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The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period.

This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model.

The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets.

This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

This article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic.

The authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach.

Employing thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period; the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period.

Based on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods.

This research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)’ response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020; Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach.

This study investigates clean energy, commodities, green bonds and environmental, social and governance (ESG) index prices forecasting and assesses the predictive performance of various factors on these asset prices, used for the development of a robust forecasting support decision model using machine learning (ML) techniques. More specifically, we explore the impact of the financial stress on forecasting price.

We utilize feature selection techniques to evaluate the predictive efficacy of various factors on asset prices. Moreover, we have developed a forecasting model for these asset prices by assessing the accuracy of two ML models: specifically, the deep learning long short-term memory (LSTM) neural networks and the extreme gradient boosting (XGBoost) model. To check the robustness of the study results, the authors referred to bootstrap linear regression as an alternative traditional method for forecasting green asset prices.

The results highlight the significance of financial stress in enhancing price forecast accuracy, with the financial stress index (FSI) and panic index (PI) emerging as primary determinants. In terms of the forecasting model's accuracy, our analysis reveals that the LSTM outperformed the XGBoost model, establishing itself as the most efficient algorithm among the two tested.

This research enhances comprehension, which is valuable for both investors and policymakers seeking improved price forecasting through the utilization of a predictive model.

To the authors' best knowledge, this marks the inaugural attempt to construct a multivariate forecasting model. Indeed, the development of a robust forecasting model utilizing ML techniques provides practical value as a decision support tool for shaping investment strategies.

This paper aims to examine the dynamic spillover effects and network connectedness between the oil prices and the Islamic and conventional financial markets in the Gulf Cooperation Council countries. The focus is on network connectedness during the 2008–2009 global financial crisis, the 2014–2016 oil crisis and the COVID-19 pandemic. The authors use daily data covering the period from January 1, 2007 to April 14, 2022.

This study applies a spillover analysis and connectedness network to investigate the risk contagion among the Islamic and conventional stock–bond markets. The authors rely on Diebold and Yilmaz’s (2012, 2014) methodology to construct network-associated measures.

The results suggest that overall connectedness among financial market uncertainties increased during the global financial crisis, the oil price collapse of 2014–2016 and the COVID-19 crisis. In addition, the authors show that the contribution of oil shocks to the financial system is limited, as the oil market was a net receiver during the 2014 oil shock and the COVID-19 crisis. On the other hand, the Islamic and conventional stock markets are extensive sources of network effects on the oil market and Islamic and conventional bond markets. Furthermore, the authors found that the Sukuk market was significantly affected by the COVID-19 pandemic, whereas the conventional and Islamic stock markets were the highest transmitters of shocks during the COVID-19 pandemic outbreak. Moreover, oil revealed a weak connectedness with the Islamic and conventional stock markets during the COVID-19 health crisis, implying that it helps provide diversification benefits for international portfolio investors.

This study contributes to this field by improving the understanding of the effect of fluctuations in oil prices on the dynamics of the volatility connection between oil and Islamic and conventional financial markets during times of stress through a network connectedness framework. The main results of this study highlight the role of oil in portfolio allocation and risk minimization when investing in Islamic and conventional assets.

This paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis.

The authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets.

The results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers.

This study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation.

This finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly.

This paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.

First, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.

Using the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.

This study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.

The important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

The purpose of this paper is to conduct a behavioral analysis, through overconfidence, in order to understand how this cognitive bias could affect risk taking and inefficiency in Islamic and conventional banks operating in the MENA region.

To achieve the objective, the authors considered two overconfidence proxies, namely loan growth rate and net interest margin. Using the generalized method of moments method regressions for panel data, the authors found that the two overconfidence proxies have an effect on the risk exposure and consequently on the efficiency level of Islamic and conventional banks.

In general, overconfidence bias causes excessive risk taking and the degradation of the cost efficiency level. Moreover, these effects emerge with a delay of three to four years and have implications that are not too different for both types of banks.

The main motivation underlying this research study is the relatively new field of behavioral finance way in treating the topic of overconfidence. The particularity of the overconfidence bias topic is its assumption that financial decisions can be influenced by cognitive biases, ignoring the fact of a predetermined risk-return calculation.

In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods.

The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness.

The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader’s seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index.

The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies.

First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic.

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.

The purpose of this paper is to explore the relationship between Dow Jones Islamic Market World Index, Islamic gold-backed cryptocurrencies and halal chain in the presence of state (regime) dynamics.

The authors have used the Markov-switching model to identify bull and bear market regimes. Moreover, the dynamic conditional correlation, the Baba, Engle, Kraft and Kroner- generalized autoregressive conditional heteroskedasticity and the wavelet coherence models are applied to detect the presence of spillover and contagion effects.

The findings indicate various patterns of spillover between halal chain, Dow Jones Islamic Market World Index and Islamic gold-backed cryptocurrencies in high and low volatility regimes, especially during the COVID-19 pandemic. Indeed, the contagion dynamics depend on the bull or bear periods of markets.

These present empirical findings are important for current and potential traders in gold-backed cryptocurrencies in that they facilitate a better understanding of this new type of assets. Indeed, halal chain is a safe haven asset that should be combined with Islamic gold-backed cryptocurrencies for better performance in portfolio optimization and hedging, mainly during the COVID-19 period.

To the best of the authors’ knowledge, this paper is the first research on the impact of the halal chain on the Dow Jones Islamic Market World Index return, Islamic gold-backed cryptocurrencies returns in the bear and bull markets around the global crisis caused by the COVID-19 pandemic.