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This study aims to identify the ability of gold and cryptocurrency (Cryptocurrency Uncertainty Index (UCRY) Price) as safe haven assets (SHA) for stocks and bonds in both conventional (i.e. stock indices and government bonds) and Islamic markets (i.e. Islamic stock indices and Islamic bonds (IB)).

The authors employed the nonadditive panel quantile regression model by Powell (2016). It measured the safe haven characteristics of gold and UCRY Price for stock indices, government bonds, Islamic stocks, and IB under gold circumstances and level of cryptocurrency uncertainty, respectively. The period spanned from 11 March 2020 to 31 December 2021.

This study discovered three findings, including: (1) gold is a strong safe haven for stocks and bonds in conventional and Islamic markets under bearish conditions; (2) UCRY Price is a strong safe haven for conventional stocks and bonds but only a weak safe haven for Islamic stocks under high crypto uncertainty; and (3) gold offers a safe haven in both emerging and developed countries, while UCRY Price provides a better safe haven in developed than in emerging countries.

Gold always wins big for safe haven properties during unstable economy. It can also win over investors who consider shariah compliant products. Therefore, it should be included in an investor's portfolio. Meanwhile, cryptocurrencies are more common for developed countries. Thus, the governments and regulators of emerging countries need to provide more guidance around cryptocurrency so that the societies have better literacy. On top of that, the investors can consider crypto to mitigate risks but with limited safe haven functions.

The originality aspects of this study include: (1) four chosen assets from conventional and Islamic markets altogether (i.e. stock indices, government bonds, Islamic stock indices and IB); (2) indicator countries selected based on the most used and owned cryptocurrencies for the SHA study; and (3) the utilization of UCRY Price as a crypto indicator and a further examination of the SHA study toward four financial assets.

This paper aims to investigate the hedge, safe-haven and diversifier properties of Islamic indexes, Bitcoin and gold for ten of the most affected countries by the coronavirus, which are the USA, Brazil, the UK, Italy, Spain, Germany, France, Russia, China and Malaysia.

This research uses the Ratner and Chiu (2013) methodology based on the dynamic conditional correlation models to improve Baur and McDermott (2010). The authors adopt a careful investigation of the features of a diversifier, hedge and safe haven using the dynamic conditional correlation–GARCH and quantile regression models.

Empirical results indicate that Islamic indexes are not considered as hedge assets for the conventional market for all studied countries during the COVID-19 pandemic crisis period. However, gold works as a strong hedge in all countries, except for Brazil and Malaysia. Bitcoin is a strong hedge in the USA and a strong hedge and safe haven in China.

International investors in China and the US stock markets should replace Islamic ‎indexes with Bitcoin in their conventional portfolio of securities during the pandemic.

To the best of the authors’ knowledge, this is the first paper that re-evaluates the hedge, safe-haven and diversifier properties of Islamic indexes, Bitcoin and gold for ten of the most affected countries by the coronavirus.

This paper evaluates the performance of actively managed conventional and Islamic equity funds in a developing economy with a focus to assess the performance-growth puzzle posited by Gruber (1993) (a.k.a Gruber’s puzzle). Under the context of an emerging market of Pakistan, this study explores if actively managed equity fund (AMEF) managers have been able to add value by outperforming the market in terms of stock-selection and market-timing abilities; and the comparative performance analysis of Islamic versus conventional AMEFs is also carried out.

We employ Sharpe and Treynor ratios, Capital asset pricing model, Fama–French three factors model (1993), Carhart four-factor model (1997) and Hendrickson (1981) market timing models on 45 equity funds comprising of 23 conventional and 22 Islamic equity funds operating in Pakistan for a period of 10 years. The overall sample period (2008–2018) is divided into two 5 years sub-periods (i.e. 2009–2013 and 2014–2018) and three 3 years sub-periods (2009–2011, 2012–2014 and 2015–2017) to be viewed in conjunction with the country's macro-economic condition.

We report that the actively managed equity funds (AMEFs) were unable to beat the market index with their stock selection or market timing capabilities. However, AMEFs depicted improved performance in the post-global financial crisis period where both conventional and Islamic AMEFs generated substantial rewards for the given amount of risk. Also, conventional AMEFs outperformed Islamic AMEFs potentially due to their holdings in highly leveraged value and large-cap stocks, while Islamic AMEFS invest more cautiously in small-cap and value firms. Analysis of market timing skills revealed that the funds have not been able to select the undervalued stocks and adopted a defensive strategy in the post-global financial crisis recovery period.

Our findings shed some interesting insights and raise some pertinent questions for research, policy, and practice – specifically for developing countries’ context. The no ‘return-growth’ configuration defies its fit with the ‘Gruber puzzle’ and somewhat presents a case of what we call the ‘Inverse Grubber puzzle’. This novel notion of the ‘Inverse Grubber puzzle’ should inform policy and practice to reflect on their practices, institutional arrangement, regulatory framework and policy design in developing economies characterized by lacklustre performance and growth of AMEFs. For example, the regulatory design may consider focusing on stimulating financial inclusion and deepening by motivating low-cost Index tracker funds (ITFs) – with lower fund management costs, while allocating the avoided cost to flow towards effective marketing campaigns driving greater awareness, financial deepening, and investor base diversification. For future research, financial development researchers may explore the implications and appropriateness of AMEFs versus ITFs in other developing economies.

The work reported in this paper is original and constitutes a valuable asset for ethno-religious-sensitive investors. The research has not been published in any capacity and is not under consideration for publication elsewhere.

The purpose of the research is to analyze the impact of world uncertainty and pandemic uncertainty on Islamic financial markets. For representing Islamic financial markets four different Islamic indices (DJ Islamic index, DJ Islamic Asia–Pacific index, DJ Islamic-Europe index and DJ Islamic-US) are taken.

The study employs quantile-on-quantile regression approach to see the overall dependence structure of variables based on quarterly data ranging from 1996Q1 to 2020Q4. This technique considers how quantiles of world uncertainty and pandemic uncertainty asymmetrically affect the quantiles of Islamic stocks by giving an appropriate framework to apprehend the overall dependence structure.

The findings of the study confirm a strong negative impact of world uncertainty and world pandemic uncertainty on regional Islamic stock indices but the strength of the relationship varies according to economic conditions and across the regions. However, the world pandemic effect remains the same and does not change. Conversely, pandemic uncertainty has a larger effect on Islamic indices as compared to world uncertainty.

Our findings have significant implications for investors and policymakers to take proper steps before any uncertainty arise. A coalition of the central bank, government officials and investment bank regulators would be needed to tackle this challenge of uncertainty.

To the best of the authors' knowledge, none of the current works has considered the asymmetric impact of world and pandemic uncertainties on Islamic stock markets at both the bottom and upper quantiles of the distribution of data.

This paper aims to investigate the immediate effect of the outbreak of the COVID-19 pandemic by investigating volatility transmission and dynamic correlation between stock (conventional and Islamic) markets, bitcoin and major commodities such as gold, oil and silver at different investment horizons before and after 161 trading days of the outbreak of the COVID-19 pandemic.

The MGARCH-DCC and maximum overlap discrete wavelet transform -based cross-correlation were used in the estimation of the volatility spillover and continuous wavelet transform in the estimation of the time-varying volatility and correlation between the assets at different investment horizons.

The authors observed a sudden correlation breakdown following the COVID-19 shock. Oil (Bitcoin) was a major volatility transmitter before (during) COVID-19. Digital gold (Bitcoin), gold and silver became highly correlated during COVID-19. The highest co-movement between the assets was observed at medium and long-term investment horizons.

The study findings have a financial implication for day traders, investors and policymakers in the understanding of volatility transmission and intercorrelation in a bid to actively manage stylized and well-diversified asset portfolios.

This study is unique for its employment in estimating the time-varying conditional volatility of the investable assets and cross-correlations between them at different investment horizons, particularly before and after COVID-19 outbreak.

This paper aims to apply several data mining techniques for predicting the daily precision improvement of Jakarta Islamic Index (JKII) prices based on big data of symmetric volatility in Indonesia’s Islamic stock market.

This research uses big data mining techniques to predict daily precision improvement of JKII prices by applying the AdaBoost, K-nearest neighbor, random forest and artificial neural networks. This research uses big data with symmetric volatility as inputs in the predicting model, whereas the closing prices of JKII were used as the target outputs of daily precision improvement. For choosing the optimal prediction performance according to the criteria of the lowest prediction errors, this research uses four metrics of mean absolute error, mean squared error, root mean squared error and R-squared.

The experimental results determine that the optimal technique for predicting the daily precision improvement of the JKII prices in Indonesia’s Islamic stock market is the AdaBoost technique, which generates the optimal predicting performance with the lowest prediction errors, and provides the optimum knowledge from the big data of symmetric volatility in Indonesia’s Islamic stock market. In addition, the random forest technique is also considered another robust technique in predicting the daily precision improvement of the JKII prices as it delivers closer values to the optimal performance of the AdaBoost technique.

This research is filling the literature gap of the absence of using big data mining techniques in the prediction process of Islamic stock markets by delivering new operational techniques for predicting the daily stock precision improvement. Also, it helps investors to manage the optimal portfolios and to decrease the risk of trading in global Islamic stock markets based on using big data mining of symmetric volatility.

This research is a pioneer in using big data mining of symmetric volatility in the prediction of an Islamic stock market index.

This paper aims to analyse the weekday effect on the Nifty Shariah indices as per the Islamic calendar. The study is intended to know about the return and volatility of these indices during Ramadhan and non-Ramadhan days.

The study focuses on analysing the Nifty Shariah indices and Sensex daily returns collected from NSE India and BSE India, respectively, during the period of 1 August 2016 to 31 July 2022. Descriptive statistics are used to analyse the data, while the Ordinary Least Square method is used to determine the impact of weekdays on the Nifty Shariah indices. Additionally, the study applies the GARCH statistical model to examine the influence of Ramadhan on the returns and volatility of the Nifty Shariah indices.

All of the Nifty Shariah indices produced positive returns during the overall sample period. According to the study, Tuesday index returns outperform other weekdays. The GARCH model indicated that the coefficient values for the Nifty 50 Shariah and Nifty 500 Shariah indices were negative. Ramadhan has a strong negative effect on volatility, according to this study.

The outcomes of the research are beneficial for investors aiming to exploit daily or weekly price fluctuations, rather than pursuing extended investment periods. Furthermore, fund managers can employ these findings to shape trading strategies, and academics can examine the performance of Shariah indices in the Indian context. This enables devout investors to make significant financial choices, thus advancing ethical values in society and upholding standards of both public and private morality.

The aim of the paper is to investigate the risk-hedging and/or safe haven properties of environmental, social and governance (ESG) index during the COVID-19 in China.

This paper employs the DCC, VCC, CCC as well as Newey–West estimator regression.

The findings provide empirical evidence of the risk hedging properties of ESG indexes as well as of the environmental, social and governance thematic indexes during the outbreak of the COVID-19 crisis. The results also support the superior risk hedging properties of ESG indexes over cryptocurrency. However, the authors do not find any safe haven properties of ESG, Bitcoin, gold and West Texas Intermediate (WTI).

The paper offers therefore, practical policy implications for asset managers, central bankers and investors suggesting the pandemic risk-hedging opportunities of ESG investments.

The study represents one of the first empirical contributions examining safe-haven and hedging properties of ESG indexes compared to traditional and innovative safe haven assets, during the eruption of the COVID-19 crisis.

This paper researches a bidirectional volatility transmission effect between stocks and exchange rate markets in the six East European and Eurasian countries.

Research process involves creation of transitory and permanent volatilities via optimal component generalized autoregressive heteroscedasticity (CGARCH) model, while these volatilities are subsequently embedded in Markov switching model.

This study’s results indicate that bidirectional volatility transmission exists between the markets in the selected countries, whereas the effect from exchange rate to stocks is stronger than the other way around in both short-term and long-term. In particular, the authors find that long-term spillover effect from exchange rate to stocks is stronger than the short-term counterpart in all countries, which could suggest that flow-oriented model better explains the nexus between the markets than portfolio-balance approach. On the other hand, short-term volatility transfer from stock to exchange rate is stronger than its long-term equivalent.

This suggests that portfolio-balance theory also has a role in explaining the transmission effect from stock to exchange rate market, but a decisive fact is from which direction spillover effect is observed.

This paper is the first one that analyses the volatility nexus between stocks and exchange rate in short and long term in the four East European and two Eurasian countries.

The purpose of this paper is to investigate investor herding behaviour and the effect of presidential elections on investor herding behaviour in African stock markets at the sector level.

The study segregates listed firms into financial, consumer goods, consumer services and basic materials sectors and uses the cross-sectional absolute deviation approach as a metric of detecting herding in each of the sectors. The authors extend the model to tease out the effect of presidential elections on investor herding behaviour.

The study reveals that sectoral differences are fundamental to the evolution of herding. Herding is prominent in a financial services sector dominated by banks. The phenomenon also prevails in markets with smaller consumer goods and services sectors. A post-presidential election effect on investor herding is found for the consumer goods and services sectors of Ghana and a pre-presidential election effect is documented in Nigeria's consumer services sector. The authors conclude that post-presidential election effect is as a result of political connections whilst a pre-presidential election effect is attributable to political business cycles.

The study is based on four African countries due to data constraints. Nonetheless, the study is the first in Africa to the best of the authors' knowledge, and the results are very solid and have a lot of practical and policy implications.

The study has implications for investors as it guides investment behaviour in pre- and post-presidential election periods.

Past studies on investor herding behaviour in African stock markets have largely concentrated on the aggregate market. Knowledge on sectoral differences in investor herding is almost non-existent for African stock markets. Furthermore, premised on the fact that stock markets react to presidential elections, there is no known study that have attempted to examine the effect of presidential elections on investor herding behaviour. This paper contributes to the literature by providing evidence on sectoral differences in investor herding behaviour and the effect of presidential elections on sectoral herding behaviour.