OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach

This paper provides a novel perspective to the predictive ability of OPEC meeting dates and production announcements for (Brent Crude and West Texas Intermediate) oil futures market returns and GARCH-based volatility using a nonparametric quantile-based methodology. We show a nonlinear relationship between oil futures returns and OPEC-based predictors; hence, linear Granger causality tests are misspecified and the linear model results of non-predictability are unreliable. When the quantile-causality test is implemented, we observe that the impact of OPEC variables is restricted to Brent Crude futures only (with no effect observed for the WTI market). Specifically, OPEC production announcements, and meeting dates predict only lower quantiles of the conditional distribution of Brent futures market returns. While, predictability of volatility covers the majority of the quantile distribution, barring extreme ends.     

Time-frequency causality and dependence structure between crude oil,EPU and Chinese industry stock: Evidence from multiscale quantile perspectives

This article investigates the time-frequency causality and dependence structure of Chinese industry stock returns on crude oil shocks and China's economic policy uncertainty (EPU) across quantiles over the period from January 2001 to June 2021. We use wavelet-based decomposition series to establish a multiscale causality-in-quantiles test and a quantile-on-quantile regression approach to reveal the complicated relationships involving crude oil, EPU and stock returns. Our empirical results are as follows: First, the predictability of crude oil and EPU on industry stock returns is significantly strong under extreme market conditions. Second, the explanatory ability of EPU on industry stock returns in the long term is stronger than EPU’s ability to explain short term returns. Third, the impacts of crude oil and EPU on industry stock returns remain remarkably asymmetric across quantile levels. Finally, nonenergy-intensive industries are also affected by crude oil shocks, but less than energy-intensive industries. Overall, these empirical findings can provide implications for policymakers to stabilize stock markets and investors to hedge the potential risks from crude oil and EPU.     

Co-movement of oil and stock prices in the GCC region: A wavelet analysis

This paper examines the short term and long term dependencies between stock market returns and OPEC basket oil returns for the six Gulf Cooperation Council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) and two non-oil producing countries in the region (Egypt and Jordan), over the period 2002–2011. We utilize the wavelet coherency methodology in our empirical analyses. The empirical evidence indicates lack of market dependencies in the short term in these countries, indicating that oil and stock returns are not strongly linked in this interval. However, we show that oil returns and the stock markets returns co-move over the long term. The results also suggest that the long term dependencies are much stronger for OPEC oil returns and Jordan stock market returns relative to OPEC oil returns and Egypt stock market returns, implying a variation in the dependencies between oil prices and stock markets across countries. We further note an increasing strength in the market dependencies after 2007, signifying enhanced diversification benefit for investors in the short term relative to the long term.     

Model‐free evaluation of directional predictability in foreign exchange markets

We examine directional predictability in foreign exchange markets using a model‐free statistical evaluation procedure. Based on a sample of foreign exchange spot rates and futures prices in six major currencies, we document strong evidence that the directions of foreign exchange returns are predictable not only by the past history of foreign exchange returns, but also the past history of interest rate differentials, suggesting that the latter can be a useful predictor of the directions of future foreign exchange rates. This evidence becomes stronger when the direction of larger changes is considered. We further document that despite the weak conditional mean dynamics of foreign exchange returns, directional predictability can be explained by strong dependence derived from higher‐order conditional moments such as the volatility, skewness and kurtosis of past foreign exchange returns. Moreover, the conditional mean dynamics of interest rate differentials contributes significantly to directional predictability. We also examine the co‐movements between two foreign exchange rates, particularly the co‐movements of joint large changes. There exists strong evidence that the directions of joint changes are predictable using past foreign exchange returns and interest rate differentials. Furthermore, both individual currency returns and interest rate differentials are also useful in predicting the directions of joint changes. Several sources can explain this directional predictability of joint changes, including the level and volatility of underlying currency returns. Copyright © 2007 John Wiley & Sons, Ltd.     

A quantile-copula approach to dependence between financial assets

This article unveils the dependence structure between United States stock prices, crude oil prices, exchange rates, and U.S. interest rates. In particular, we employ linear and nonlinear estimation methods, such as quantile regression and the quantile-copula approach. Over the 1998–2017 period, we find that there is a positive relationship between the dollar value and the S&P 500 stock price, with the exception of the lower and upper tails of the stock return distribution. Further evidence is obtained on the dependence structure between other asset returns. The stock returns are negatively related to oil prices but positively to U.S. interest rates. Our results highlight the way that financial assets are linked, which have implications for risk management and monetary policy.     

Heterogeneity dependence between oil prices and exchange rate: Evidence from a parametric test of Granger causality in quantiles

This paper re-examines the nexus between crude oil price and exchange rate by investigating their heterogeneity dependence structure within the framework of Granger causality in quantiles for a sample of developed and emerging economies (namely UK, Canada, Brazil, Russia, Mexico, Norway, India, Japan, South Africa, South Korea and European Union (EU)). The results indicate no distinct causality between the crude oil price changes and the real exchange rate returns for all countries besides Russia at the median of the conditional distribution. Besides, the crude oil price changes influence the exchange rate returns in all countries, except Norway and EU, particularly around the tails of the conditional distributions of exchange rate returns. This suggests that the oil price changes influence the real exchange rate returns when the real exchange rate returns are either in extreme appreciation or depreciation. Moreover, the crude oil price movement can be explained by the exchange rate returns for most oil importers only when the crude oil market is bearish or bullish. By contrast, the real exchange rate can permanently affect the crude oil price for most oil-importing countries irrespective of the crude oil market's state. Finally, our findings provide an essential reference for managing the extreme risk dependence between the exchange rate market and the crude oil market.     

A model of dynamic tail dependence between crude oil prices and exchange rates

We explore the tail dependence between crude oil prices and exchange rates via a dynamic quantile association regression model based on the flexible Fourier form. This method allows us to describe the quantile dependence between conditional distributions of assets. We first perform simulation exercises to gauge the estimation precision of our model. We then undertake empirical analyses to examine the dynamic relation between crude oil and nine exchange rates. We reveal a mildly symmetric tail dependence between these two assets but it increases sharply during the Great Recession of 2008. Further robustness check substantiates the baseline results.     

Crack spread option pricing with copulas

A copula-based approach for pricing crack spread options is described. Crack spread options are currently priced assuming joint normal distributions of returns and linear dependence. Statistical evidence indicates that these assumptions are at odds with the empirical data. Furthermore, the unique features of energy commodities, such as mean reversion and seasonality, are ignored in standard models. We develop two copula-based crack spread option models using a simulation approach that address these gaps. Our results indicate that the Gumbel copula and standard models (binomial, and Kirk and Aron (1995)) mis-price a crack spread option and that the Clayton model is more appropriate. We contribute to the energy derivatives literature by illustrating the application of copula models to the pricing of a heating oil–crude oil “crack” spread option.     

The quantilogram: With an application to evaluating directional predictability

We propose a new diagnostic tool for time series called the quantilogram. The tool can be used formally and we provide the inference tools to do this under general conditions, and it can also be used as a simple graphical device. We apply our method to measure directional predictability and to test the hypothesis that a given time series has no directional predictability. The test is based on comparing the correlogram of quantile hits to a pointwise confidence interval or on comparing the cumulated squared autocorrelations with the corresponding critical value. We provide the distribution theory needed to conduct inference, propose some model free upper bound critical values, and apply our methods to S&P500 stock index return data. The empirical results suggest some directional predictability in returns. The evidence is strongest in mid range quantiles like 5–10% and for daily data. The evidence for predictability at the median is of comparable strength to the evidence around the mean, and is strongest at the daily frequency.     

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.     

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.     

Searching hedging instruments against diverse global risks and uncertainties

We evaluate the influence of five major risk and uncertainty factors on four asset classes. Our time-varying findings suggest that each asset hedges only a particular uncertainty factor, whereas gold does more than one factor, especially during COVID-19. Our frequency-based quantile regression (QR) results show that in the raw frequency, gold and Islamic stock can better hedge various uncertainty factors than Bitcoin and crude oil, depending on the market conditions. Additionally, using the frequency bands (e.g., short, medium, and long term) data, we further notice that, depending on the market circumstances and investment horizons, gold and Islamic stock returns are still better hedges for the various risks and uncertainties than Bitcoin and crude oil returns. Our findings have crucial risk and portfolio management implications for investors, portfolio managers, and policymakers.     

Estimation of Asymmetric Stochastic Volatility Models: Application to Daily Average Prices of Energy Products

Energy supply and demand, and as a consequence energy prices, are likely to represent one of the biggest challenges of the 21st century. Commodity markets exhibit increased volatility when there is little or no underutilized supply capability to meet natural fluctuations in demand. In the case of energy markets, the large capital requirements and significant lead times associated with energy production and delivery make them more susceptible to the imbalances in supply capability and demand. Energy price volatility has destructive impact on market agents, and this impact is intensified when the prices exhibit asymmetric volatility. This article pursues two aspects of the issue. First we consider general aspects, especially the asymmetric pattern of volatility of daily returns of different types of energy products. Then, we analyze the behaviour of daily returns by using traditional models of volatility that include AGARCH, TGARCH, EGARCH, and ARSV strategies, as well as a threshold asymmetric autoregressive stochastic volatility (TA-ARSV) model that we propose. The energy products considered in this analysis are probably the most relevant energy products for the economic activity of the nations and the economic relations between countries: Crude Oil (OPEC reference basket and London Brent index), Gasoline, Natural Gas, Butane, and Propane. We use spot prices and the time reference ranges from 1986–1993 to 2009 depending on the product.     

Dynamic spillovers and linkages between gold,crude oil,S&P 500, and other economic and financial variables. Evidence from the USA

This paper focuses on the price determinants of gold, and on the challenges associated with gold’s safe haven property. Specifically, it analyses the interlinkages and the return spillover effect among gold, crude oil, S&P 500, dollar exchange rate, Consumer Price Index (CPI), economic policy uncertainty and Treasury bills, by employing a Vector Autoregression (VAR) and the spillover index of Diebold and Yilmaz (2012), Diebold and Yılmaz (2014). Monthly realized return series, covering the period from 2nd of January 1986 to 31st of December 2019 are used to examine the short-run linkages, and the return spillovers rolling-window estimates in analyzing the transmission mechanism in a time-varying fashion, respectively. Our findings identify gold as a strong dollar hedge, while crude oil and Treasury bills appear to drive inflation; they also indicate strong spillover effects between exchange rate and gold returns. In general, co-movement dynamics display state-dependent characteristics. Both total and directional spillovers increase significantly during market turbulence caused by severe financial crises such as the Global Financial Crisis (GFC) of 2007–2009 and the European Sovereign Debt Crisis of 2010–2012. Net spillovers switch between positive and negative values for all these markets, implying that the recipient/transmitter position changes drastically with market events. Economic policy uncertainty, stock market returns, and crude oil price returns are the main transmitters, while Treasury bills and CPI are the main return shock recipients. Gold and exchange rate act both as receivers and transmitters over the sample period.     

On the predictability of the distribution of excess returns in currency markets

This paper investigates the predictability of foreign exchange (FX) volatility and liquidity risk factors on returns to the carry trade, an investment strategy that borrows in currencies with low interest rates and invests in currencies with high interest rates. Previous studies have suggested that this predictability could have been spuriously accounted for due to the persistence of the predictors. The analysis uses a predictive quantile regression model developed by Lee (2016) that allows for persistent predictors. We find that predictability changes remarkably across the entire distribution of currency excess returns. Predictability weakens substantially in the left tail once persistence is accounted for, implying a moderate negative predictive relation between FX volatility risk and carry trade returns. By contrast, it becomes stronger in the right tail. Furthermore, we provide evidence that FX volatility risk still dominates liquidity risk after controlling for persistence. These findings suggest that the persistence of the predictors needs to be taken into account when one measures predictability in currency markets. Finally, out-of-sample forecast performance is also presented.     

Relationship between oil,stock prices and exchange rates: A vine copula based GARCH method

In this paper, we apply a vine copula approach to investigate the dynamic relationship between energy, stock and currency markets. Dependence modeling using vine copulas offers a greater flexibility and permits the modeling of complex dependency patterns for high-dimensional distributions. Using a sample of more than 10 years of daily return observations of the WTI crude oil, the Dow Jones Industrial average stock index and the trade weighted US dollar index returns, we find evidence of a significant and symmetric relationship between these variables. Considering different sample periods show that the dynamic of the relationship between returns is not constant over time. Our results indicate also that the dependence structure is highly affected by the financial crisis and Great Recession, over 2007–2009. Finally, there is evidence to suggest that the application of the vine copula model improves the accuracy of VaR estimates, compared to traditional approaches.     

Dependent relationships between Chinese commodity markets and the international financial market: Evidence from quantile time-frequency analysis

In this article, the quantile time–frequency method is utilized to study the dependence of Chinese commodities on the international financial market. The impacts of risk management and diversification benefits of different portfolios are examined by calculating the reduction in downside risk. Moreover, we estimate and compare Sharpe Ratios (SRs) and Generalized Sharpe Ratios (GSRs) based on the frequencies of the investigated portfolios. Our empirical results reveal a strong asymmetric response from Chinese commodity markets. Specifically, we find that gold is a safe-haven asset, and due to negative correlations found at lower quantiles in medium and long term, an increase in the USD index damages bull commodity markets but boosts bear conditions under long-term investments, and negative (positive) tail correlations with interest rates (IRs) in bull (bear) markets are observed. It is proven that WTI can decrease short-run risks while USD and GOLD are more efficient in the diversification of downside risk. Adding international commodities may not improve the returns of Chinese commodities at given risk levels in the short and medium term through SRs and GSRs. In brief, investors should consider these dependence structures and modes of risk management in terms of time and frequency.     

The impact of oil on equity returns of Canadian and U.S. Railways and airlines

The purpose of this paper is to investigate the relationship between oil price movements and equity returns of railways and airline in Canada and the U.S. Using a robust set of oil measures, which includes both West Texas Intermediate (WTI) and Western Canadian Select (WCS) data, this research finds that railways and airlines react uniquely to oil price movements. Specifically, equity returns of railways in Canada and airlines in the U.S. tend to be negatively impacted by positive movements in WTI. Equity returns of airlines in Canada and railways in the U.S show limited evidence of any impact. Additional estimations suggest that equity returns of airlines react asymmetrically and that information regarding oil price movements may gradually diffuse over time. With the changing North American energy landscape (e.g., oil sands and shale oil), the increased reliance on transporting crude oil via railways should lead academics and practitioners to further research in this area.     

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.     

Time-frequency connectedness of crude oil,economic policy uncertainty and Chinese commodity markets: Evidence from rolling window analysis

This article investigates the time–frequency connectedness of economic policy uncertainty (EPU), WTI crude oil and Chinese commodity markets during the period between 2004 and 2020. Rolling window wavelet vector autoregression and connectedness networks are developed to evaluate the time-varying characteristics of the connectedness. The empirical results are as follows: First, the total connectedness between EPU, oil and commodities becomes stronger as the time scale increases. Second, the net connectedness of EPU and WTI in the system is positive, indicating that EPU and WTI are contributors to information and will affect financial markets across time scales. Third, the connectedness remains at a high level during financial crises across all scales, and the contribution of EPU and crude oil to commodities increases significantly. Specifically, compared with other commodity sectors, grains are greatly affected by EPU under the condition that the energy sector is seriously affected by crude oil. Overall, investors and policy makers should consider connectedness in terms of time and frequency when making a decision.