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This article questions the validity of regression models when high correlations exist between independent variables and presents the application of VAR as an alternative technique through the comparison of two groups of selected stocks that represent components of Dow Jones and S&P 500 indices, respectively. The results indicate that panel regressions face serious specification problems, while the impulse response function underlines that the shock to the volume innovation has a mostly positive impact on the volatility in both S&P and Dow Jones sample, but the tendency cannot be easily accounted for. The positive impact of volatility shocks on the inter market depth is rather unexpected, but it may be associated with an increase in volume that does not enormously enhance the spread up to the point where it will be too costly for market‐makers to trade, and accordingly, quickly narrows the spread to absorb new liquidity influx in the market. In the Granger causality tests Dow Jones stocks with comparatively larger average volume depth values and price levels provide slightly stronger relations between analyzed variables compared to the stocks included in the S&P sample.

The purpose of this paper is twofold: first, to propose a new robust volatility ratio (RVR) that compares the intraday high–low volatility with that of the intraday open–close volatility estimator; and second, to empirically test the proposed RVR on the cross-sectional (CS) average of the constituent stocks of India’s BSE Sensex and US’s Dow Jones Industrial Average index to find the evidence of “excess volatility.”

The authors model the proposed RVR by assuming the logarithm of the price process to follow the Brownian motion. The authors have theoretically shown that the RVR is unbiased in the case of zero drift parameter. Moreover, the RVR is found to be an even function of the non-zero drift parameter.

The empirical results show that the analysis based on the RVR supports the existence of “excess volatility” in the CS average of the constituent stocks of India’s BSE Sensex and US’s Dow Jones index. In particular, the authors have observed that the CS average of individual constituent stocks of BSE Sensex is found to be more excessively volatile than the US’s Dow Jones index during the period of the study from January 2008 to September 2016, based on multiple k-day time window analysis.

The study has implications for the policy makers and practitioners who would like to understand the volatility behavior in the asset returns based on the RVR of this study. In general, the proposed model can be used as a specification tool to find whether the stock prices follow the random walk behavior or excessively volatile.

The authors contribute to the existing volatility literature in finance by proposing a new RVR based on extreme values of asset prices and absolute returns. The authors implement the bootstrap technique on RVR to find the estimates of mean and standard error for multiple k-day time windows. The RVR can capture the excess volatility by comparing two independent volatility estimators. This is possibly the first study to find the CS average of all the constituent stocks of BSE Sensex based on the RVR.

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 compare and analyse the aspects of Shariah screening methodologies within the selected Gulf Cooperation Council (GCC) countries as well as comparing the methodologies with the USA, and to examine how Shariah screening methodologies affect financing and investing activities of a firm.

Shariah screening methodologies within the selected GCC countries and between the GCC countries and the USA are compared on the basis of the data collected from secondary sources.

Design, qualification and Shariah governance set the Shariah screening methodologies within the GCC countries apart. Feasibility, duration, economic viability and funds required differentiate these Shariah screening methodologies between the GCC countries and the USA. Shariah screening methodologies implied in the USA is more stringent than in the GCC countries.

The suggestions in this study include using a longer research timeline, examining many more number of countries’ Shariah screening methodologies and exploring other types of Shariah screening methodologies.

The possibility of generalising the implementation of strict and uniform Shariah screening methodologies across all the country-specific Shariah indices amongst Muslim nations, globally, is likely to benefit all the Muslim countries, by strengthening the understanding, interaction and economic co-operation amongst these countries.

People’s needs can be tended to if Maqasid Al-Shariah (objectives of Shariah) is achieved through flexibility, dynamism and creativity within the social policy.

Aspects of Shariah screening methodologies are compared and contrasted within the selected GCC countries as well as between the GCC countries and the United States and the role of Shariah screening methodologies is examined in order to determine the extent of what is Shariah-Compliant and what is Non-Shariah Compliant for a firm.

The purpose of this paper is to present the Islamic stock and Sukuk market efficiency and focus on the presence of investor herding behaviour (HB) captured by Hurst exponent estimation.

The Hurst exponent was estimated with various methods. The authors studied the evolving efficiency of the “Dow Jones” indices from 1 January 2010 to 30 December 2016 using a rolling sample of the Hurst exponent. In addition, they used a time-varying parameter method based on the Hurst of delayed returns. After that, the robust Hurst method was considered. In the next step, the efficiency of the different activity types of Islamic bonds was studied using an efficiency index. Finally, the Hurst exponent estimates were applied to assess the presence of HB.

The results show that, firstly, there’s a strong correlation between the “DJIM” and “DJSI” prices and returns. Secondly, by using robust Hurst estimate, it is observed that the “DJIM” is the most efficient market. The Hurst exponent estimation results show that HB is more intensive in the Islamic stock market. These results indicate also the inexistence of this behaviour in the studied Sukuk market.

Sukuk as Islamic financial assets is recent. Their relative time series are not long enough to apply the long memory approach. Furthermore, this work can be extended to study other Islamic financial markets.

Herding affects risk-return characteristics of assets and has an impact on asset pricing models. Practitioners are interested in understanding herding and its timing as it might create profitable trading opportunities.

This work analyses the impact of Islamic principles on the financial markets and their ability to understand some behavioural biases.

This study contributes to the literature by identifying the efficiency and the presence of HB with Hurst exponent estimation in Islamic markets.

Introduction – Emerging markets are under the influence of many external factors in global market conditions. International interest rates and price fluctuations in major stock market indices are also among these factors. The FED policies shape the international capital movements in particular, which significantly affects the emerging markets. For this reason, emerging stock markets may show different reactions especially in times of crisis.

Purpose – The purpose of this study is to investigate whether the BIST30 index acted in accordance with the overreaction hypothesis (ORH) against the return changes in the Dow Jones Industrial Average (DJIA) index in the process of the 2008 global financial crisis.

Methodology – The data set of the study was analysed by dividing it into two periods. The first period is the monetary expansion period between 17 August 2007, when the Federal Reserve (FED) reduced the interest rate for the first time, until 22 May 2013 when the FED announced that it would reduce the bond purchases. The second period is the monetary contraction period including the dates between 23 May 2013 and 1 June 2017. An error correction model (ECM) was established in both periods for the indices, determined as cointegrated. The validity of the ORH was tested by Cumulative Abnormal Return (CAR) Analysis.

Findings – According to the ECM, the authors identified that the effect of short-term changes in the DJIA return in the monetary expansion period on BIST30 index return was higher than that in the monetary contraction period. However, according to the findings obtained from the CAR analysis results, the BIST30 index did not generally act in accordance with the ORH against the DJIA. Findings can be appreciated as a decision-making tool especially for investment specialists and investors interested in securities investments.

Lowering of investment barriers between European nations has led to increasing integration of their capital markets. Consequently, global investors may be well‐advised to evaluate European stocks, not on the basis of the country of listing, but on the basis of the transnational industrial sector to which the stocks belong. This study utilizes performance measures, grounded in modern portfolio theory, to assess the risk‐adjusted return that has accrued to major transnational industrial sectors in Europe, such as consumer products, technology, utilities and financial services. The empirical documentation generated here can be used by international investors as input in decision making for sectorial allocation of funds in the European component of their global stock portfolios.

In this paper we develop a new semi-parametric model for conditional correlations, which combines parametric univariate Generalized Auto Regressive Conditional Heteroskedasticity specifications for the individual conditional volatilities with nonparametric kernel regression for the conditional correlations. This approach not only avoids the proliferation of parameters as the number of assets becomes large, which typically happens in conventional multivariate conditional volatility models, but also the rigid structure imposed by more parsimonious models, such as the dynamic conditional correlation model. An empirical application to the 30 Dow Jones stocks demonstrates that the model is able to capture interesting asymmetries in correlations and that it is competitive with standard parametric models in terms of constructing minimum variance portfolios and minimum tracking error portfolios.

This paper aims to investigate the relationship between sustainable investments and a series of uncertainties from January 2014 to December 2021, including many economic and political turbulences and the COVID-19 pandemic.

The authors use Rényi’s transfer entropy method, a nonparametric flexible tool that considers both the center distribution and lower quantiles, capturing extreme rare events that give additional insights to analysis.

The authors’ results indicate significant bidirectional information transmissions between the crude oil volatility and sustainability indices. The authors report information flows between the cryptocurrency uncertainty and sustainability indices considering tail events. The results are essential for market participants making decisions during turbulent times.

This paper is carried out for a variety of uncertainty measures and environmental, social and governance (ESG) portfolios of both developed and developing markets. It adds to literature in terms of methodology used. Rényi’s transfer entropy methodology is first used to measure the relationship between uncertainties and ESG investments.

This study analyses the impact of economic and trade policy uncertainty on US and Chinese stock markets. Also, this study examines the hedge and safe-haven properties of US and China stocks against both US and Chinese economic and trade policy uncertainty.

To achieve the desired goals, the authors employ Dynamic Conditional Correlation through Glosten et al. (1993) model based on the Generalized Autoregressive Conditional Heteroscedasticity (DCC-GJR-GARCH (1, 1)) and Quantile cross-spectral (QS) models. The study uses monthly observations spanning from March 2010 to June 2022.

This study evidence that the economic and trade policy uncertainty between USA and China is extremely sensitive and has high volatility clustering effects on DJChina88 and DJUS, respectively. Conversely, against the Chinese economic and trade policy uncertainty, the US stock market indexes show both hedging properties across the period and safe-haven during COVID-19 and Russia–Ukraine crises. In contrast, among the Chinese stock markets, only DJShenzhen and DJShanghai stock indices might provide strong hedging and safe-haven properties against the US economic and trade policy uncertainties; however, DJShenzhen (DJChina88) stock shows weak hedge and safe-haven properties (hedging benefits) against Chinese trade policy uncertainty (CTPU) (Chinese economic policy uncertainty [CEPU]).

The findings have significant implications for investors, portfolio managers and regulators in hedging and making proper decisions under uncertain circumstances.

The study extends the literature on stock market performance to cover the economic and trade policy uncertainty by providing novel evidence during the recent COVID-19 and Russia–Ukraine invasion.