial markets (the futures, bond, fund, stock and foreign exchange markets) as the research objects. The Diebold and Yilmaz spillover index model and the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method are used to study the volatility spillover effect and the asymmetric cross-correlation between crude oil markets and financial markets. When the volatility spillover effect and the asymmetric relationship that exists between the financial markets are examined, the volatility spillover of the oil market to the financial market is found to be significantly higher than that of the financial market to the oil market. In particular, the spillover effect was even more significant from late-2019 to early-2020. Analysis demonstrates an asymmetric cross-correlation between crude oil markets and the abovementioned five Chinese financial markets. In particular, the impact of the Chinese crude oil market on the stock market is greatest, especially with respect to the Brent and WTI crude oil markets. Except for the bond market, when the INE and Brent markets are increasing, the risk exposure to financial markets is more significant. Among financial markets, INE-Bond market asymmetry is stronger than WTI-bond market asymmetry, but weaker than that of the Brent-Bond market when there are large fluctuations. [ABSTRACT FROM AUTHOR]

GARCH-type models have been widely used for forecasting crude oil price volatility, but often ignore the structural changes of time series, which may lead to spurious volatility persistence. Therefore, this paper focuses on the smooth and sharp structural changes in crude oil price volatility, i.e., smooth shift and regime switching, respectively, and investigates which structural change based GARCH models have better performance for forecasting crude oil price volatility. The empirical results indicate that, first, the flexible Fourier form (FFF) GARCH-type models considering smooth shift can accurately model structural changes and yield superior fitting and forecasting performance to traditional GARCH-type models. Second, the Markov regime switching (MRS) GARCH model incorporating regime switching exhibits superior fitting performance compared to the single-regime GARCH-type models, but it does not necessarily beat the counterparts for forecasting. Finally, the FFF-GARCH-type models outperform MRS-GARCH for forecasting crude oil price volatility and portfolio performance. [ABSTRACT FROM AUTHOR]

This paper systematically evaluates the influence of international crude oil risk on Chinese macro-financial risks, by quantifying risk spillover effects from international crude oil market to China's three major markets (stock, foreign exchange and commodity markets). Specifically, this paper initially calculates the risks of international crude oil market and China's three major markets by adopting a conditional autoregressive value at risk (CAViaR) model, then the spillover index is used to capture the risk spillovers from international crude oil market to China's major markets. The empirical results indicate that there exists significant heterogeneous risk spillover effects transmitted from international crude oil market to China's three major markets. To be more specific, the risk derived from international crude oil market is always a dominant driving force of risk in China's commodity market. However, the shocks of global oil risk do not affect much of the risks of China's stock and foreign exchange markets in general. In addition, our results further report that international oil risk has considerable effects on China's macro-financial risks during several specific periods, which can be attributed to several major events. Specifically, the risk spillovers originated from international crude oil market remarkably contribute to the risk of China's commodity market during the period of global financial crisis. International crude oil risk makes great contribution to the risks of Chinese stock and foreign exchange markets, when several global notable events occur as well as major financial reforms in China are implemented. The empirical results have significant implications for policy-makers and market participants. [ABSTRACT FROM AUTHOR]

This paper proposes a new compound model to investigate the dynamic linkage mechanism between the natural gas and crude oil markets from the multi-scale perspective. In the proposed model, two main steps are involved: multi-scale analysis and network research. Based on the bivariate empirical mode decomposition (BEMD) and Fine-to-Coarse algorithm, the multi-scale analysis ensures the relationship between price fluctuations can be studied under three different time-scales, which corresponding to market disequilibrium, significant events, and long term trend. By integrating the grey correlation degree and Coarse-Gaining algorithm, the network research reveals the dynamic spillover effects of the two markets at different time-scales. To capture the different linked characteristic in different periods, the sample data of Henry Hub and WTI spot prices from 1997 to 2017 is divided into three periods. We transfer the daily correlations between the price fluctuations with specific time-scale into the correlation patterns and establish the network based on their transmission relations. The crucial correlation patterns and the significant transmission relations are revealed by some network indicators. And the proposed approach in this paper can be applied to other field of research. • A new compound model is proposed based on multi-scale analysis and complex network. • The spillover effects of the two markets under three inner factors are studied. • The linkage mechanisms of the two markets at different time-scales are varying. • The important correlation patterns and transmission relations are revealed. [ABSTRACT FROM AUTHOR]

This paper investigated the impacts of global crude oil price shocks on China's precious metals market using the meliorated autoregressive conditional jump intensity (ARJI) model (Chan and Maheu, 2002) and the extended autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) (Gronwald, 2012), based on the comparative analysis of gold and platinum markets. The results showed that discrete jumps existed in the crude oil market. Considering the volatility clustering of oil returns, the effect of oil price expected shocks on precious metals market was negative (−0.7025,-1.3341), due to the profitability of capital; While that of unexpected shocks was positive (0.0211,0.0645), which can be explained by the adaptive expectation theory. In terms of discrete jumps, the impact of current jumps on precious metals market was significantly negative (−1.4810,-2.6775), according to the risk transmission theory, and the responses tended to be characterized by “overreactions”. In addition, some evidence also suggested permanent volatility effects in China's precious metals market. [ABSTRACT FROM AUTHOR]

This paper aims to examine the dynamic linkages among the gold market, US dollar and crude oil market. The analysis also delves more deeply into the effect of the global financial crisis on the short-term relationship. We use fractional cointegration to analyze the long-term memory feature of these volatility processes to investigate whether they are tied through a common long-term equilibrium. The DCC-MGARCH model is employed to investigate the time-varying long-term linkages among these markets. The Krystou–Labys non-linear asymmetric Granger causality method is used to examine the effect of the financial crisis. We find that (i) there is clearly a long-term dependence among these markets; (ii) the dynamic gold–oil relationship is always positive and the oil–dollar relationship is always negative; and (iii) after the crisis, we can observe evidence of a positive non-linear causal relationship from gold to US dollar and US dollar to crude oil, and a negative non-linear causal relationship from US dollar to gold. Investors who want to construct their optimal portfolios and policymakers who aim to make effective macroeconomic policies should take these findings into account. [ABSTRACT FROM AUTHOR]

Previous research on the crude oil market has focused on the constant degree of the market integration or diversification, ignoring the time-varying market integration and diversification during different typical stages of the global oil price volatility. This paper proposes a novel wavelet-based complex network method to investigate the evolution feature of the world crude oil market integration and diversification from the perspective of the interdependent structural relationship of global oil prices, so that two critical reference indexes, namely the reference decision-making cycle and the target regional market, will be proposed for decision makers to better adjust their strategies. The results show that the dominant evolution cycle of the market integration from the stable stage to the high shock stage is time-varying, featuring the weekly → weekly → short yearly → short quarterly → short monthly cycle, and the dominant evolution cycle of the market diversification is also time-varying, characterized by the short monthly → weekly → long yearly → weekly → weekly cycle. These findings provide a clearer reference decision-making cycle for decision makers to create a more efficient period-oriented strategy. Two larger stable homogeneous groups of regional oil markets and the dominant regional markets in the process of the market evolution are discovered, providing more details regarding the target monitoring regional market for the oil-related early warning strategy and hedging strategies. [ABSTRACT FROM AUTHOR]

The nonlinear components of predictors could be informative, while conventional predictive regression models only consider the linear components in the time-series prediction of crude oil price returns. Inspired by this, we propose a novel method to obtain useful information embedded in the predictors' nonlinearity. We incorporate not only the original predictors (i.e., the linear components) but also their quadratic terms (i.e., the nonlinear components) when generating diffusion indices. The diffusion indices with nonlinearity can improve the crude oil return predictability both in- and out-of-sample. We also consider alternative nonlinear measures, including exponential, absolute, higher-power, and interactive forms. The loading results with supervised learning suggest that squared-form variables are informative. Furthermore, the diffusion indices with nonlinearity are better equipped to explain the supply and demand of crude oil, which is the economic source of predictability. The predictability is also significant in a market-timing exercise and survives a series of robustness checks and extensions. • This paper introduces nonlinearity to forecast crude oil price returns. • We consider squared, exponential, absolute, higher-power, and interactive forms as nonlinearity. • The diffusion indices with nonlinearity improve predictability in- and out-of-sample. • The predictability finding is economically significant in a market-timing exercise. • We explore the predictive source of nonlinearity via loadings and oil fundamentals. [ABSTRACT FROM AUTHOR]

Using multifractal detrended cross-correlation analysis (MF-DCCA), this paper studies the cross-correlation behavior between crude oil market and five selected exchange rate markets. The dataset covers the period of January 1,1996–December 31,2014, and contains 4,633 observations for each of the series, including daily closing prices of crude oil, Australian Dollars, Canadian Dollars, Mexican Pesos, Russian Rubles, and South African Rand. Our empirical results obtained from cross-correlation statistic and cross-correlation coefficient have confirmed the existence of cross-correlations, and the MF-DCCA results have demonstrated a strong multifractality between cross-correlated crude oil market and exchange rate markets in both short term and long term. Using rolling window analysis, we have also found the persistent cross-correlations between the exchange rates and crude oil returns, and the cross-correlation scaling exponents exhibit volatility during some time periods due to its sensitivity to sudden events. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impacts of oil price changes on energy stocks in Chinese stock market from the multifractal perspective. The well-known multifractal detrended fluctuation analysis (MF-DFA) is applied to detect the multifractality. We find that both returns and volatilities of energy industry index display apparent multifractal behavior. Oil market activity is an important source of multifractality in energy stocks index in addition to long-range correlations and fat-tail distributions. [ABSTRACT FROM AUTHOR]

In this paper, we perform a comprehensive entropic segmentation analysis of crude oil future prices from 1983 to 2014 which used the Jensen–Shannon divergence as the statistical distance between segments, and analyze the results from original series S and series begin at 1986 (marked as S ∗ ) to find common segments which have same boundaries. Then we apply time irreversibility analysis of each segment to divide all segments into two groups according to their asymmetry degree. Based on the temporal distribution of the common segments and high asymmetry segments, we figure out that these two types of segments appear alternately and do not overlap basically in daily group, while the common portions are also high asymmetry segments in weekly group. In addition, the temporal distribution of the common segments is fairly close to the time of crises, wars or other events, because the hit from severe events to oil price makes these common segments quite different from their adjacent segments. The common segments can be confirmed in daily group series, or weekly group series due to the large divergence between common segments and their neighbors. While the identification of high asymmetry segments is helpful to know the segments which are not affected badly by the events and can recover to steady states automatically. Finally, we rearrange the segments by merging the connected common segments or high asymmetry segments into a segment, and conjoin the connected segments which are neither common nor high asymmetric. [ABSTRACT FROM AUTHOR]

Environmental change created worldwide interest in investing in renewable energy. Less reliance on fossil fuels would have a substantial influence on investors for alternative energy, especially renewable energy. The literature has concentrated on empirical studies of herding behaviour in finance, but not in renewable energy. This paper fills the gap by investigating herding in renewable energy, using daily closing prices in renewable and fossil fuel energy stock returns in the USA, Europe, and Asia, for March 24, 2000–May 29, 2020, which covers the Global Financial Crisis (GFC) (2007–2009), the coronavirus crises of SARS (2003). And the ongoing COVID-19 (2019–2020) pandemic. The paper shows that: (1) for low extreme oil returns, investors are more likely to display herding in the stock market; (2) for SARS and COVID-19, herding is more likely during extremely high oil returns after the GFC; and (3) herding is more likely during periods of extremely low oil returns during the coronavirus crises. These results suggest that after the GFC, investors are more sensitive to asset losses, so they will be more likely to display herding in the stock market. However, during SARS and COVID-19, investors panic so they may unwisely sell their assets. There are strong cross-sector herding spillover effects from US fossil fuel energy to renewable energy, especially before the GFC, while the US fossil fuel energy market has a significant influence on the Europe and Asia renewable energy returns during COVID-19. During SARS, which was not a pandemic, US fossil fuels only had an impact on US renewable energy returns. • Environmental change has led to investing in renewable energy as an alternative to fossil fuels. • The literature has concentrated on herding behaviour in finance, but not in renewable energy, so this paper investigates herding in renewable energy for the USA, Europe, and Asia, for the period March 24, 2000–May 29, 2020 that covers the GFC, SARS, and COVID-19. • For (1) low extreme oil returns, herding is more likely in the stock market; (2) SARS and COVID-19, herding is more likely during extremely high oil returns after the GFC; and (3) extremely low oil returns during SARS and COVID-19, herding is more likely. • After the GFC, investors are more likely to display herding in the stock market but, during SARS and COVID-19, investors may unwisely sell their assets. • There are strong cross-sector herding spillover effects from US fossil fuel energy to renewable energy, especially before the GFC. [ABSTRACT FROM AUTHOR]

In view of the importance and complexity of crude oil price, we investigate the dynamic forecasting of crude oil prices via Bayesian and Econophysics approaches. We propose information entropy to measure the predictability of crude oil prices and employ the rolling window approach to forecast the dynamic price of crude oil. In this article, Bayesian approach is applied to estimate the parameters, at the same time, the classic estimate approach is also adopted. Finally, we compare the forecasting results of the two methods. The results of this paper indicates that (1) both estimate approaches can effectively estimate the parameters of Heston model; (2) under a specific loss function, Bayesian approach has advantage over classical estimation approach when estimated sample size is small, while the estimate sample size increases, classical method slightly outperforms Bayesian method; (3) the predicted entropy of the estimated interval of 200 days under the Bayesian method are closest to the actual information entropy, while the estimated interval of 600 days under the maximum likelihood method is closest to the information entropy; (4) except for the estimation interval of 200 days, the predicted entropy under the maximum likelihood estimation is more representative of the predictability of the crude oil price. we also consider the more general case, we test this two methods of different steps ahead and compare the stability of the two methods. • The Heston model is used to forecast the price and information entropy of crude oil market. • Classical and Bayesian estimation approaches are applying to estimate the parameters of the model. • The out-of-sample predicting with rolling time windows are adopted in this paper. • We use both high-frequency financial data and realized volatilities of financial markets. • We forecast the price of crude oil for 100 days ahead in different estimation intervals. [ABSTRACT FROM AUTHOR]

Abstract: This paper empirically investigates the functions of price discovery and risk transfer in crude oil and gasoline futures markets using some econometric models. And the results indicate that, first, 95.71% and 59.41% of the price discovery function is performed by futures in crude oil and gasoline markets respectively during the sample period, implying the greater contribution of the futures markets compared with that of the spot markets. Meanwhile, the gasoline futures price makes 85.71% contribution to price discovery in its interaction with crude oil futures price. Second, crude oil futures price performs the risk transfer function much better than gasoline futures price in interactions with their respective spot prices. And the risk transfer function between crude oil and gasoline futures markets is not well performed. Finally, the recent financial crisis has not significantly influenced the price discovery and risk transfer functions between crude oil and gasoline futures markets. [Copyright &y& Elsevier]

Abstract: In recent months, the crude oil market has shown an abnormal behavior as compared with the previous 20 years. The most relevant feature of it being the high-level prices, well beyond the most pessimistic forecasting of a couple of years ago. The aim of this paper is to show that the crude oil price dynamics in the period from mid-2003 to late-2004 can be described by a piecewise linear increasing trend plus an oscillatory behavior with power-law periodicity. In about mid-December 2003, a transition in the period of the price oscillations was detected. Before and after such a date, the oscillation period decreased and increased with time, respectively. Additionally, after July, 2004, the slope of the trend has suffered a 200% increment, which may indicate that the market instability is becoming stronger. [Copyright &y& Elsevier]

In this paper, we combine the DCC-MIDAS model with asymmetry effects with the DY spillover index model and study the asymmetric volatility spillover relationship between the international crude oil market and three major financial markets of China. Based on the high-frequency daily data from 2003 to 2019, we divide the volatility caused by positive return and negative return into good volatility and bad volatility, we use our methods to characterize volatility spillovers across crude oil market, stock market, bond market and gold market from the perspectives of long-term and short-term volatilities, as well as good and bad volatilities. The results show that there are asymmetric volatility spillover effects between the crude oil market and different financial markets in China. The long-term volatility spillover effects are significantly higher than the short-term volatility spillover effects of crude oil market, and the good volatility spillovers effects are greater than the bad volatility spillovers effects. China's financial markets are dominated by the bad volatility spillovers during financial disasters affected by the crude oil market, at the same time, the bad total volatility spillovers rise sharply and are periodically higher than the good volatility spillovers. In addition, gold under short-term conditions can effectively hedge the risks. • We combine the DCC-MIDAS model with asymmetry effects with the DY spillover index model. • China's financial markets are dominated by bad volatility spillovers during financial disasters. • The long-term volatility spillovers are great than the short-term volatility spillovers. • Gold under the short-term condition may hedge the risks. [ABSTRACT FROM AUTHOR]

The price of commodities are often difficult to forecast due to the underlying characteristics of the series such as autocorrelation, heteroskedasticity, and non-linearity. For many commodities, it is both socially and financially detrimental to have too much error in the forecasts. In the case of electricity production, both stock-outs and over production result in failure in the form of blackouts on one side and waste on the other. Therefore, it is desirable to improve the models that are used to forecast both the production and demand of electricity in order to optimize the match between supply and demand. To accomplish the aforementioned, understanding of the behavior of the commodity is necessary. In this paper, we apply a multi-fractal asymmetric detrended cross-correlation analysis to analyze the presence and asymmetry of the cross-correlations between the price of electricity in U.S. with respect to crude oil and natural gas markets. Our data draws from the major producers of electricity in the U.S. and evaluates the multi-fractal asymmetric detrended cross-correlation with respect to WTI and Natural Gas; both important inputs into the production of electricity. Our findings illustrate the fractal and cross-correlation relationship between electricity production and commodity prices. • Electricity prices differ in behavior across regions within the U.S. • Asymmetry in the cross-correlation exponents under different trends. • Persistence and anti-persistence varies for commodity and periodicity. • Cross-correlation relationship exists between electricity and commodity prices. • Fractal behavior exists between electricity and commodity prices. [ABSTRACT FROM AUTHOR]

This paper investigates the time-frequency connectedness across the global green bond market and several mainstream financial and energy markets in an attempt to figure out whether green bonds represent a different asset class. The connectedness methodology proposed by Baruník and Křehlík (2018) is employed for that purpose. This approach enables quantifying the dynamics of connectedness in terms of return and volatility over time and across time scales simultaneously. The empirical results indicate that connectedness between the global green bond market and the conventional financial and energy markets mainly occurs at shorter time horizons, suggesting that shocks are rapidly transmitted across markets with an effect lasting less than a week. A strong connectedness in return and volatility is found between green bonds and Treasury and investment-grade corporate bonds, principally because of the numerous characteristics they share. This finding implies that green fixed-income securities are not a different asset class, but they closely mirror the performance of government and high-quality corporate bonds. In contrast, there is a quite limited connectedness between the green bond market and the general stock market, the renewable energy equity sector and the crude oil market regardless of the time horizon considered. This evidence shows that green bonds appear as a valuable tool to fight against climate change without having to sacrifice part of the return generated by traditional assets, particularly ordinary bonds. Furthermore, it can have useful implications for investors and policy makers. [ABSTRACT FROM AUTHOR]

This paper investigates the co-movement and asymmetric interactions between energy and grain prices, based on the evidence from the crude oil and corn markets, the most important energy and grain markets, respectively. Time series analysis indicates that there is a consistent trend between the crude oil price and corn price with a significant positive correlation coefficient 0.7471 during the sampling period, from January 2008 to February 2016. In addition, we find that there is a long-run equilibrium relationship between the two commodities' prices. Moreover, while linear Granger causality tests suggest that there is a two-way Granger causality relationship between the price changes in the two markets, non-linear Granger causality tests suggest that there is only a one-way causality relationship from corn to oil price. However, both linear and non-linear Granger causality tests indicate the asymmetry of causality relationship between the two markets (the price change in corn market can more significantly Granger cause the change in crude oil market). Further analysis suggests that the contribution of the corn market to price discovery in a large commodity market is larger than that of the crude oil market. [ABSTRACT FROM AUTHOR]