Impact of speculation and economic uncertainty on commodity markets

We examine the interactions between commodity futures returns and five driving factors (financial speculation, exchange rate, stock market dynamics, implied volatility for the US equity market, and economic policy uncertainty). Nonlinear causality tests are implemented after controlling for cointegration and conditional heteroscedasticity in the data over the period May 1990 – April 2014. Our results show strong evidence of unidirectional linear causality from commodity returns to excess speculation for the majority of the considered commodities, in particular for agriculture commodities. This evidence casts doubt on the claim that speculation is driving food prices. We also find unidirectional linear causality from energy futures markets to exchange rates and strong evidence of nonlinear causal dependence between commodity futures returns, on the one hand, and stock market returns and implied volatility, on the other hand. Overall, the new evidence found in this paper can be utilized for policy and investment decision-making.     

Nonlinear dynamic correlation between geopolitical risk and oil prices: A study based on high-frequency data

This study investigates the nonlinear dynamic correlations between geopolitical risk (GPR) and oil prices using nonlinear Granger causality and DCC-MVGARCH methods based on high-frequency data. The relationship between GPR and oil prices is found to have a complex nonlinear relationship rather than a simple linear one. Further, a bidirectional nonlinear Granger causality is found to consistently exist between GPR and oil volatility across different components of realized volatility. In terms of returns, GPR has relatively weak unidirectional nonlinear Granger causation with oil returns. The dynamic correlation analysis shows that GPR mainly affects oil volatility rather than returns. Moreover, GPR mainly affects oil volatility through the jump component of the oil market after the financial crisis, and there is a strong positive correlation between GPR and volatility jumps. Our findings innovatively suggest that GPR can potentially be utilized to improve models of volatility jumps and provide reference for investors and price analysts in oil markets who want to design sensible risk-management strategies.     

Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation

Linear and nonlinear Granger causality tests are used to examine the dynamic relation between daily Dow Jones stock returns and percentage changes in New York Stock Exchange trading volume. We find evidence of significant bidirectional nonlinear causality between returns and volume. We also examine whether the nonlinear causality from volume to returns can be explained by volume serving as a proxy for information flow in the stochastic process generating stock return variance as suggested by Clark's (1973) latent common-factor model. After controlling for volatility persistence in returns, we continue to find evidence of nonlinear causality from volume to returns.     

Is there a symmetric nonlinear causal relationship between large and small firms?

This paper uses both linear and nonlinear causality tests to reexamine the causal relationship between the returns on large and small firms. Consistent with previous results, we find that large firms linearly lead small firms. We also find a significant linear causality in the direction from small firms to large firms, particularly in the more recent time period where the impact from small firms to large firms is greater than from large to small. More important, in contrast to the received literature, we find significant nonlinear causality that is bi-directional and of the same duration in either direction. Using the BEKK asymmetric GARCH model we are able to capture most of the detected nonlinear relationship. This indicates that volatility spillovers are largely responsible for the observed nonlinear Granger causality.     

Oil and stock market activity when prices go up and down: the case of the oil and gas industry

We examine the asymmetric effects of daily oil price changes on equity returns, market betas, oil betas, return variances, and trading volumes for the US oil and gas industry. The responses of stock returns associated with negative changes in oil prices are higher than that associated with positive changes in oil prices. Stock risk measured by market beta is influenced more due to oil price decreases than due to oil price increases. On the other hand, oil risk exposures (oil betas) and return variances are more influenced by oil price increases than oil price decreases. The results of our study indicate that oil and gas firm returns, market betas, oil betas, return variances respond asymmetrically to oil price changes. We also find that relative changes in oil prices along with firm-specific factors such as firm size, ROA, leverage, market-to-book ratio (MBR) are important in determining the effects of oil price changes on oil and gas firms’ returns, risks, and trading volumes.     

International nonlinear causality between stock markets

In this paper, we test for linear and nonlinear Granger causality between the French, German, Japanese, UK and US daily stock index returns from 1973 to 2003. We find a strong contemporaneous linear dependence between European countries and a directional linear dependence from the US towards the other markets. Besides, linear causality increases after 1987, a finding consistent with the expected effects of financial liberalization of the 1980s and the 1990s. Above all, we document the presence of bidirectional nonlinear causality between daily returns. To check for spurious nonlinear causality, we filter out heteroskedasticity using a FIGARCH model. The dramatic decrease in the number of significant nonlinear causality lags confirms that heteroskedasticity played a major part in the previous findings. We then check if a few structural breaks can explain the remaining nonlinear causality. We find that a large number of nonlinear relationships vanish when we control for structural breaks, whereas linear causality remains.     

Oil Risk Exposure: The Case of the U.S. Oil and Gas Sector

We estimate oil price risk exposures of the U.S. oil and gas sector using the Fama‐French‐Carhart's four‐factor asset pricing model augmented with oil price and interest rate factors. Results show that the market, book‐to‐market, and size factors, as well as momentum characteristics of stocks and changes in oil prices are significant determinants of returns for the sector. Oil price risk exposures of U.S. oil and gas companies in the oil and gas sector are generally positive and significant. Our study also finds that oil price risk exposures vary considerably over time, and across firms and industry subsectors.     

Nonlinearity in the stock price–dividend relation

We provide empirical evidence of nonlinearities in the present value (PV) model of stock prices. We test for nonlinearity both in the contemporaneous and in the dynamic stock price–dividend relation for the UK, the US, Japan, and Germany. We employed three nonlinear nonparametric techniques, namely nonlinear cointegration, locally-weighted regression, and nonlinear Granger causality tests. Whilst there is no evidence of linear cointegration and Granger causality for any country, there is significant evidence of nonlinear cointegration and nonlinear Granger causality for all four countries. Furthermore, out-of-sample forecasts obtained from the locally-weighted regression are more accurate than out-of-sample forecasts obtained from the linear model for the UK, the US, and Japan. These results are robust to sub-period analysis. The results are in line with empirical evidence that expected stock returns are time-varying.     

Asymmetric effects of oil shocks on stock market returns in Saudi Arabia: evidence from industry level analysis

We examine the impact of oil price shocks on stock market returns in Saudi Arabia using the country-level as well as the industry-level stock market data. We find that the relation between changes in oil prices and equity returns is positive and significant at the country-level and at the industry level. Our results show that oil prices have asymmetric effects on equity returns for 4 out of 15 industrial sectors (e.g., hotel and tourism, insurance, multi-investment, and petrochemicals). These results have significant implications for investors, portfolio managers, policymakers, and corporate finance managers.     

Information content of volume: An investigation of Tokyo commodity futures markets

This study examines the relationship between volume and price changes for Tokyo commodity futures contracts by focusing on the predictive power of volume. The findings indicate a positive simultaneous relation between volume and absolute returns. The relation is not entirely contemporaneous since lagged volume contains predictive power for absolute returns. However, linear and nonlinear causality tests show that volume does not forecast returns. The results are qualitatively the same for contracts traded with different methods.     

Oil price shocks and the US stock market: A nonlinear approach

We study the response of US stock market returns to oil price shocks and to what extent it behaves asymmetrically over the different phases of the business cycle. For this purpose, we decompose the oil price changes into supply and demand shocks in the oil market and assess the state-dependent dynamics of structural shocks on US stock returns using a smooth transition vector autoregression model. When nonlinearity is considered, quantitatively very different asymmetric dynamics are observed. Our findings show that the responses of US stock returns to disaggregated shocks are asymmetric over the business cycle and that the impact of demand-driven shocks on US stock returns is stronger and more persistent, especially when economic activity is depressed. Furthermore, the contribution of shocks to expectation-driven precautionary demand in recessions accounts for a larger share of the variability of US stock market returns than that predicted by standard linear vector autoregressions.     

Does Bitcoin affect decomposed oil shocks differently? Evidence from a quantile-based framework

This paper studies the impact of Bitcoin on decomposed oil price shocks within a quantile-based framework, through which the underlying investment sheltering role of Bitcoin for various oil price fluctuations is explored. The aggregate oil price shock is decomposed into three perspectives of the demand, the supply, and the changing attitudes towards risk. A comparison of the sheltering role between Bitcoin and gold is further evaluated. By using a non-parametric causality test, we find that there exists an asymmetric and unidirectional causal relationship from Bitcoin/gold to oil shocks. Such the unidirectional causality appears only to the demand and supply shocks of oil instead of the risk-specific shocks, and is more evident at median quantiles. By jointly considering the data distribution of both dependent and independent variables realized by a quantiles-on-quantiles method, both Bitcoin and gold generally depict the hedge and safe haven abilities for oil shocks, and such the ability is shown to be different not only between Bitcoin and gold but also for various sources of oil shocks. The sheltering role of gold is found to be greater than that of Bitcoin for the supply shock, while the results reverse for the demand shock. Moreover, shocks from the identified shocks from the COVID-19 pandemic and the Russia–Ukraine conflict are found to not change the cross-market relationship. A series of robustness checks confirm our findings that possess important implications for various stakeholders.     

Stock return forecasting: Some new evidence

This paper makes three contributions to the literature on forecasting stock returns. First, unlike the extant literature on oil price and stock returns, we focus on out-of-sample forecasting of returns. We show that the ability of the oil price to forecast stock returns depends not only on the data frequency used but also on the estimator. Second, out-of-sample forecasting of returns is sector-dependent, suggesting that oil price is relatively more important for some sectors than others. Third, we examine the determinants of out-of-sample predictability for each sector using industry characteristics and find strong evidence that return predictability has links to certain industry characteristics, such as book-to-market ratio, dividend yield, size, price earnings ratio, and trading volume.     

Measuring quantile dependence and testing directional predictability between Bitcoin,altcoins and traditional financial assets

This paper studies causal relationships and the potential of improving conditional quantile forecasting between Bitcoin and seven altcoin markets as well as between Bitcoin and three mainstream assets, namely gold, oil, and the S&P500, by applying the Granger-causality in distribution and in quantiles tests. We find significant bidirectional causality between Bitcoin and all altcoins and assets considered in the two distribution tails. An enhanced forecast of Bitcoin price returns is thus derived by conditioning on altcoins or assets and vice versa during extreme market conditions. However, under normal market conditions the results for the centre of the distribution of the Bitcoin price returns conditional on altcoins depend on both the altcoin considered and quantile under investigation. We also find evidence that Bitcoin is not isolated from financial markets, while this developing financial asset is a strong safe-haven for oil and a weak safe-haven for S&P500, but it cannot be considered as either a weak or strong safe-haven for gold. Our results reveal a more complete relationship between Bitcoin and altcoins as well as financial assets than was previously considered.     

Oil Price and Stock Market Synchronization in Gulf Cooperation Council Countries

Knowing that the Gulf Cooperation Council (GCC) economies are dichotomous in nature, and growth in the non-oil sector is tributary to the oil sector, we document the extent of synchronization between crude oil prices and stock markets for each of the GCC markets and for the GCC as an economic bloc. We use both the bivariate and multivariate nonparametric synchronicity measures proposed by Mink et al. (2007) to assess that linkage. We find a low to mild (mild to strong) degree of synchronization between oil price and stock market returns (volatilities). In a very few instances, we find very strong (above 80 percent) associations between these variables. These results hold irrespective of whether we assume that stock market participants form adaptive or rational expectations about the price of oil. Dynamic factor results confirm that shocks to volatility are more important than shocks to oil price returns for the GCC stock markets.     

Striking oil: Another puzzle?

Changes in oil prices predict stock market returns worldwide. We find significant predictability in both developed and emerging markets. These results cannot be explained by time-varying risk premia as oil price changes also significantly predict negative excess returns. Investors seem to underreact to information in the price of oil. A rise in oil prices drastically lowers future stock returns. Consistent with the hypothesis of a delayed reaction by investors, the relation between monthly stock returns and lagged monthly oil price changes strengthens once we introduce lags of several trading days between monthly stock returns and lagged monthly oil price changes.     

Relación de causalidad entre el índice de precios del productor y el índice de precios del consumidor incorporando cambios estructurales El caso de México

This paper analyzes the causal relationship between producer and consumer prices in the case of Mexico during the period 1994:01–2012:02. To do this, we use three unit root tests (Dickey-Fuller, 1979 y 1981; Phillips-Perron, 1988; Lee-Strazicich, 2003) and two tests of causality (Granger,1969; Toda y Yamamoto,1995). The results indicate that taking into account structural changes and the deterministic trend, both price indexes are stationary. We find unidirectional causal relationship running from CPI to IPP.     

The Price Response to S&P 500 Index Additions and Deletions: Evidence of Asymmetry and a New Explanation

We study the price effects of changes to the S&P 500 index and document an asymmetric price response: There is a permanent increase in the price of added firms but no permanent decline for deleted firms. These results are at odds with extant explanations of the effects of index changes that imply a symmetric price response to additions and deletions. A possible explanation for asymmetric price effects arises from the changes in investor awareness. Results from our empirical tests support the thesis that changes in investor awareness contribute to the asymmetric price effects of S&P 500 index additions and deletions.     

Investigating the nature of interaction between crypto-currency and commodity markets

This paper investigates the dynamic relationship and volatility spillovers between cryptocurrency and commodity markets using different multivariate GARCH models. We take into account the nature of interaction between these markets and their transmission mechanisms when analyzing the conditional cross effects and volatility spillovers. Our results confirm the presence of significant returns and volatility spillovers, and we identify the GO-GARCH (2,2) as the best-fit model for modeling the joint dynamics of various financial assets. Our findings show significant dynamic linkages and volatility spillovers between gold, natural gas, crude oil, Bitcoin, and Ethereum prices. We find that gold can serve as a safe haven in times of economic uncertainty, as it is a good hedge against natural gas and crude oil price fluctuations. We also find evidence of bidirectional causality between crude oil and natural gas prices, suggesting that changes in one commodity's price can affect the other. Furthermore, we observe that Bitcoin and Ethereum are positively correlated with each other, but negatively correlated with gold and crude oil, indicating that these cryptocurrencies may serve as useful diversification tools for investors seeking to reduce their exposure to traditional assets. Our study provides valuable insights for investors and policymakers regarding asset allocation and risk management, and sheds light on the dynamics of financial markets.     

Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis

This paper investigates the relationship between investor attention and the major cryptocurrency markets by wavelet-based quantile Granger causality. The wavelet analysis illustrates the interdependence between investor attention and the cryptocurrency returns. Multi-scale quantile Granger causality based on wavelet decomposition further demonstrates bidirectional Granger causality between investor attention and the returns of Bitcoin, Ethereum, Ripple and Litecoin for all quantiles, except for the medium. Among them, the Granger causality from investor attention to the returns is relatively very weak for Ethereum. In the short term, the Granger causality from these cryptocurrency returns to investor attention seems symmetric, but in the medium- and long- term, the causality shows some asymmetry. The Granger causality from investor attention to these cryptocurrency returns is asymmetric and varies across cryptocurrencies and time scales. Specifically, investor attention has a relatively stronger impact on the cryptocurrency returns in bearish markets than that in bullish markets in the short term.