Does social network sentiment influence the relationship between the S&P 500 and gold returns?

This study explored the relationship between investor sentiment (extracted from the StockTwits social network), the S&P 500 Index and gold returns. We investigated bilateral causality between gold prices and S&P 500 prices, the power of investor sentiment and gold returns to predict S&P 500 returns, and the influence of gold returns on S&P 500 volatility. We also considered whether the influence of sentiment varies according to the user's degree of experience. We considered the sentiment of messages that mentioned the S&P 500 Index and that users posted between 2012 and 2016. Granger causality analysis, ARIMA models and GARCH models were used for predicting S&P 500 Index returns and S&P 500 volatility. We observed a causal relationship between gold price and the S&P 500 Index. Our results also suggest that sentiment and gold returns predict S&P 500 Index returns. Finally, we observed that gold returns influence S&P 500 volatility and that the sentiment of experienced users affects S&P 500 returns.     

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.     

The Dynamic Relation Between Stock Returns, Trading Volume, and Volatility

We examine the dynamic relation between returns, volume, and volatility of stock indexes. The data come from nine national markets and cover the period from 1973 to 2000. The results show a positive correlation between trading volume and the absolute value of the stock price change. Granger causality tests demonstrate that for some countries, returns cause volume and volume causes returns. Our results indicate that trading volume contributes some information to the returns process. The results also show persistence in volatility even after we incorporate contemporaneous and lagged volume effects. The results are robust across the nine national markets.     

A High‐Frequency Investigation of the Interaction between Volatility and DAX Returns

One of the most noticeable stylised facts in finance is that stock index returns are negatively correlated with changes in volatility. The economic rationale for the effect is still controversial. The competing explanations have different implications for the origin of the relationship: Are volatility changes induced by index movements, or inversely, does volatility drive index returns? To differentiate between the alternative hypotheses, we analyse the lead‐lag relationship of option implied volatility and index return in Germany based on Granger causality tests and impulse‐response functions. Our dataset consists of all transactions in DAX options and futures over the time period from 1995 to 2005. Analyzing returns over 5‐minute intervals, we find that the relationship is return‐driven in the sense that index returns Granger cause volatility changes. This causal relationship is statistically and economically significant and can be clearly separated from the contemporaneous correlation. The largest part of the implied volatility response occurs immediately, but we also observe a smaller retarded reaction for up to one hour. A volatility feedback effect is not discernible. If it exists, the stock market appears to correctly anticipate its importance for index returns.     

Volatility spillovers between WTI and Brent spot crude oil prices: an analysis of granger causality in variance patterns over time

There has been an increase in price volatility in oil prices during and since the global financial crisis (GFC). This study investigates the Granger causality patterns in volatility spillovers between West Texas International (WTI) and Brent crude oil spot prices using daily data. We use Hafner and Herwartz’s (2006) test and employ a rolling sample approach to investigate the changes in the dynamics of volatility spillovers between WTI and Brent oil prices over time. Volatility spillovers from Brent to WTI prices are found to be more pronounced at the beginning of the analysis period, around the GFC, and more recently in 2020. Between 2015 and 2019, the direction of volatility spillovers runs unidirectionally from WTI to Brent oil prices. In 2020, however, a Granger-causal feedback relation between the volatility of WTI and Brent crude oil prices is again detected. This is due to the uncertainty surrounding how the COVID-19 pandemic will evolve and how long the economies and financial markets will be affected. In this uncertain environment, commodities markets participants could be reacting to prices and volatility signals on both WTI and Brent, leading to the detection of a feedback relation.     

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.     

New evidence of extreme risk transmission between financial stress and international crude oil markets

We aim to analyze the risk transmission between financial stress and crude oil under different shocks, with applying a novel Granger causality test. Recent works suggest that this risk transmission is mixed, however, scholars mainly focus on their average causality but neglect the extreme causality and its time-varying characteristic. Using the weekly data of the financial stress index and WTI prices spanning from 1994 to 2020, we employ the extreme time-domain and frequency-domain Granger causality test to conduct our research. Results obtained from the time-domain test imply that their causality generally originates from extreme shocks rather than non-extreme shocks, which hasn’t been found before. For further distinguishing the long-run and short-run effects of these shocks, we apply the frequency-domain test and discover that these causalities are mainly found for long the run. Thus, investors and policy-makers may benefit from monitoring financial stress, especially under long-term extreme conditions.     

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.     

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.     

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.     

Symmetric and asymmetric nonlinear causalities between oil prices and the U.S. economic sectors

This study investigates the causal dynamics of the U.S. sector price changes and oil price changes using the symmetric nonlinear and asymmetric nonlinear causality tests. We find a unidirectional causality from each sector to the oil market using the Granger and MWald linear causality tests. However, the symmetric nonlinear and asymmetric nonlinear causality for negative price changes tests yield unidirectional causality from the oil to the sector price changes which sharply contrast the evidence using the linear models. We find bidirectional causality using the asymmetric nonlinear test for positive price changes, suggesting temporal, dual and nonlinear information flow during bull markets. Our results from the nonlinear and asymmetric causality tests remain robust after accounting for structural breaks. The empirical findings unravel nonlinear interactions between sector price and oil price changes as well as the importance of signs of changes in the interacting variables, implying oil returns may need to be priced when forecasting sector returns.     

Investigating volatility spillover of energy commodities in the context of the Chinese and European stock markets

This paper investigates spillover from energy commodities to Shanghai stock exchange and European Stock market, and identifies possible risks transmission and portfolio diversification opportunities. The study is conducted on daily spot prices of carbon (CO2) emission, natural gas and crude oil from 16 December 2010 to 29 December 2022, employing Granger causality test, dynamic conditional correlation (DCC), Diebold-Yilmaz (2012) and Barunik-Krehlic (2017) models. Results identify higher volatility and imply greater connectedness in the longer run. Additionally, natural gas is witnessed as the highest contributor of the shocks and crude oil as the highest receiver of the shocks from the network connection. Further results suggest for investment in energy commodities in shorter run rather than long run for efficient portfolio diversification. Results from this study are expected to have practical implications for portfolio managers, investors, and market regulators, given the suggestion of this study to incorporate energy stocks for efficient diversification of risk.     

Chinese institutional investors’ sentiment

We use daily survey data on Chinese institutional investors’ forecasts to measure investors’ sentiment. Our empirical model uncovers that share prices and investor sentiment do not have a long-run relation; however, in the short-run, the mood of investors follows a positive-feedback process. Hence, institutional investors are optimistic when previous market returns were positive. Contrarily, negative returns trigger a decline in sentiment, which reacts more sensitively to negative than positive returns. Investor sentiment does not predict future market movements—but a drop in confidence increases market volatility and destabilizes exchanges. EGARCH models reveal asymmetric responses in the volatility of investor sentiment; however, Granger causality tests reject volatility-spillovers between returns and sentiment.     

Stock returns,trading volume,and volatility: The case of African stock markets

The presence of the African Stock Markets (ASMs) in the global frontier markets indices confirms their global portfolio diversification role. This study investigates the asymmetric and intertemporal causality among the stock returns, trading volume, and volatility of eight ASMs. Results based on the linear model reveal that return generally Granger cause trading volume. However, evidence from the quantile regression shows that lagged trading volume has a negative causal effect on returns at low quantiles and positive causal effects at high quantiles. This evidence is consistent with volume-return equilibrium models, disposition and overconfidence models, and information asymmetry models. The positive causal effects of volatility on volume support the dispersion of beliefs model. In contrast, intertemporal evidence of contemporaneous and lagged causal relationships from trading volume to volatility supports the mixture of distribution hypothesis, sequential information acquisition hypothesis, and dynamic efficient market hypothesis. Volume-return and return-volume causality dynamics are quantile-specific and therefore driven by market conditions. However, the volume-volatility causality is dependent on volatility regimes. The linear model results confirm how model misspecification can distort and even reverse empirical evidence relative to nonlinear models.     

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.     

基于高频数据的股指期货与现货市场波动溢出和信息传导研究

本文利用沪深300指数和当月股指期货连续合约的高频数据,采用非参数方法估计日度股票指数和股指期货的整体波动、连续性波动和跳跃,发现两个市场波动成分存在双向的格兰杰因果关系,但是期货市场的跳跃并不会影响后续股票市场的跳跃。此外,已实现相关系数在股指期货上市初期表现出了较大的变动,整体表现出了较强的联动趋势。最后,日内高频价格之间存在稳定的协整关系,两个市场存在双向的信息传导,股指期货的价格发现功能得到发挥。     

Interdependence and dynamic linkages between the emerging stock markets of South Asia

The present article examines the dynamic linkages between the stock markets of Bangladesh, India, Pakistan and Sri Lanka using a temporal Granger causality approach by binding the relationship among the stock price indices within a multivariate cointegration framework. We also examine the impulse response functions. Our main finding is that in the long run, stock prices in Bangladesh, India and Sri Lanka Granger‐cause stock prices in Pakistan. In the short run there is unidirectional Granger causality running from stock prices in Pakistan to India, stock prices in Sri Lanka to India and from stock prices in Pakistan to Sri Lanka. Bangladesh is the most exogenous of the four markets, reflecting its small size and modest market capitalization.     

Dynamic jump intensities and risk premiums: Evidence from S&P500 returns and options

We build a new class of discrete-time models that are relatively easy to estimate using returns and/or options. The distribution of returns is driven by two factors: dynamic volatility and dynamic jump intensity. Each factor has its own risk premium. The models significantly outperform standard models without jumps when estimated on S&P500 returns. We find very strong support for time-varying jump intensities. Compared to the risk premium on dynamic volatility, the risk premium on the dynamic jump intensity has a much larger impact on option prices. We confirm these findings using joint estimation on returns and large option samples.     

投机基金持仓头寸与国际油价动荡:1994～2009

总规模超过3万亿美元的石油投机基金的偏爱做多行为直接导致了国际石油价格在2002至2009年间急剧动荡。对代表投机基金的非商业持仓头寸与国际油价之间的协整关系及格兰杰因果检验结果显示,石油投机基金的天量投机行为是国际油价动荡的主要原因。因此,要实现石油价格稳定,在改变美元为主导的国际货币体系的同时,还要测算石油投资基金的最优投机度,一旦投机行为触及最优投机度,交易系统应启动熔断制度,限制投机行为。