Predictability of GCC stock returns: The role of geopolitical risk and crude oil returns

Asymmetric effects of climate policy uncertainty, geopolitical risk, and crude oil prices on clean energy prices

We investigate the asymmetric effects of climate policy uncertainty (CPU), geopolitical risk (GPR), and crude oil prices (WTI) on the realized volatility of the returns of clean energy prices (CEP) in the USA. Using the non-linear autoregressive distributed lags (NARDL) model on data from January 2001 to December 2021, we provide evidence that the effects of CPU, GPR, and WTI on CEP's returns and realized volatility differ in the short and long run and are asymmetric. An increase and decrease in CPU affect CEP's realized volatility more than returns in the long run. Notably, an increase in CPU positively affects the CEP's returns, and a decrease negatively affects CEP's returns in the short run. Moreover, an increase in GPR exerts higher effects on returns in the short run, while both an increase and a decrease in GPR have significant long-run effects. An increase or decrease in WTI shows higher effects on CEP's returns and realized volatility in the long run, while an increase in WTI shows short-run effects. Our findings provide valuable information for making investment decisions while considering the asymmetric effects of climate policy uncertainty, geopolitical risks, and crude oil prices.

Using machine learning to analyze the impact of coronavirus pandemic news on the stock markets in GCC countries

COVID-19 has resulted in high volatility in financial markets across the world. The goal of this study is to investigate the impact of COVID-19-related news on the stock markets in Gulf Cooperation Council (GCC) countries. The study utilizes machine learning approaches to assess the role of COVID-19 news in stock return predictability in these markets. The results reveal that the stock markets in the United Arab Emirates (UAE), Qatar, Saudi Arabia, and Oman were impacted by coronavirus-related news; however, this news had no impact on the stocks in Bahrain. Moreover, the results indicate that the impacted markets were influenced differently in terms of the quantities and types of news.

Application of Multifractal Analysis in Estimating the Reaction of Energy Markets to Geopolitical Acts and Threats

Since the industrial revolution, the geopolitics of energy has been a driver of global prosperity and security, and determines the survival of life on our planet. This study examines the nonlinear structure and multifractal behavior of the cross-correlation between geopolitical risk and energy markets (West Texas Intermediate (WTI), Brent, natural gas and heating oil), using the multifractal detrended cross-correlation analysis. Furthermore, an in-depth analysis reveals different associations of the indices of overall geopolitical risk, geopolitical acts, and geopolitical threats against the four energy products. Based on daily data ranging from 1 January 1985 to 30 August 2021, the findings confirm the presence of nonlinear dependencies, suggesting that geopolitical risk and energy markets are interlinked. Furthermore, significant multifractal characteristics are found and the degree of multifractality is stronger between the overall geopolitical risk and WTI while the lowest degree of multifractality is with Brent. Overall, for the WTI and heating-oil markets, the influence of geopolitical threats is more pronounced rather than their fulfilment. Contrarily, the Brent and natural gas are more correlated to geopolitical acts. Energy products exhibit heterogeneous persistence levels of cross-correlation with all the indicators of geopolitical risk, being more persistent in the case of small fluctuations compared to large fluctuations.

Stock Market Synchronization: The Role of Geopolitical Risk

Given the importance of stock market synchronization for international portfolio diversification, we estimate the degrees of co-movements among US, Chinese and Russian markets. By applying the TVP-VAR approach, we measure total and bivariate synchronization indices utilizing daily data from 1998 to 2021. Our analysis demonstrates that the total connectedness index (TCI) is 26.15% among the three markets. We find that the US market is the highest volatility contributor, whereas the Russian market is the highest receiver. Since stock market synchronization is exposed to geopolitical risk, at the second stage, we apply the Quantile-on-Quantile framework to measure the response of total and bilateral connectedness indices to geopolitical risk (GPR). The findings affirm our proposition that GPR impedes TCI when it has a bullish state and a higher quantile of GPR. The response of bilateral connectedness is negative towards GPR concerning US–China and US–Russian pairs. However, the degree of connectedness between Russian and Chinese stock markets is less responsive to GPR.

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model

This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

Geopolitical Uncertainties and Malaysian Stock Market Returns: Do Market Conditions Matter?

Geopolitical uncertainties have been a concern for global economies and financial markets’ participants. By employing Markov switching regression and quantile regression, we investigated the effect of global and country-specific geopolitical uncertainties on Malaysian Conventional and Islamic stock returns in different market conditions. The estimated results of the Markov switching regression show that Malaysian conventional and Islamic stocks react differently to global and country-specific geopolitical uncertainties under different market volatility conditions, implying volatility dependent exposures and reactions to global and country-specific geopolitical uncertainties. The quantile regression results also reveal that Malaysian conventional and Islamic stocks respond differently to global and country-specific geopolitical uncertainties at different market stages. The empirical findings, therefore, indicate a heterogeneous and non-linear stock reaction to geopolitical uncertainties, providing new insights into geopolitical uncertainties and stock return relationships. Hence, the results will be valuable for asset pricing and investments in an emerging market such as the Malaysian market.