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The purpose of this paper is to propose an uncertain regression model with an intrinsic error structure facilitated by an uncertain canonical process.

This model is suitable for dealing with expert's knowledge ranging from small to medium size data of impreciseness. In order to have a rigorous mathematical treatment on the new regression model, this paper establishes a series of new uncertainty concepts sequentially, such as uncertainty joint multivariate distribution, the uncertainty distribution of uncertainty product variables and uncertain covariance and correlation based on the axiomatic uncertainty theoretical foundation. Two examples are given for illustrating a small data regression analysis.

The uncertain regression model is formulated and the estimation of the model coefficients is developed.

The paper is devoted to a regression model to handle a small amount of data with mathematical rigor.

The theory and the methodology of the uncertain canonical process regression is proposed for the first time. It addresses the practical challenges of small data size modelling.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

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 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.

The purpose of this study is to investigate the information asymmetry pricing (relation between information asymmetry and expected return) based on environmental uncertainty and accounting conservatism.

The current study applies panel regression method estimator to investigate the relationship between accounting conservatism, environmental uncertainty and information asymmetry pricing of 1,309 firm-year observations in Iran for the period 2008–2018.

The result indicated the negative relation between accounting conservation and information asymmetry pricing and documented a positive association between environmental uncertainty and information asymmetry pricing.

In the present study, the weaknesses caused by the ambiguity of capital market efficiency in market performance-based statistical models are compensated and partially covered by quantifying the relationships and implementing models in each quintile. Results obtained from this study will aid policymakers to evaluate disclosure rules and firms to manage their information. The study is based on the corporate accounting and financial literature and examines behavioral changes in information and its effect on information asymmetry pricing that can be applied to investors, managers, standardization committees and legislators.

The risk of accounting information in the context of the capital market environment can be divided into two parts: a part that is ambiguous about the accuracy of this information and another part that is a distribution of information. Unlike other research, information asymmetry pricing has also been addressed with regard to the origin and distribution of information. This study also considers the effect of information asymmetry and market constraints by considering the ability of financial reports to transmit firm information.

An empirical investigation examining the environmental uncertainty regarding inventory ordering which confronts a retailer in dealing with its suppliers is described. Of particular interest is how this uncertainty impacts on retailers' behavioural relationships with their suppliers. The findings indicate that increased levels of environmental uncertainty regarding inventory ordering result in higher levels of retailer‐supplier conflict. Suppliers that can offer retailers better customer service in order to reduce environmental uncertainty can improve their relations with retailers and thus develop a more efficient distribution system.

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key difference from traditional Bayesian variable selection is that we also allow for uncertainty regarding which endogenous variables to include in the model. That is, all models include the forecast variables, but may otherwise have differing sets of endogenous variables. This is a difficult problem to tackle with a traditional Bayesian approach. Our solution is to focus on the forecasting performance for the variables of interest and we construct model weights from the predictive likelihood of the forecast variables. The procedure is evaluated in a small simulation study and found to perform competitively in applications to real world data.

The assumption that a set of observed variables is a function of an underlying common factor plus some error has dominated measurement in marketing and the social sciences in general for decades. This view of measurement comes with assumptions, which, however, are rarely discussed in research. In this article, we question the legitimacy of several of these assumptions, arguing that (1) the common factor model is rarely correct in the population, (2) the common factor does not correspond to the quantity the researcher intends to measure, and (3) the measurement error does not fully capture the uncertainty associated with measurement. Our discussions call for a fundamental rethinking of measurement in the social sciences. Adapting an uncertainty-centric approach to measurement, which has become the norm in in the physical sciences, offers a means to address the limitations of current measurement practice in marketing.