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This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

The purpose of this paper is to develop a quantitative risk assessment method for agricultural products cold chain logistics to assess the condition of the fresh agricultural products cold chain process objectively and accurately.

A risk assessment index system of agricultural products cold chain logistics is designed on the basis of the risk identification for the process of agricultural products cold chain logistics. This paper first uses catastrophe progression method and a new maximum deviation method to build an improved catastrophe progression assessment model for agricultural products cold chain logistics. In order to verify the reliability and validity of the model, two representative enterprises are selected as the case in the study.

The results in the empirical research indicate strong support for the assessment model and coincide with the reality. The risk assessment index system can also reflect the key risk factors from agricultural products cold chain logistics scientifically. In addition, the improved catastrophe progression assessment method proposed in this paper can be scientific and reasonable to predict risk.

This paper contributes to provide a new risk assessment model for agricultural products cold chain logistics. The new model overcomes the limitation of subjective empowerment and it increases the objectivity and scientificity in the process of cold chain logistics risk assessment. This paper also shows that practitioners involved in the field of products cold chain logistics can manage the potential risk by a set of scientific methods for assessing the risk before the accident.

The paper provides a practical guideline to practitioners, especially for cold chain logistics managers, relevant management departments, and cold chain logistics management consultants. It is proved that the new risk assessment method and the risk assessment index system of agricultural products cold chain logistics can help them assess the risk scientifically and reasonably.

Although the calculation is simple, the new model can overcome the limitation of subjective empowerment scientifically and reasonably, and thus has important practical value.

This paper aims to focus on the relation between digital transformation and banks’ reputation, as examined through the information disclosed by the five largest Italian banking groups’ efforts to extend and enhance their digital resources. Considering digitalization as a key strategy for managing reputation, which, in turn, can leverage financial and value performance management, the paper investigates whether and how digital activities might affect banks’ reputation. Therefore, this paper proposes the relationship between digitalization and reputation as a lever for performance management and for increasing efficiency.

The authors use content analysis to generate a digital disclosure index, categorizing activities human, structural and relational. For banks’ reputations, the proxies are a measure of corporate reputation and a reputational risk index. Methodologically the study used multiple case studies, considered as particularly suitable to gain an in-depth understanding of the topic in the case of the five banks. A collection of secondary data and semi-structured interviews are included.

Overall, the digitalization-reputation link shows that banks’ reputation is variously affected, not only by exposure to risk (including reputational risk) but also by strategic issues such as digitalization and the effectiveness of the corresponding communication. Consequently, banks should view digitalization as a key driver to be considered not in a stand-alone perspective, but in a combined approach.

Continued research should include the Covid-19 implications. Additionally, it would be important to compare a larger number of banks, with different characteristics, also including variables indicating the corporate governance mechanisms.

The analysis contributes to fostering scholars’ and practitioners’ management of the digital transformation challenge that is a current key-factor, capable of increasing banks’ value. It considers not only the drivers directly affecting monetary value but also the institutions’ social and relational value, as well as their reputation.

This paper extends prior research on the digitalization-reputation relation by investigating digital transformation through disclosure of activities in this area within the Italian banking sector. It allows to leverage the key-factors that can contribute to increasing banks’ value, considering not only the drivers directly affecting monetary value but also the institutions’ social and relational value, as well as their reputation.

The outbreak and the spreading of the COVID-19 pandemic have impacted the global financial sector, including the alternative clean and renewable energy sector. This paper aims to assess the impact of the pandemic, COVID-19 on the stock market indices of the clean energy sector using quantile regression methods.

This study utilized daily data sets on the four major categories of stocks: (1) Morgan Stanley Capital International Global Alternative Energy Index, (2) WilderHill Clean Energy Index, (3) Renewable Energy Industrial Index (RENIXX) and (4) the S&P 500 Global Clean Index. The study adopts a multifactor capital asset pricing model.

Clean and alternative energy stocks are powerful instruments for diversification. However, the impact of the volatility index induced by infectious disease is negative and significant across quantiles.

For investors and policymakers, considering how the uncertainty caused by COVID-19 and the geopolitical index influences renewable energy markets is of great practical importance. For investors, it throws insights into portfolio diversification. For policy makers, it helps to devise strategies to reboot the economy along the lines of the deployment of renewables. This study sheds light on a global green-energy transition and has practical implications for renewable energy resilience in post-pandemic times.

This paper can be considered as a pioneer that explores the nexus between oil prices, interest rates, volatility index, and geopolitical risk upon the stock indices of clean and alternative sources of (renewable) energy in the COVID-19 pandemic situation. The results have important insights into the area of energy and policy decision-making. Additionally, the paper's novelty lies in using the explanatory variables associated with the Covid 19 pandemic.

The aim of the paper is to investigate the risk-hedging and/or safe haven properties of environmental, social and governance (ESG) index during the COVID-19 in China.

This paper employs the DCC, VCC, CCC as well as Newey–West estimator regression.

The findings provide empirical evidence of the risk hedging properties of ESG indexes as well as of the environmental, social and governance thematic indexes during the outbreak of the COVID-19 crisis. The results also support the superior risk hedging properties of ESG indexes over cryptocurrency. However, the authors do not find any safe haven properties of ESG, Bitcoin, gold and West Texas Intermediate (WTI).

The paper offers therefore, practical policy implications for asset managers, central bankers and investors suggesting the pandemic risk-hedging opportunities of ESG investments.

The study represents one of the first empirical contributions examining safe-haven and hedging properties of ESG indexes compared to traditional and innovative safe haven assets, during the eruption of the COVID-19 crisis.

This paper aims to investigate the pass-through (PT) effect in Turkey by using quarterly data for the period 1998: Q1-2019: Q2 to understand the dynamic potential effects of exchange rates on domestic prices.

The paper launches several nonlinear models in which the basic determinants of domestic prices in Turkey are determined through Markov regime-switching models (MSMs). Hence, this research follows the variables of the consumer price index (CPI), USD exchange rate, gross domestic product (GDP; demand side of the economy), industrial production index (production side of the economy), economic uncertainty and geopolitical risk index for Turkey.

This work explores that the exchange rate and demand side of the economy (GDP) follow a positive nonlinear relationship with CPI at both regimes. The production side of the economy (IP) affects negatively the CPI during regime 0. Economic uncertainty influences the CPI positively at Regime 1, while geopolitical risk has a negative association with CPI at Regime 0. Eventually, the paper provides some policy proposals associated with the impacts of GDP, IP, economic uncertainty and geopolitical risk on CPI in Turkey.

One may claim that any PT model, which does not observe the possible structural or regime shifts in estimated parameters, might fail to estimate the coefficients unbiasedly and efficiently. Hence, this work differs from available relevant works in the literature since this paper considers linearity or nonlinearity important and reveals that the relevant PT model follows a nonlinear path rather than a linear path, this nonlinear path is converged strongly by MSMs and estimates the significant regime shifts in the constant term and, in parameters of independent variables of PT by MSMs.

Across societies, gendered climate response decisions remain top-down and have limited progress because the influenced risk dynamics and their interrelations are not adequately understood. This study aims to address this gap by proposing an interdisciplinary innovative method, called women climate vulnerability (WCV) index, for measuring and comparing a diverse range of risks that threaten to undermine the adaptive capacity and resilience of rural women.

This paper builds on the literature to identify 12 risk categories across physical, economic and political sectors that affect rural women. These categories and attendant 51 risk indicators form the WCV index. A case study in Ben Tre Province (Vietnam) was used to demonstrate the application of the WCV methodology to rural contexts. The authors combined empirical, survey and secondary data from different sources to form data on the indicators. Structured expert judgment was used to address data gaps. Empirical and expert data were combined using a few weighting steps and a comprehensive coding system was developed to ensure objective evaluation.

The WCV assessment results reveal a reasonably worrisome picture of women’s vulnerability in Ben Tre as top highest-likelihood and deepest-impact risks predominate in physical and economic risk sectors. Stability, human security and governance categories have lowest scores, demonstrating a fairly politically favourable condition in the province. The medium risk scores captured in land and infrastructure categories reveal promising determinants of the adaptation of women in this rural province. The results demonstrate the usefulness of the WCV index in collecting bottom-up data, evaluating a wide variety of risks that rural women face and pinpointing priority areas that need to be addressed.

The WCV is systematic, customisable and localised. It combines field research and empirical data through structured expert judgment, thus enables researchers to fill data gaps and to do evidence-based assessment about diverse risk vulnerabilities. By doing so, the WCV index gives critical insights into the challenges that rural women face. This enables local governments to better understand cross-sectoral risks, pinpoint priority areas of action and timely channel funding and policy resources to support women where they need it most.

This research aims to evaluate the accuracy of several Value-at-Risk (VaR) approaches for determining the Minimum Capital Requirement (MCR) for Islamic stock markets during the pandemic health crisis.

This research evaluates the performance of numerous VaR models for computing the MCR for market risk in compliance with the Basel II and Basel II.5 guidelines for ten Islamic indices. Five models were applied—namely the RiskMetrics, Generalized Autoregressive Conditional Heteroskedasticity, denoted (GARCH), fractional integrated GARCH, denoted (FIGARCH), and SPLINE-GARCH approaches—under three innovations (normal (N), Student (St) and skewed-Student (Sk-t) and the extreme value theory (EVT).

The main findings of this empirical study reveal that (1) extreme value theory performs better for most indices during the market crisis and (2) VaR models under a normal distribution provide quite poor performance than models with fat-tailed innovations in terms of risk estimation.

Since the world is now undergoing the third wave of the COVID-19 pandemic, this study will not be able to assess performance of VaR models during the fourth wave of COVID-19.

The results suggest that the Islamic Financial Services Board (IFSB) should enhance market discipline mechanisms, while central banks and national authorities should harmonize their regulatory frameworks in line with Basel/IFSB reform agenda.

Previous studies focused on evaluating market risk models using non-Islamic indexes. However, this research uses the Islamic indexes to analyze the VaR forecasting models. Besides, they tested the accuracy of VaR models based on traditional GARCH models, whereas the authors introduce the Spline GARCH developed by Engle and Rangel (2008). Finally, most studies have focus on the period of 2007–2008 financial crisis, while the authors investigate the issue of market risk quantification for several Islamic market equity during the sanitary crisis of COVID-19.

This paper aims to consider the role of geopolitical risk in explaining tourism demand in India, a major tourist destination of the Asian region. Furthermore, the study also considers how in addition to geopolitical risk, economic policy uncertainty, economic growth, exchange rate, inflation and trade openness impact tourism demand.

The Bayer and Hanck (2013) method of cointegration is applied to explore the relationship between geopolitical risk and tourism demand. Furthermore, the study has also used the auto distributed lag model to determine whether there is a long-run cointegrating association between tourism demand, geopolitical risk, economic policy uncertainty, economic growth, exchange rate and trade openness. Finally, the vector error correction model confirms the direction of causality across the set of the major variables.

This paper finds that geopolitical risk adversely impacts inbound international travel to India. This study also obtains the consistency of the results across different estimation techniques controlling for important macro variables. The Granger causality test confirms the unidirectional causality from geopolitical risk to tourism and further from economic uncertainty to tourism. The findings from the study confirm that geopolitical risks have long-term repercussions on the tourism sector in India. The results indicate that there is an urgent need to develop a pre-crisis management plan to protect the aura of Indian tourism. The tourism business houses should develop skilful marketing strategies in the post-crisis to boost the confidence of the tourists.

This paper provides valuable practical implications to tourism business houses. The tourism business houses can explore geopolitical risk measure and economic policy uncertainty measure to analyse the demand for international tourism in India. Further, the major stakeholders can establish platforms to help tourists to overcome the fear associated with geopolitical risk.

This study is the first of its kind to explore the geopolitical risks and their long-run consequences in the context of tourism in India. The study puts emphasis on the role of national policy to maintain peace otherwise it would be detrimental to tourism.

In this paper, we analyze the hedging risk intrinsic in the auto call step down equity linked securities (ELS) based on two underlying indices including HSCEI, which are major products of the ELS market. And we also propose new hedging strategies based on Conditional Value at Risk (CVaR) using stocks portfolio and futures. Due to the non-symmetric bimodal distribution for return of ELS, which comes from the Knock-In (KI) property inherent in step down ELS structure, and inherent shortfall risk in the ELS structure, a local delta hedging strategy has a limit. In addition, hedging using futures are difficult because of 1) frequent roll-over related with HSCEI futures, 2) price difference between underlying index and futures and 3) lack of futures liquidity caused by excessive ELS issue based on HSCEI. As a way to manage these problems, this paper proposes new hedging strategies : First, construct stocks portfolio tracking index using method suggested by Rockafellar and Uryasev (2002), Alexander, Coleman and Li (2006). Second, do hedging by using this stocks portfolio and futures. This paper shows that 1) index-tracking stocks portfolio based on CVaR has a better performance and lower shortfall risk than index by comparing market ratio, information ratio and Sharpe Ratio, and 2) hedging using stocks portfolio is better than futures. As the policy proposals, if ETF, which tracks the underlying indices of ELS based on CVaR, is to be listed on the exchange (KRX), various kinds of product structures for mid-risk-mid-return structured products will be able to develop further, as well as offer more convenience with hedging.