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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.

Market links (and price discovery) between financial assets and lead–lag relationships are topics of interest for financial economists, financial managers and analysts. The lead–lag relationship analysis should consider both short and long-term investors. From a portfolio diversification perspective, the first type of investor is generally more interested in determining the co-movement of financial assets at higher frequencies, which are short-run fluctuations, while the latter concentrates on the relationship at lower frequencies, or long-run fluctuations. The paper aims to discuss these issues.

For this study, a technique was employed known as the wavelet approach, which has recently been imported to finance from engineering sciences to study the co-movement dynamics between global sukuk and bond markets. Data cover the period from January 2010 to December 2015.

The results indicate that: there is no unidirectional causality from developed market bond indices to Malaysia and Dow Jones indices, which is promising for fixed-income investors of a developed market; and in relation to emerging markets, the Malaysian sukuk market has a bidirectional causality with Indonesia, Malaysia, India and South Korea bond indices but not China bond indices, while in terms of the Dow Jones sukuk index, there is no unidirectional causality between the listed emerging markets and the sukuk index except Indonesia’s market during the sample period.

This analysis provides evidence regarding the timely and appropriate measure of correlation changes and the behaviour of sukuk and bond indices globally, which is beneficial to the management of sukuk and bond portfolios.

The evidence hitherto unexplored, which was produced by the application of a wavelet cross-correlation amongst the selected sukuk and bond indices, provides robust and useful information for international financial analysts as well as long and short-term investors.

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.

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.

The purpose of this study is to examine empirically the conditional correlation between the major US indices (S&P500 index and Dow Jones Industrial index) and three selected meat commodities as: Feeder Cattle, Leen Hogs and Live Cattle during the period from July 22, 2010 to June 30, 2017.

In this study, the authors use for the first time the GARCH-DECO (1,1) to examine empirically the conditional nexus between the major US indices (S&P500 index and Dow Jones Industrial index) and three selected meat commodities as; Feeder Cattle, Leen Hogs and Live Cattle during the period from July 22, 2010 to June 30, 2017.

From the empirical findings, the authors conclude the existence of a highly significance of conditional heteroscedasticity parameters can demonstrate us to distinguish the nature of the volatility dependency between S&P500 index and Dow Jones Industrial index and three selected meat commodities indices.

This can find clear the significance of relationship in the process of financialization of the major US index and meat commodities indices in the case of this paper.

The purpose of this paper is to examine the connectedness across a variety of Sukuk and conventional bond indices and the implications for optimal asset allocation for the period January 1, 2010–April 30, 2020.

The data set consists of five major Sukuk (Dow Jones Sukuk, Thompson Reuters BPA Malaysia Sukuk, Indonesia Government Sukuk, S&P MENA Sukuk and Tadawul Sukuk and Bonds Index) and five conventional bond indexes, one for developed (USA) and four for emerging markets (Malaysia, Indonesia, Africa and Qatar). This study investigates the connectedness and volatility spillover effects across the aforementioned indices, by following the Diebold and Yilmaz (2012) approach, based on the time-varying parameter vector autoregressive (TVP-VAR) model. In addition, this paper provides optimal hedge ratios and portfolio weights for investors.

The empirical results show that Sukuk and conventional bond markets are highly integrated and that total connectedness exhibits sensitivity to exogenous shocks. The Dow Jones and the Malaysian Sukuk indices are the primary shock transmitters to other markets. However, the weak volatility spillovers between the Dow Jones and conventional bonds suggest that opportunities for optimal asset allocation may in fact exist. The highest (lowest) hedging effectiveness can be achieved by taking a short position in Malaysian (Qatarian) bonds.

To the best of the knowledge, this is the largest sample taken into account to investigate the connectedness between Sukuk and conventional bonds.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This paper proposes the Student's t Dynamic Linear Regression (St-DLR) model as an alternative to the various extensions/modifications of the ARCH type volatility model. The St-DLR differs from the latter models of volatility because it can incorporate exogenous variables in the conditional variance in a natural way. Moreover, it also addresses the following issues: (i) apparent long memory of the conditional variance, (ii) distributional assumption of the error, (iii) existence of higher moments, and (iv) coefficient positivity restrictions. The model is illustrated using Dow Jones data and the three-month T-bill rate. The empirical results seem promising, as the contemporaneous variable appears to account for a large portion of the volatility.

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 paper aims to investigate the financial uncertainty vary according to different financial assets type: conventional and Islamic.

Common factors are related to risk or known information. For this, the authors use general dynamic factor model to extract common variation between both types of indexes. Then they calculate stochastic volatility for each idiosyncratic component. They also carry out the study on three different family indexes respectively, Dow Jones, S&P and MSCI indexes, for the period going from January 1, 2008 to June 30, 2018. Through a comparison analysis with uncertainty index designed for conventional assets, the authors examine the similarity between the two indexes via mean, median and variance tests. They decrypt the interrelation between them by using OLS linear regression, vector autoregressive model.

The findings show that Islamic assets uncertainty is different from conventional uncertainty level. This difference can be due to the Shariah screening and the prohibition of gharar. The main findings suggest that Islamic financial uncertainty is lower than conventional one. The OLS results prove that conventional financial uncertainties have no impact on their Islamic counterparts. In addition, Islamic financial uncertainty appears to have no significant influence on conventional one exception for Dow Jones pair. Overall, the findings support the decoupling hypothesis in term of uncertainty only for SP and MSCI indexes.

Risk averse investors can find their claim in Shariah-compliant assets, as it offers a low level of financial uncertainty. A portfolio manager may benefit from the long run non-association in uncertainty between Islamic and conventional assets especially in time of crisis.

In this work, the authors measured financial uncertainty differently and take into account the specific features of each index type to improve the results quality.