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This paper aims to capture tail dependence between sentiment index and Shanghai composite index (SCI) by proposing a sentiment index based on text mining.

Online text mining and the Copula model were used in this study.

First, the paper finds herding effect in the expression of investors’ sentiment from online text data, and the usage occurrence frequency of most vocabulary is less correlative with SCI. Second, given these two features, the paper uses weighted divide-and-conquer algorithm to construct a sentiment index. Finally, because of multivariate non-Gaussian joint distribution between them, the paper uses the Copula model to detect their tail dependences, and finds that both upper and lower tail dependences could have a significant influence between positive sentiment and SCI, with a higher probability on the upper one. Additionally, only the upper tail dependence exhibits the significant influence between negative sentiment and SCI.

This paper proposes a framework of constructing investment sentiment index with the weighted conquer-and-divide algorithm, and characterizes tail dependence between sentiment index and SCI. The implication can measure the environment of investment market of China and provide an empirical ground for bandwagon effect and bargain shopper effect.

This paper examines the extreme dependence and asymmetric risk spillovers between crude oil futures and ten US stock sector indices (consumer discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, telecommunication and utilities) before and during COVID-19 outbreak. This study is based on the rationale that stock sectors exhibit heterogeneity in their response to oil prices depending on whether they are classified as oil-intensive or non-oil-intensive sectors and the possible time variation in the dependence and risk spillover effects.

The authors employ static and dynamic symmetric and asymmetric copula models as well as Conditional Value at Risk (VaR) (CoVaR). Finally, they use robustness tests to validate their results.

Before the COVID-19 pandemic, crude oil returns showed an asymmetric tail dependence with all stock sector returns, except health care and industrials (materials), where an average (symmetric tail) dependence is identified. During the COVID-19 pandemic, crude oil returns exhibit a lower tail dependency with the returns of all stock sectors, except financials and consumer discretionary. Furthermore, there is evidence of downside and upside risk asymmetric spillovers from crude oil to stock sectors and vice versa. Finally, the risk spillovers from stock sectors to crude oil are higher than those from crude oil to stock sectors, and they significantly increase during the pandemic.

There is heterogeneity in the linkages and the asymmetric bidirectional systemic risk between crude oil and US economic sectors during bearish and bullish market conditions; this study is the first to investigate the average and extreme tail dependence and asymmetric spillovers between crude oil and US stock sectors.

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 purpose of this paper is to examine the dependence structure between the US conventional stock market and each Islamic and conventional stock market provided by the Dow Jones index, namely, for the UK, Canada, Europe, the emerging countries and Asia-Pacific. This paper considers both the bearish and bullish market phases of the 2008 global financial crisis to analyze the financial contagion.

The authors implement the copula framework-based GJR-GARCH-t model for the period from December 31, 2004 to September 30, 2016.

The marginal models suggest a strong persistence of volatility in all stock markets. The dependence structure for stock market pairs under-consideration is not all strictly symmetrical. Moreover, the Islamic stock markets witness the same behavior as their conventional counterparts. Finally, the resilience and the decoupling hypotheses are not all around upheld by the empirical proof.

The findings of this paper are very important for global investors in their risk management during extreme market events. As the Sukuk is considered as a safe haven during crisis episodes, the investors are invited to take it into account for further portfolio diversification.

Risk management has been crucial to investors and regulators for pursuing market diversification opportunities and developing strategies to ensure market stability. This study examines the dependence structures of volatility, related to co-movements and macroeconomic effects, among Latin American stock markets and the risk–return spectrum benefits in the Latin American market using time-varying returns and volatility forecasts within a multivariate structure.

The sample comprised the largest stock markets in Latin America during the period from January 2000 to December 2017 and copulas and multivariate models were applied.

The results indicated that the copula with the best fit for modeling the dependence structure of the markets was symmetric Joe-Clayton with time-varying parameters. The dependence volatility structure was higher in the positive (upper tail) than in the negative (lower tail) returns, which may indicate that the Latin American markets had diversification benefits during downturns. Evidence of market coupling was found during times of the global crisis (subprime crisis) in Latin America. The presence of monetary and temporal effects over the dependence structures suggests that investors may obtain gains in a multivariate structure with copula distributions.

The findings will be of interest to researchers and practitioners for several reasons. First, this study contributes to the growing literature on the relationship between market dependence and volatility. Second, it indicates that the Latin American markets may present diversification advantages during downturns. Third, it informs the influence of macroeconomic effects on Latin American markets. The models that included the nonnormal and asymmetric characteristics of the financial market yielded better results in terms of less information loss and data adherence.

The design and pricing of weather‐based insurance instruments is strongly based on an implicit assumption that the dependence structure between crop yields and weather variables remains unchanged over time. The purpose of this paper is to verify this critical assumption by employing historical time series of weather and farm yields from a semi‐arid region.

The analysis employs two different approaches to measure dependence in multivariate distributions – the regression analysis and copula approach. The estimations are done by employing Bayesian hierarchical model.

The paper reveals statistically significant temporal changes in the joint distribution of weather variables and wheat yields for grain‐producing farms in Kazakhstan over the period from 1961 to 2003.

By questioning its basic assumption the paper draws attention to serious limitations in the current methodology of the weather‐based insurance design.

The empirical results obtained indicate that the relationship between weather and crop yields is not fixed and can change over time. Accordingly, greater effort is required to capture potential temporal changes in the weather‐yield‐relationship and to consider them while developing and rating weather‐based insurance instruments.

The estimation of selected copula and regression models has been done by employing Bayesian hierarchical models.

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.

The purpose of this paper is to propose that simple measures of linear association are unable to capture accurately the dependence between the survival of hedge funds and funds of funds, respectively. The paper then aims to advocate the use of copulas to model the joint survival of hedge funds and funds of funds managed by the same manager.

The paper uses both a one‐step approach where the margins and the copula parameters are estimated jointly, and a two‐step approach where the margins are fitted first and the copula parameter is estimated thereafter given the fixed margins. The margins are estimated non‐parametrically, semi‐parametrically, and parametrically, respectively.

First, the paper finds that Kendall's tau and Spearman's rho are anywhere between three and eight times larger than the corresponding sample based measures when various families of copulas are employed. Second, additional tests show that the two survival functions are strongly dependent, with the degree of nonlinear association increasing in the lower left quadrant.

This is the first paper to use copulas to model the joint survival of hedge funds and funds of funds. The results highlight the asymmetric dependence between hedge funds and funds of funds, which has implications for risk management practices.

The aim of this paper is to measure and evaluate the relationship between returns-volatility and trading volume and returns and volatility of financial market indexes using time-varying copulas.

The time dynamic dependence parameter is allowed to evolve according to a restricted ARMA-type equation which includes a constant term that is driven by a hidden two-state first-order Markov chain.

In using this time dynamics in conjunction with non-elliptical distribution functions and tail dependence measure, the authors are allowing for (and focusing on) non-linearities in the returns-volume-volatility relationship. The results support the assumption that current trading volume provides information about future volatility as well as that there is a negative relationship between returns and their volatilities in financial market indexes.

The authors provide an interesting empirical interpretation for the regimes the authors have identified: in the high dependence regime the sequential information arrival hypothesis and/or noise trading hypothesis are valid, consequently future volatility prediction is possible and persistent but does not last indefinitely; in the low dependence regime, the future volatility prediction is more unlikely to occur, since both trading volume and return negatives have a low (near zero) relation with future volatility.

This paper aims to statistically model the serial dependence in the first and second moments of a univariate time series using copulas, bridging the gap between theory and applications, which are the focus of risk managers.

The appealing feature of the method is that it captures not just the linear form of dependence (a job usually accomplished by ARIMA linear models), but also the non‐linear ones, including tail dependence, the dependence occurring only among extreme values. In addition it investigates the changes in the mean modeling after whitening the data through the application of GARCH type filters. A total 62 US stocks are selected to illustrate the methodologies.

The copula based results corroborate empirical evidences on the existence of linear and non‐linear dependence at the mean and at the volatility levels, and contributes to practice by providing yet a simple but powerful method for capturing the dynamics in a time series. Applications may follow and include VaR calculation, simulations based derivatives pricing, and asset allocation decisions. The authors recall that the literature is still inconclusive as to the most appropriate value‐at‐risk computing approach, which seems to be a data dependent decision.

This paper uses a conditional copula approach for modeling the time dependence in the mean and variance of a univariate time series.