The Governance and Disclosure of IFRS 9 Economic Scenarios

Extraordinary economic conditions during the COVID-19 pandemic caused many IFRS 9 impairment models to produce unreliable results. Severe market reactions, resulting from unprecedented events, prompted swift action from the regulatory authorities to maintain the financial system's stability. Banks managed the uncertainty and volatility in the models with expert overlays, increasing the risk of biased outcomes. This study examines new ways of enhancing the governance and transparency of the IFRS 9 economic scenarios within banks and suggests additional financial disclosures. Benchmarking is proposed as a useful tool to evaluate the IFRS 9 economic scenarios and ensure effective challenge as part of a model risk governance framework. Archimedean copulas are used to generate objective economic benchmarks. Ideas around benchmarking are illustrated for a set of South African economic variables, and the outcomes are compared to the IFRS 9 scenarios published by the six biggest South African banks in their annual financial statements during the pandemic.

COVID‐19 and tail risk contagion across commodity futures markets

This paper examines the impact of COVID‐19 on tail risk contagion across commodity futures markets using a copula‐based network method. We document a significant increase in the lower and upper tail contagiousness of commodities following the COVID‐19 outbreak. Contagion shows an obvious clustering characteristic, that is, there is higher tail risk connectedness between commodities in the same category. Agricultural commodities are significantly less contagious than metals and energy commodities;soft commodities in particular can offer investors significant diversification benefits. There are several hub commodities in the contagion network, chief among them copper, which are good transmitters of shocks and should be treated with caution by investors and regulators. Although tail risk and contagiousness of individual commodities increase together during the pandemic, we find a negative cross‐sectional relationship between tail risk and contagiousness, that is, commodities with high tail risk are not necessarily highly contagious and may even be less so.

COVID-19 and tail risk contagion across commodity futures markets

This paper examines the impact of COVID-19 on tail risk contagion across commodity futures markets using a copula-based network method. We document a significant increase in the lower and upper tail contagiousness of commodities following the COVID-19 outbreak. Contagion shows an obvious clustering characteristic, that is, there is higher tail risk connectedness between commodities in the same category. Agricultural commodities are significantly less contagious than metals and energy commodities;soft commodities in particular can offer investors significant diversification benefits. There are several hub commodities in the contagion network, chief among them copper, which are good transmitters of shocks and should be treated with caution by investors and regulators. Although tail risk and contagiousness of individual commodities increase together during the pandemic, we find a negative cross-sectional relationship between tail risk and contagiousness, that is, commodities with high tail risk are not necessarily highly contagious and may even be less so.

Tail-risk spillovers from China to G7 stock market returns during the COVID-19 outbreak: A market and sectoral analysis.

This study uses a combination of copulas and CoVaR to investigate risk spillovers from China to G7 countries before and during the COVID-19 pandemic. Using daily data on stock and equity sectors for the period from January 1, 2013 to June 9, 2021, the main empirical results show that, before the COVID-19 pandemic, stock markets were positively related and systemic risk was comparable for all countries. However, during the COVID-19 outbreak, the level of dependence increased for all G7 countries and the upside-downside risk spillovers become on average higher for all stock markets, with the exception of Japan. Our results also provide evidence of higher market risk exposure to information from China for the technology and energy sectors. Moreover, we find an asymmetric risk spillover from China to the G7 stock markets, with higher intensity in downside risk spillovers before and during COVID-19 spread.

Green Bonds and Commodities: A New Asymmetric Sustainable Relationship

In this study we examine the relationship between corporate green bonds and commodities (both perishable & non-perishable) that attracts very little attention in relative literature. For the first time, we investigate a long-term relationship between green bonds and commodities including a significantly higher number of commodities and observations. Furthermore, we adopt a novel methodology, the VaR (value at risk) based copulas, to describe the asymmetric risk spillover between green bonds and commodities by considering the asymmetric tail distribution. Our results reveal an insignificant risk spillover effect from commodity market uncertainty. Further, we found non-perishable commodities are transmitting risk to perishable commodities (barring lead). In addition, in contrast to other similar studies the risk spillover is comparatively higher regarding lead, gold, and agriculture commodities as against copper and silver. On the other hand, energy commodities have the least spillover effect. Finally, these results have several important implications for investors as well as for policymakers.

Interactive multi-criteria group decision-making with probabilistic linguistic information for emergency assistance of COVID-19.

This paper develops a new method for interactive multi-criteria group decision-making (MCGDM) with probabilistic linguistic information and applies to the emergency assistance area selection of COVID-19 for Wuhan. First, a new possibility degree for PLTSs is defined and a new possibility degree algorithm is devised to rank a series of probabilistic linguistic term sets (PLTSs). Second, some new operational laws of PLTSs based on the Archimedean copulas and co-copulas are defined. A generalized probabilistic linguistic Choquet (GPLC) operator and a generalized probabilistic linguistic hybrid Choquet (GPLHC) operator are developed and their desirable properties are discussed in details. Third, a tri-objective nonlinear programming model is constructed to determine the weights of DMs. This model is transformed into a linear programming model to solve. The fuzzy measures of criterion subsets are derived objectively by establishing a goal programming model. Fourth, using the probabilistic linguistic Gumbel weighted average (PLGWA) operator, the collective normalized decision matrix is obtained by aggregating all individual normalized decision matrices. The overall evaluation values of alternatives are derived by the probabilistic linguistic Gumbel hybrid Choquet (PLGHC) operator. The ranking order of alternatives is generated. Finally, an emergency assistance example is illustrated to validate the proposed method of this paper.