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This study aims to investigate the impact of the COVID-19 pandemic on both of stock prices and investor's sentiment in China during the onset of the COVID-19 crisis.

In this study, the ADCC-GARCH model was used to analyze the asymmetric volatility and the time-varying conditional correlation among the Chinese stock market, the investors' sentiment and its variation. The authors relied on Diebold and Yilmaz (2012, 2014) methodology to construct network-associated measures. Then, the wavelet coherence model was applied to explore the co-movements between these variables. To check the robustness of the study results, the authors referred to the RavenPack COVID sentiments and the Chinese VIX, as other measures of the investor's sentiment using daily data from December 2019 to December 2021.

Using the ADCC-GARCH model, a strong co-movement was found between the investor's sentiment and the Shanghai index returns during the COVID-19 pandemic. The study results provide a significant peak of connectivity between the investor's sentiment and the Chinese stock market return during the 2015–2016 and the end of 2019–2020 turmoil periods. These periods coincide, respectively, with the 2015 Chinese economy recession and the COVID-19 pandemic outbreak. Furthermore, the wavelet coherence analysis confirms the ADCC results, which revealed that the used proxies of the investor's sentiment can detect the Chinese investors' behavior especially during the health crisis.

This study provides two main types of implications: on the one hand, for investors since it helps them to understand the economic outlook and accordingly design their portfolio strategy and allocate decisions to optimize their portfolios. On the other hand, for portfolios managers, who should pay attention to the volatility spillovers between investor sentiment and the Chinese stock market to predict the financial market dynamics during crises periods and hedge their portfolios.

This study attempted to examine the time-varying interactions between the investor's sentiment proxies and the stock market dynamics. Findings showed that the investor's sentiment is considered a prominent channel of shock spillovers during the COVID-19 crisis, which typically confirms the behavioral contagion theory.

This paper aims to investigate the conditional equicorrelation and cross-quantile dependence between the DeFi, European and GCC currency markets (Oman, Qatar, Bahrain, Kuwait, Saudi Arabia and the United Arab Emirates).

This study applies the GARCH-DECO model and cross-quantilogram framework.

The findings reveal evidence of weak and negative average equicorrelations between the examined markets through time, excluding the COVID-19 outbreak and Russia–Ukraine conflict, which is consistent with the literature examining relationships in different markets. From the cross-quantilogram model, the authors note that the dependence between DeFi, EURO and GCC foreign exchange rate markets is greatest in the short run and diminishes over the medium- and long-term horizons, indicating rapid information processing between the markets under consideration, as most innovations are transmitted in the short term.

For the pairs of DeFi and currency markets, the static and dynamic optimal weights and hedging ratios are also estimated, providing new empirical data for portfolio managers and investors.

To the best of the authors’ knowledge, this is one of the most important research looking into the conditional correlation and predictability between the DeFi, EURO and GCC foreign exchange markets. More importantly, this study provides the first empirical proof of the safe-haven, hedging and diversification qualities of DeFi, EURO and GCC currencies, and this work also covers the COVID-19 pandemic and the Russia–Ukraine war with the use of a single dynamic measure produced by the GARCH-DECO model. In addition, the directional predictability between variables under consideration using the cross-quantilogram model is examined, which can be capable of capturing the asymmetry in the quantile dependent structure. The findings are helpful for both policymakers and investors in improving their trading selections and strategies for risk management in different market conditions.

This paper aims to test for volatility spillovers among Islamic stock markets with the exogenous impact of geopolitical risk (GPR) to check the risk transmission among Saudi Arabia, Malaysia, Indonesia and Turkey. Researchers test for both the symmetric and asymmetric risk transmission.

For the symmetric response of volatility, the study uses simple generalized autoregressive conditional heteroscedastic (GARCH) and for the asymmetric response of volatility with the exogenous impact of GPR, the exponential GARCH models have been adopted.

The results suggest spillover effects exist from Turkey to Saudi Arabia, Indonesia to Malaysia and Saudi Arabia and Malaysia to Indonesia. The findings of volatility spillover from GPR to sample countries suggest that only Malaysia and Indonesia experience volatility spillovers from GPR.

The present study is limited to the context of four countries and Islamic equities; the study contributes to the literature on volatility spillover, Islamic finance, GPR and asset pricing.

This study contributes to individual, institutional investors’ policymakers’ knowledge in determining security prices, trading plans, investment hedging and policy regulation.

The extant literature disregards the GPR index to examine the volatility spillover effects among Islamic stock markets, which allow researchers to justify the mechanism of risk transmission due to GPR across the Islamic stock market.

To the best of the authors’ knowledge, this is the first research of its type to look at volatility spillover and GPR transmission in Islamic stock markets.

This study aims to assess how real income, oil prices and gold prices affect housing prices in China from 2010 to 2021.

This study uses a novel bootstrap autoregressive distributed lag (ARDL) testing to empirically analyze the short and long links among the tested variables.

The ARDL estimations demonstrate a positive impact of oil price shocks and real income on housing market prices in both the phrases of the short and long run. Furthermore, the results reveal that gold price shocks negatively affect housing prices both in the short and long run. The result can be attributed to China’s housing market and advanced infrastructure, resulting in a drop in housing prices as gold prices increase. Additionally, the prediction of housing market prices will provide a base and direction for housing market investors to forecast housing prices and avoid losses.

To the best of the authors’ knowledge, this is the first attempt to analyze the effect of gold price shocks on housing market prices in China.

This paper investigates the probable differential impact of the confirmed cases of COVID-19 on the equities markets of G7 and Nordic countries to ascertain possible interdependencies, diversification and safe haven prospects in the era of the COVID-19 pandemic over the short-, intermediate- and long-term horizons.

The authors apply a unique methodology in a denoised frequency-domain entropy paradigm to the selected equities markets (Li et al. 2020).

The authors’ findings reinforce the operability of the entrenched market dynamics in the COVID-19 pandemic era. The authors divulge that different approaches to fighting the pandemic do not necessarily drive a change in the deep-rooted fundamentals of the equities market, specifically for the studied markets. Except for an extreme case nearing the end (start) of the short-term (intermediate-term) between Iceland and either Denmark or the US equities, there exists no potential for diversification across the studied markets, which could be ascribed to the degree of integration between these markets.

The authors’ findings suggest that politicians should pay closer attention to stock market fluctuations as well as the count of confirmed COVID-19 cases in their respective countries since these could cause changes to market dynamics in the short-term through investor sentiments.

The authors measure the flow of information from COVID-19 to G7 and Nordic equities using the entropy methodology induced by the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), which is a data-driven technique. The authors employ a larger sample period as a result of this, which is required to better comprehend the subtleties of investor behaviour within and among economies – G7 and Nordic geographical blocs – which largely employed different approaches to fighting the COVID-19 pandemic. The authors’ focus is on diverging time horizons, and the ICEEMDAN-based entropy would enable us to measure the amount of information conveyed to account for large tails in these nations' equity returns. Furthermore, the authors use a unique type of entropy known as Rényi entropy, which uses suitable weights to discern tailed distributions. The Shannon entropy does not account for the fact that financial assets have fat tails. In a pandemic like COVID-19, these fat tails are very strong, and they must be accounted for.

This paper aims to investigate the role of price-based information from major cryptocurrencies, foreign exchange, equity markets and key commodities in predicting the next-minute Bitcoin (BTC) price. This study answers the following research questions: What is the best sparse regression model to predict the next-minute price of BTC? What are the key drivers of the BTC price in high-frequency trading?

Least absolute shrinkage and selection operator and Ridge regressions are adopted using minute-based open-high-low-close prices, volume and trade count for eight major cryptos, global stock market indices, foreign currency pairs, crude oil and gold price information for February 2020–March 2021. This study also examines whether there was any significant break and how the accuracy of the selected models was impacted.

Findings suggest that Ridge regression is the most effective model for predicting next-minute BTC prices based on BTC-related covariates such as BTC-open, BTC-high and BTC-low, with a moderate amount of regularization. While BTC-based covariates BTC-open and BTC-low were most significant in predicting BTC closing prices during stable periods, BTC-open and BTC-high were most important during volatile periods. Overall findings suggest that BTC’s price information is the most helpful to predict its next-minute closing price after considering various other asset classes’ price information.

To the best of the authors’ knowledge, this is the first paper to identify the covariates of major cryptocurrencies and predict the next-minute BTC crypto price, with a focus on both crypto-asset and cross-market information.

In this study, the authors analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

The study used a mix of wavelet-based approaches, including continuous wavelet transformation and discrete wavelet transformation. The analysis used data from the Geopolitical Risk index (GP{R), Dow Jones Sukuk index (SUKUK), Dow Jones Islamic index (DJII), Dow Jones composite index (DJCI), one of the top crude oil benchmarks which is based on the Europe (BRENT) (oil fields in the North Sea between the Shetland Island and Norway), and Global Gold Price Index (gold) from May 31, 2012, to June 13, 2022.

The results of the study indicate that during the COVID-19 and Russia–Ukraine conflict period geopolitical risk (GPR) was in the leading position, where BRENT confirmed the lagging relationship. On the other hand, during the COVID-19 pandemic period, SUKUK, DJII and DJCI are in the leading position, where GPR confirms the lagging position.

The present study is unique in three respects. First, the authors revisit the influence of GPR on global asset markets such as Islamic stocks, Islamic bonds, conventional stocks, oil and gold. Second, the authors use the wavelet power spectrum and coherence analysis to determine the level of reliance based on time and frequency features. Third, the authors conduct an empirical study that includes recent endogenous shocks generated by health crises such as the COVID-19 epidemic, as well as shocks caused by the geopolitical danger of a war between Russia and Ukraine.

1. We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

2. The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.

3. GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.

4. Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.

We analyze the impact of geopolitics risk on Sukuk, Islamic and composite stocks, oil and gold markets and portfolio diversification implications during the COVID-19 pandemic and Russia–Ukraine conflict period.

The results of the wavelet-based approach show that Dow Jones composite and Islamic indexes have observed the highest mean return during the study period.

GPR and BRENT are estimated to have the highest amount of risk throughout the observation period.

Dow Jones Sukuk, Islamic and composite stock show similar trend of volatility during the COVID-19 pandemic period and comparatively gold observes lower variance during the COVID-19 pandemic and Russia–Ukraine conflict.

This paper aims to provide new perspectives on the integration of East Asian stock markets and the dynamic volatility transmission to the Bitcoin market utilising daily data from 2014 to 2020.

The authors undertake comprehensive analyses of the dependency dynamics, systemic risk and volatility spillover between major East Asian stock and Bitcoin markets. The authors employ a vine-copula-CoVaR framework and a VAR-BEKK-GARCH method with a Wald test.

(a) With exception of KS11 and N225; HSI and SSE; HSI and KS11, which have moderate dependence, dependencies among other markets are low. In terms of tail risk, the upper tail risk is more significant in capturing strong common variation. (b) Two-way and asymmetric risk spillover effects exist in all markets. The Hong Kong and Japanese stock markets have significant risk spillovers to other markets, and quite notably, the Chinese stock market is the largest recipient of systemic risk. However, the authors observe a more significant risk spillover from the Chinese stock market to the Bitcoin market. (c) The VAR-BEKK-GARCH results confirm that the Korean market is a significant emitter of volatility spillovers. The Bitcoin market does provide diversification benefits. Interestingly, the Chinese stock market has an intriguing relationship with Bitcoin. (d) An increase in spillovers in East Asia boosts spillovers to Bitcoin, but there is no intuitive effect of Bitcoin spillovers on East Asian spillovers.

For the first time, the authors examine the dynamic linkage between Bitcoin and the major East Asian stock markets.

This paper aims to examine the dynamic conditional correlation (DCC) and hedging ratios between Dow Jones markets and oil, gold and bitcoin. Using daily data, including the COVID-19 pandemic and the Russia–Ukraine war. We employ the DCC-generalized autoregressive conditional heteroskedasticity (GARCH) and asymmetric DCC (ADCC)-GARCH models.

DCC-GARCH and ADCC-GARCH models.

The most of DCCs among market pairs are positive during COVID-19 period, implying the existence of volatility spillovers (Contagion-effects). This implies the lack of additional economic gains of diversification. So, COVID-19 represents a systematic risk that resists diversification. However, during the Russia–Ukraine war the DCCs are negative for most pairs that include Oil and Gold, implying investors may benefit from portfolio-diversification. Our hedging analysis carries significant implications for investors seeking higher returns while hedging their Dow Jones portfolios: keeping their portfolios unhedged is better than hedging them. This is because Islamic stocks have the ability to mitigate risks.

Our paper may make a valuable contribution to the existing literature by examining the hedging of financial assets, including both conventional and Islamic assets, during periods of stability and crisis, such as the COVID-19 pandemic and the Russia–Ukraine war.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.