Time-frequency effect of crude oil and exchange rates on stock markets in BRICS countries: Evidence from wavelet quantile regression analysis

This study examines the effects of oil prices and exchange rates on stock market returns in BRICS countries (Brazil, Russia, China, India and South Africa) from a time–frequency perspective over the period 2009–2020. We use wavelet decomposition series to develop a threshold rolling window quantile regression to detect time–frequency effects at various scales. The empirical results are as follows. First, our findings confirm that the effects of both crude oil prices and exchange rates on BRICS stock returns are asymmetric. Positive shocks of crude oil have a greater impact on a bull market, whereas negative shocks have a greater impact on a bear market. Second, there is a short-term enhancement effect of crude oil and exchange rate on BRICS stock markets. In addition, volatility in the macro financial environment also exacerbates the impacts of oil prices and exchange rates on the stock market, and these fluctuations are heterogeneous. Overall, these findings provide useful insights for international investors and policy makers.     

物流企业如何应对高油价

国际原油价格持续上涨,国内成品油价格也不断攀升,给国内的物流业带来了明显冲击。文章在分析油价上涨对物流成本、物流市场结构、物流竞争结构的影响的基础上,提出了面对高油价我们一方面应降低物流成本,缓解高油价带来的成本压力,另一方面可以通过提高运输价格、加强企业间的合作和发展绿色物流来提高国内物流企业竞争力,以应对高油价带来的影响。     

How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis

This paper studies the time–frequency, nonlinear quantile relationship between investor attention (GSVI) and crude oil over the period from January 2000 to April 2020. To do so, the wavelet coherency, wavelet-based causality-in-quantiles test and quantile-on-quantile method are employed. The results indicate that first, the correlation between investor attention and crude oil is relatively high, and the highly correlated regions are concentrated from 8 to 16 months. In most cases, the GSVI is negatively correlated with the crude oil market. Additionally, under extreme market conditions, the explanatory ability is stronger than in the normal market, and it is greater in the low-frequency domain than in the high-frequency domain. Finally, investor attention has an apparent asymmetric impact on crude oil prices and returns at each scale, displaying a positive effect on the low quantiles of crude oil but a negative effect on the high quantiles across all quantiles of the GSVI. In the short term, when crude oil prices and returns are in a bear market, the larger volume of the GSVI has a greater impact on them. Moreover, the impact becomes greatest under extreme market conditions.     

Dynamic interaction of risk–return trade-offs between oil market and China’s stock market: An analysis from the risk preferences perspective

The risk–return trade-off refers to the compensation required by investors for bearing risks, which can be viewed as the risk preference of investors in a market. The current study investigates the dynamic interdependence of risk–return trade-offs between China’s stock market and the crude oil market from the perspective of risk preference of investors, which is designed to explore the transmission process of investors’ risk preference in both markets. Specifically, this study applies the time-varying parameter GARCH-M model, namely TVP-GARCH-M model, to characterize the time-dependent risk–return trade-offs (investors’ risk preferences) in the crude oil and China’s stock markets, then examines their relationship through Granger causality tests. Results show that a variation in risk preferences of the oil market investors can dramatically cause a variation in risk preferences of the Chinese stock market investors, while the risk preference of investors in the Chinese stock market does not lead to that in the crude oil market, which is in accordance with expectations. The dynamic effect of investors’ risk appetite in the crude oil market is further examined by the TVP-VAR model. The findings of this work suggest that there generally exists a positive impact of investors’ risk preference in the oil market and that the effect is time-varying to a greater degree during the short and medium term. Moreover, responses of the Chinese stock market investors’ risk preference were more significant during the 2008 financial crisis. Additionally, the empirical results remain robust when applying alternative crude oil prices and China’s stock prices.     

Time-frequency co-movements between oil prices and interest rates: Evidence from a wavelet-based approach

This paper investigates time–frequency co-movements between crude oil prices and interest rates. To test this relationship, the study applied a continuous wavelet and cross wavelet approaches to data from West Texas Intermediate (WTI) crude oil prices and interest rates in the United States (U.S.). Results from the sample period revealed significant relationships, in the intermediate term, between WTI crude oil prices and U.S. interest rates. Moreover, co-movements between oil price and interest rate variables were especially sensitive during abnormal political events and periods of financial ‘meltdown’. We further use Partial Wavelet Coherence (PWC) and Multiple Wavelet Coherence (MWC) methods to investigate the impacts of five major control variables namely GDP growth, unemployment, three-month Treasury bill, CPI index and industrial production index. The results show a powerful impact of control variables on oil-interest rates co-movements under different frequencies. Finally, we show evidence of co-integrating long run relationship between oil markets and control variables. These results have important implications for energy investors and policy makers.     

Dependence structures between Chinese stock markets and the international financial market: Evidence from a wavelet-based quantile regression approach

In this study, we investigate the dependence structures between six Chinese stock markets and the international financial market including possible safe haven assets and global economic factors under different market conditions and investment horizons. The research is conducted by combining a quantile regression approach with a wavelet decomposition analysis. Although we find little or insignificant dependence under short investment horizons, we detect the strong asymmetric dependence of oil prices and the US dollar index on the six Chinese stock markets in the medium and long terms. Moreover, not only is crude oil not a safe haven, it may damage Chinese stock markets as it increases over the long term, even in bull markets. Meanwhile, appreciation of the US dollar (depreciation of RMB) damages (boosts) Chinese stock markets during bull (bear) market conditions under long investment horizons. Moreover, we find that VIX (volatility index)-related derivatives may serve as good risk management tools under any market condition, while gold is a safe haven asset only during crisis periods.     

A study of the efficiency of the Chinese clean energy stock market and its correlation with the crude oil market based on an asymmetric multifractal scaling behavior analysis

We examine the multifractal scaling behavior and market efficiency of China’s clean energy stock indexes using an asymmetric multifractal detrended fluctuation analysis (A-MFDFA) and then investigate the tail correlation between this index and the crude oil market via an asymmetric multifractal detrended cross-correlation analysis (A-MFDCCA). First, we reveal that the overall, upward and downward trends of the clean energy stock indexes all have significant multifractal characteristics. The clean energy stock market is far from efficient regardless of whether the fluctuations are small or large. In addition, both upward and downward fluctuations exhibit considerable asymmetry. The significant gap between the downward and overall trends indicates that the downward trend following small-scale fluctuations implies weaker efficiency for investors. Furthermore,based on the sliding market deficiency measure (MDM),we find that the change in efficiency in the three trends significantly depends on the length of the window. In the short term, there is no significant efficiency difference among these three trends; however, in the long term, the asymmetry in the upward and downward trends has gradually increased,especially after December 2018. The results demonstrate that bear markets can offer considerably more opportunities for obtaining excess profits. Finally, we reveal that the cross-correlation between the trends of crude oil prices and low-carbon indexes exhibits significant multifractal characteristics. When the crude oil market is in a bull market or the low-carbon energy market is in a bear market, especially in a larger-scale fluctuation, investors should pay attention to the long-term influence of the counterparty market and carry out a hedging operation to avoid risks.     

Toward efficiency in the crude-oil market

The ‘official’ (OPEC) prices of crude oil before the collapse in the oil market in the mid-1980s can be interpreted as contract prices and analysed on the basis of the theory of futures (or forward) markets. This paper uses the generalized method of moments estimation technique to test for efficiency in the relationship between the official prices and the ex-post spot prices at the time of delivery. Efficiency is rejected for the sample period 1978–1985 as a whole, but evidence is found of improvements over time. Further, the GMM Wald and Hansen tests, although asymptotically equivalent, are shown to differ greatly when applied to a small sample of monthly oil price data.     

原油价格支配因素分析

当今,原油价格在世界经济衰退的形势下持续高位运行,2010年美国进口原油平均价格达到74美元。持续增长的中国经济对进口原油的依赖程度将会继续加大。要保障中国的能源安全,以较低的能源成本为中国经济提供动力,就需要对原油价格决定因素进行分析。本文从原油的历史出发,分析原油价格在各个阶段的决定因素,根据当前世界原油价格的决定因素,有针对性地提出建议,为国家石油战略提供参考。     

The asymmetric effect of crude oil prices on stock prices in major international financial markets

The aim of this paper is to explore the potential asymmetric impacts of positive and negative shocks in crude oil prices on stock prices in six major international financial markets which include China, Hong Kong, America, Japan, Britain, and Germany. We test for these asymmetric effects on 8 major international financial markets indices over the 2007M01–2020M03 periods. Our independent measures include the prices of Brent crude oil futures and West Texas Intermediate (WTI) futures. We use the nonlinear ARDL (NARDL) model proposed by Shin et al. (2014), which can capture both short- and long-run nonlinearities through positive and negative partial sum decompositions of the explanatory variables. This research finds that positive and negative fluctuations of oil price have asymmetric effects on stock price index in four financial markets, but the performance of the asymmetry is different. Specifically, the impacts of volatility in oil prices on two indices of Chinese stock prices are different, and the asymmetric effects of oil price volatility on stock price indices in China and other financial markets are significantly different.     

Forecasting oil futures market volatility in a financialized world: Why speculative activities matter

We analyze the relation between volatility and speculative activities in the crude oil futures market and provide short-term forecasts accordingly. By incorporating trading volume and opening interest (speculative ratio) into the volatility dynamics, we document the subtle interaction between the two measures of which the volatility-averse behavior of speculative activities plays a considerable role in the market. Moreover, by accounting for structural changes, we find significant evidence that this behavior currently becomes weaker than in the past, which implies the oil futures market is less informative and/or less risk-averse in recent time period. Our forecasts based on these features perform very well under the predictive preferences that are consistent with the volatility-averse behavior in the oil futures market. We provide discussions and policy inferences.     

Resource prices and planning horizons

This paper shows that a seemingly simple assumption – that agents use a rolling planning horizon – can reconcile the puzzling long run price dynamics of exhaustible resources such as oil, gas and metals. A rolling horizon has the effect of removing the scarcity consideration of resource owners when stocks are large. Hence, extraction will be non-decreasing and resource prices non-increasing for a long period of time and there will be no connection between the price growth and the interest rate – in line with the trends of a majority of exhaustible resources in the last century. A calibration of the model to the oil market yields a price which closely fits the gradually falling real oil price after WWII and the sharply increasing price after 1998. This suggests that, while long run scarcity was not an important parameter on the oil market in the 20th century, it has been important in shaping the oil price from around 1998 and onwards.     

EMPIRICAL STUDIES OF GASOLINE RETAILING: A GUIDE TO THE LITERATURE

Abstract This paper surveys the empirical literature on gasoline retailing, which has been growing rapidly over the last three decades, possibly in response to antitrust and regulatory concerns and increased availability of pricing data. Studies of both pricing and non‐price decision variables are considered. In general, it is found that crude oil prices are the primary driver of national price movements over time. However, market structure has been identified as playing a role in price dynamics, equilibrium selection and price differentials across markets and stations. The economic literature emphasizes the importance of heterogeneity across stations and coordination problems faced by retailers. Several directions for future work are suggested, including the development of theory and demand estimation using high‐frequency station level data.     

Spillovers and directional predictability between international energy commodities and their implications for optimal portfolio and hedging

This study sheds a new light on the dependence and the directional predictability between eight major energy price returns, using the Cross-Quantilogram (CQ) and the Partial CQ (PCQ) analysis. The energy prices cover the time series for the U.S. natural gas and seven internationally traded crude oil types. The results reveal a significant directional predictability running from most of energy commodities returns to the OPEC basket and the very light Tapis crude oil returns. However, the quantile predictability in both directions is enabled only for the relations between the light Brent and the light WTI, and between the OPEC basket and the Malaysian Tapis. The time-varying predictability analysis reveals that there is a significant upper quantile dependence between these international energy commodities. Finally, we find that the TAPIS can be a good hedging vehicle for other energy markets. These findings may be instructive for both policymakers (in terms of financial stability) and market participants (in terms of performance).     

Retrieving the implicit risk neutral density of WTI options with a semi-nonparametric approach

This paper contributes to the literature on the estimation of the Risk Neutral Density (RND) function by proposing a log-semi-nonparametric (log-SNP) distribution as the implicit RND when the Gram-Charlier model is used for option pricing. The performance of the model is compared to the lognormal (Black Scholes) benchmark for a sample of option prices for West Texas Intermediate (WTI) crude oil that were traded in the period between January 2016 and December 2017. Results show that the lognormal specification tends to systematically undervalue option prices and that the proposed log-SNP distribution, which explicitly adjusts for negative skewness and excess kurtosis, results in markedly improved accuracy, especially in periods of market instability. As a result, the implied skewness and excess kurtosis are relevant sources of information on market expectations that should be used for hedging and risk management purposes.     

Long‐run relations in European electricity prices

This paper analyses the interdependencies existing in wholesale electricity prices in six major European countries. The results of a robust multivariate long‐run dynamic analysis reveal the presence of four highly integrated central European markets (France, Germany, the Netherlands and Austria). The trend shared by these four electricity markets appears to be common also to gas prices, but not to oil prices. The existence of a common long‐term dynamics among electricity prices and between electricity prices and gas prices can be explained by the similarity of the market design across Europe and by the same marginal generation technology. Since standard unit root and cointegration tests are not robust to the peculiar characteristics of electricity prices time series, we also develop a battery of robust inference procedures that should assure the reliability of our results. Copyright © 2009 John Wiley & Sons, Ltd.     

Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.     

Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach

This study examines the asymmetric multifractality and the market efficiency of the stock markets in the countries that are the top crude oil producers (USA, KSA, Canada and Russia) and consumers (Brazil, China, India, and Japan) using an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method. The results show evidence of an asymmetric multifractal nature for all markets. Moreover, the multifractality is stronger in the upward movement of the market returns, except in China. The degree of efficiency of the stock markets is shown to be time-varying and experienced a decrease during the 2008 global financial crisis (GFC), but an upside trend occurred during the recent oil price crash followed a significant decline during COVID-19. The stock markets have an anti-persistent feature during GFC and COVID-19, whereas they exhibit a long-term persistent feature during oil price crash. More interestingly, the efficiency of the stock markets of crude oil producers is lower in general than that of oil consumers. Furthermore, the efficiency of the stock market is lower in the downward movement of the market returns than in the upward movement. Asymmetry and oil price uncertainty index are the key driver of the stock markets and can serve as predictor of the stock market dynamics of top oil producers and top oil consumers particularly during COVID-19 and oil price crash.     

Crude oil price forecasting based on internet concern using an extreme learning machine

The growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil’s futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market.     

Investor sentiment and energy futures predictability: Evidence from Feasible Quasi Generalized Least Squares

Contributing to the budding literature on how emotional and sentimental actions impact the performance of financial markets, this study examines the predictability of energy futures prices with investors’ sentiments. In particular, we examine which of the three (neutral, bear and bull) investors’ sentiments offer accurate forecast information on four energy futures prices. Using the predictability test proposed by Westerlund and Narayan (2015), we discover that all the forms of investors’ sentiments are significant predictors of the movements in energy futures prices. However, the bear sentiments outshine other variants in the forecast of crude oil futures prices, while the bull sentiments provide the most accurate forecast information for the remaining energy futures prices, namely heating oil, gasoline and natural gas. We also find this evidence consistent even when asymmetries are considered in the predictability models. Among other implications of these findings, investors in energy futures and portfolio managers are expected to consider often emotional perceptions in their portfolio constructions and the predictability of future gains.