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This research provides some evidence by the vine copula approach, suggesting the significant and symmetric causal relation between subsections of Baltic Exchange and hence concluding that investing in different indexes, which is currently a risk diversification system, is not a correct risk reduction strategy.

The daily observations of Baltic Capesize Index (BCI), Baltic Handysize Index (BHSI), Baltic Dirty Tanker Index (BDTI) and Baltic LNG Tanker Index (BLNG) over an eight-year period have been used. After collecting data, calculating the return and estimating the marginal distribution of return rates for each of the indexes applying asymmetric power generalized autoregressive conditional heteroskedasticity and autoregressive moving average (APGARCH-ARMA), and with the assumption of skew student's t-distribution, the dependence of Baltic indexes was modeled based on Vine-R structures.

A positive and symmetrical correlation was observed between the study groups. High and low tail dependence is observed between all four indexes. In other words, the sector business groups associated with each of these indexes react similarly to the extreme events of other groups. The BHSI has a pivotal role in examining the dependency structure of Baltic Exchange indexes. That is, in addition to the direct dependence of Baltic groups, the dependence of each group on the BHSI can transmit accidents and shocks to other groups.

Since the Baltic Exchange indexes are tradable, these findings have implications for portfolio design and hedging strategies for investors in shipping markets.

Vine copula structures proves the causal relationship between different Baltic Exchange indexes, which are derived from different types of markets.

This paper aims to analyze the dependence structure between the Green Sukuk Spread in Malaysia and uncertainty factors from January 1, 2017, to May 23, 2023, covering two main periods: the pre-COVID-19 and the COVID-19 periods.

This study contributes to the current literature by explicitly modeling nonlinear dependencies using the Regular vine copula approach to capture asymmetric characteristics of the tail dependence distribution. This study used the Archimedean copula models: Student’s-t, Gumbel, Gaussian, Clayton, Frank and Joe, which exhibit different tail dependence structures.

The empirical results suggest that Green Sukuk and various uncertainty variables have the strongest co-dependency before and during the COVID-19 crisis. Due to external uncertainties (COVID-19), the results reveal that global factors, such as the Infect-EMV-index and the higher financial stress index, significantly affect the spread of Green Sukuk. Interestingly, in times of COVID-19, its dependence on Green Sukuk and the news sentiment seems to be a symmetric tail dependence with a Student’s-t copula. This result is relevant for hedging strategies, as investors can enhance the performance of their portfolio during the COVID-19 crash period.

This study contributes to a better understanding of the dependency structure between Green Sukuk and uncertainty factors. It is relevant for market participants seeking to improve their risk management for Green Sukuk.

This paper examines and forecasts correlations between cryptocurrencies and major fiat currencies using Generalized Autoregressive Score (GAS) time-varying copulas. The authors examine to which extent the multivariate GAS method captures the volatility persistence and the nonlinear interaction effects between cryptocurrencies and major fiat currencies.

The authors model tail dependence between conventional currencies and Bitcoin utilizing a Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedastic model (GJR-GARCH)-GAS copula specification, which allows detecting the leptokurtic feature and clustering effects of currency returns distribution.

The authors' results show evidence of multiple tail dependence regimes, implying the unsuitability of applying static models to entirely describe the extreme dependence between Bitcoin and fiat currencies. Compared to the most common constant copulas, the authors find that the multivariate GAS copulas better forecast the volatility and dependency between cryptocurrencies and foreign exchange markets. Furthermore, based on the value-at-risk (VaR) and expected shortfall (ES) analyses, the authors show that the multivariate GAS models produce accurate risk measures by adding cryptocurrencies to a portfolio of fiat currencies.

This paper has two main contributions to the existing literature on cryptocurrencies. First, the authors empirically examine the tail dependence structure between common conventional currencies and bitcoin using GJR-GARCH GAS copulas which consider the leptokurtic feature and clustering effects of currency returns distribution. Second, by modeling VaR and ES, the authors test the implication of using time-varying models on the performance of currency portfolios, including cryptocurrencies.

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.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

This paper aims to empirically investigate time–frequency linkages between Euramerican mature and Asian emerging crude oil futures markets in terms of correlation and risk spillovers.

With daily data, the authors first undertake the MODWT method to decompose yield series into four different timescales, and then use the R-Vine Copula-CoVaR to analyze correlation and risk spillovers between Euramerican mature and Asian emerging crude oil futures markets.

The empirical results are as follows: (a) short-term trading is the primary driver of price volatility in crude oil futures markets. (b) The crude oil futures markets exhibit certain regional aggregation characteristics, with the Indian crude oil futures market playing an important role in connecting Euramerican mature and Asian emerging crude oil futures markets. What’s more, Oman crude oil serves as a bridge to link Asian emerging crude oil futures markets. (c) There are significant tail correlations among different futures markets, making them susceptible to “same fall but different rise” scenarios. The volatility behavior of the Indian and Euramerican markets is highly correlated in extreme incidents. (d) Those markets exhibit asymmetric bidirectional risk spillovers. Specifically, the Euramerican mature crude oil futures markets demonstrate significant risk spillovers in the extreme short term, with a relatively larger spillover effect observed on the Indian crude oil futures market. Compared with India and Japan in Asian emerging crude oil futures markets, China's crude oil futures market places more emphasis on changes in market fundamentals and prefers to hold long-term positions rather than short-term technical factors.

The MODWT model is utilized to capture the multiscale coordinated motion characteristics of the data in the time–frequency perspective. What’s more, compared to traditional methods, the R-Vine Copula model exhibits greater flexibility and higher measurement accuracy, enabling it to more accurately capture correlation structures among multiple markets. The proposed methodology can provide evidence for whether crude oil futures markets exhibit integration characteristics and can deepen our understanding of connections among crude oil futures prices.

Being an efficient mechanism for the value of money, public–private partnership (PPP) is one of the most prominent approaches for infrastructure construction. Hence, many controversies about the performance effectiveness of these delivery systems have been debated. This research aims to develop a novel performance management perspective by revealing the causal effect of key performance indicators (KPIs) on PPP infrastructures.

The literature review was used in this study to extract the PPPs KPIs. Experts’ judgment and interviews, as well as questionnaires, were designed to obtain data. Copula Bayesian network (CBN) has been selected to achieve the research purpose. CBN is one of the most potent tools in statistics for analyzing the causal relationship of different elements and considering their quantitive impact on each other. By utilizing this technique and using Python as one of the best programming languages, this research used machine learning methods, SHAP and XGBoost, to optimize the network.

The sensitivity analysis of the KPIs verified the causation importance in PPPs performance management. This study determined the causal structure of KPIs in PPP projects, assessed each indicator’s priority to performance, and found 7 of them as a critical cluster to optimize the network. These KPIs include innovation for financing, feasibility study, macro-environment impact, appropriate financing option, risk identification, allocation, sharing, and transfer, finance infrastructure, and compliance with the legal and regulatory framework.

Identifying the most scenic indicators helps the private sector to allocate the limited resources more rationally and concentrate on the most influential parts of the project. It also provides the KPIs’ critical cluster that should be controlled and monitored closely by PPP project managers. Additionally, the public sector can evaluate the performance of the private sector more accurately. Finally, this research provides a comprehensive causal insight into the PPPs’ performance management that can be used to develop management systems in future research.

For the first time, this research proposes a model to determine the causal structure of KPIs in PPPs and indicate the importance of this insight. The developed innovative model identifies the KPIs’ behavior and takes a non-linear approach based on CBN and machine learning methods while providing valuable information for construction and performance managers to allocate resources more efficiently.

This study aims to empirically explore and compare the dynamic dependency between health-care sector and Islamic industries before, during and after the COVID-19 pandemic.

Time-varying student-t copula is used for before, during and after COVID-19 periods. The data used are the daily frequency price series of the selected markets from February 2017 to October 2023.

Empirical results found strong evidence of significant impact of the COVID-19 pandemic on the dependence structure of the studied indexes: Co-movements between various sectors are certain. The authors assist also in the birth of new dependence structure with the health-care industry in response to the COVID-19 crisis. This reflects the contagion occurrence from the health-care sector to other sectors.

By specifically examining the Islamic industry, this study sheds light on the resilience, challenges and opportunities within this sector, contributing novel perspectives to the broader discourse on pandemic-related impacts on economies and industries. Also, this paper conducts a comprehensive temporal analysis, examining the dynamics before, during and after the COVID-19 lockdown. Such approach enables an understanding of how the relationship between the health-care sector and the Islamic industry evolves over time, accounting for both short-term disruptions and long-term effects. By considering the pre-pandemic context, the paper adopts a longitudinal perspective, enabling a deeper understanding of how historical trends, structural factors and institutional frameworks shape the interplay between the health-care sector and the Islamic industry.

The purpose of this study is to examine the dynamic connectedness and volatility spillovers between commodities and corporations exhibiting the best environmental, social and governance (ESG) practices. In addition, the authors determine the optimal hedge ratios and portfolio weights for ESG and commodity investors and portfolio managers.

This study uses the novel frequency connectedness framework to point out volatility spillover between ESG indices covering the USA, developed and emerging markets and commodity indices, including energy (crude oil, natural gas and heating oil), industrial metals (aluminum, copper, zinc, nickel and lead) and precious metals (gold and silver) by using daily data between January 3, 2011 and May 26, 2021, covering significant socio-economic developments and the COVID-19 outbreak.

The results of this study suggest a total connectedness index at a mediocre level, mainly driven by the shocks creating uncertainty in the short term. And the results indicate that all ESG indices are net volatility transmitters, and all commodity indices other than crude oil and copper are net volatility receivers.

The results imply statistically significant hedging and portfolio diversification opportunities to investors and portfolio managers across the asset classes, proven by the hedging effectiveness analyses.

This study provides implications for policymakers focusing on the risk of contagion among the commodity and ESG markets during turbulent periods to ensure international financial stability.

This study contributes to the existing literature by differentiating ESG portfolios as the USA, developed and developing markets and examining dynamic connectedness and volatility spillovers between ESG portfolios and commodities with a different technique. This study also contributes by considering COVID-19 outbreak.

The purpose of this paper is to introduce a generalization of the time-varying correlation elliptical copula models and to analyse its impact on the tail risk of a portfolio of foreign currencies during the Covid-19 pandemic.

The authors consider a multivariate time series model where marginal dynamics are driven by an autoregressive moving average (ARMA)–Glosten-Jagannathan-Runkle–generalized autoregressive conditional heteroscedastic (GARCH) model, and the dependence structure among the residuals is given by an elliptical copula function. The correlation coefficient follows an autoregressive equation where the autoregressive coefficient is a function of the past values of the correlation. The model is applied to a portfolio of a couple of exchange rates, specifically US dollar–Japanese Yen and US dollar–Euro and compared with two alternative specifications of the correlation coefficient: constant and with autoregressive dynamics.

The use of the new model results in a more conservative evaluation of the tail risk of the portfolio measured by the value-at-risk and the expected shortfall suggesting a more prudential capital allocation policy.

The main contribution of the paper consists in the introduction of a time-varying correlation model where the past values of the correlation coefficient impact on the autoregressive structure.