Implications For Risk Management Research Articles

Economic Tremors From a Perfect Storm: The Ukrainian Crisis and Its Impact on Regional Stock Market Volatility

This study investigates the impact of the Russia-Ukraine war on stock market volatility in neighboring countries, analyzing five stock market indices (BET, BUX, WIG, SAX, and MOEX) over a three-year period encompassing one year before and two years after the conflict’s outbreak. Employing four volatility estimators (Close-to-Close, Parkinson, Garman-Klass, and Rogers-Satchell), this research examines the evolution of market volatility and inter-market correlations. Findings reveal a general increase in volatility across most indices following the war’s commencement, with the MOEX index experiencing the highest turbulence. The concept of a “proximity penalty” is partially supported, as geographical closeness to the conflict zone does not uniformly correspond to increased volatility. Also, findings show an initial strengthening of correlations between markets in the first year of the war, suggesting a “contagion effect.” However, this is followed by a weakening of correlations in the second year, indicating a potential “decoupling effect” as markets begin to respond more to local economic conditions. These results have significant implications for investors, policymakers, and risk managers, highlighting the need for dynamic portfolio management strategies, tailored policy responses, and flexible risk models that can adapt to changing market conditions during prolonged geopolitical crises. This study contributes to the existing literature by extending the temporal scope of analysis beyond the immediate aftermath of the war’s outbreak and providing insights into the “proximity effect” in the context of a major European conflict. The observed patterns of initial volatility spikes followed by varying degrees of persistence and changing correlation structures offer a nuanced picture of how geopolitical events impact financial markets over time, emphasizing the complex interplay between political events and financial market dynamics. JEL classification: G15, F51 Article History: Received: October 14, 2024; Reviewed: November 27, 2024; Accepted: December 2, 2024; Available online: December 17, 2024.

How difficult is it perceived to takeover in a level 3 automated vehicle? Investigation of the ease of use according to task and situational factors

One goal of automated driving is to mitigate risks by minimising human intervention. However, widespread acceptance of automated vehicles hinges on their perceived ease of use, particularly during takeover scenarios. This online survey investigates the perceived difficulty of takeover in Level 3 Automated Vehicles, focusing on factors influencing takeover performance such as duration of automated mode and traffic density, as well as Non-Driving-Related Tasks like listening to music or engaging in conversation. Using Anderson’s experimental protocol based on Integrated Information Theory, 235 drivers aged 18–72 rated takeover difficulty across 32 realistic scenarios varying in these factors. Results indicate significant impacts of all factors on takeover perception, identifying three distinct driver profiles: takeover averse, adaptive, and confident. Findings underscore implications for vehicle acceptance and risk management in automated driving.

Higher-order moments spillovers among energy, carbon and tourism markets: Time- and frequency-domain evidence.

This paper uses the GJRSK model to estimate the high-order moments of energy (oil, natural gas, and coal), the carbon market, and tourism stocks. Then, it utilizes a novel TVP-VAR time-frequency connectedness approach to examine higher-order moments spillovers among them. The results show a strong connectedness among the three markets. The energy market is the emitter of volatility, skewness and kurtosis spillovers; tourism stock is the receiver; and the carbon market is the transmitter. From a time-domain perspective, the higher-order moments spillovers of the three markets are time-varying, especially during extreme periods, where the energy market's spillover effects on tourism stocks are significantly enhanced, indicating that tourism stocks bear a greater risk at leptokurtosis and fat-tail moment. From a frequency-domain perspective, the long-term asymmetric spillovers of oil, natural gas, and tourism markets on the carbon market are more pronounced than the short-term. Moreover, the COVID-19 pandemic exacerbated the higher-moment spillovers of energy and tourism stocks on the carbon market. Meanwhile, the Russia-Ukraine conflict led to extreme risk transmission within the energy market. These findings have significant implications for cross-industry investors and green finance risk management.

Assessing climate change threats and urbanization impacts on surface runoff in Gdańsk (Poland): insights from remote sensing, machine learning and hydrological modeling

This study investigates the impacts of Land Use/Land Cover (LULC) changes and climate change on surface runoff in Gdańsk, Poland, which is crucial for local LULC planning and urban flood risk management. The analysis employs two primary methodologies: remote sensing and hydrological modeling. Remote sensing was conducted using Google Earth Engine and Land Change Modeler in IDRISI Terrset software to analyze historical (1985–2022) and future (2050–2100) LULC. Hydrological modeling was performed using the Natural Resources Conservation Service curve number method to assess the overall impact of LULC changes on Gdańsk’s hydrology at the local scale. The Orunia basin, a critical area due to intensive LULC development, was selected for detailed hydrological analysis using the Hydrologic Modeling System (HEC-HMS). The analysis encompassed three scenarios: LULC changes, climate change, and combined LULC and climate change effects. The LULC analysis revealed a marked increase in urban area, a shift in forest and vegetation cover, and a reduction in agricultural land. HEC-HMS simulations showed an increase in the runoff coefficient across selected decades, which was attributed to the combined effect of LULC and climate change. The projected increases under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios for 2050 and 2100 are projected to surpass those observed during the baseline period. The findings highlight that the synergistic effects of LULC and climate change have a more significant impact on Gdańsk’s hydrology at both local and basin scales than their separate effects. These insights into LULC shifts and urban hydrological responses hold implications for sustainable urban planning and effective flood risk management in Gdańsk and similar urban settings.

Impact of Acid Rain on Release Characteristics of Heavy Metals in Low-Sulfur Tailings with Strong Acid Neutralization Capacity: A Case Study from Northern Guangxi, China

Tailing ponds are major sources of heavy metal pollution. Previous studies primarily focused on tailings with high sulfur content, with limited attention to low-sulfur tailings. This study explored the release behavior of Pb, Zn, and Cd from low-sulfur tailings under simulated acid rain conditions, considering factors such as pH, particle size, and weathering degree. Samples were collected from a lead–zinc tailing pond in the karst regions of northern Guangxi, China. Batch leaching experiments indicated that even with high acid neutralization capacity (ANC = 166.57–167.45 kg H2SO4/t), substantial heavy metal leaching occurred under acidic conditions (pH 2–3), with Pb, Zn, and Cd concentrations increasing 4–6 times compared to neutral conditions. Leachate concentrations were slightly higher in coarser particles than in finer ones, while weathering further enhanced metal release, particularly for Cd. These findings suggest that acid neutralization alone may not be sufficient to prevent heavy metal leaching in low-sulfur tailings exposed to acid rain. However, due to the laboratory scale of this study, further validation through field-scale or mesocosm experiments is necessary to confirm the observed trends and assess their implications for environmental risk management in karst regions.

Co-occurrence and correlation of arsenic, cadmium, and lead in processed foods intended for infants and young children: evaluation of two statistical approaches adapted to censored data and their implications for risk management

We evaluated the co-occurrence of arsenic (As), cadmium (Cd), and lead (Pb) in 10 categories of processed foods intended for infants and young children (<2 years old) and adapted methodology to characterise and evaluate the impact of correlations on toxic element (TE) concentrations in these foods. Co-occurrence was assessed by calculating the frequency of samples having concentrations above the limit of detection (LOD) among TE sets (i.e. As-Cd, As-Pb, Cd-Pb, and As-Cd-Pb). Pairwise correlations were evaluated using two statistical approaches adapted to censored data: (i) non-parametric Kendall’s tau and (ii) parametric Bayesian modelling. Nonparametric and parametric correlation analyses showed similar results. We found positive correlations among one or more pairs of the TEs (As, Pb, and Cd) in 9 of the 10 categories of foods intended for infants and young children and a negative correlation for one TE pair for a single food category. Where positive correlations are observed, removing samples with high concentrations of a given TE reduces the mean of the other TEs in that food. We also explored the impact of correlations among TEs on the fraction of the supply below maximum levels. Positive correlations among TEs have the potential to reduce the impact on the supply when setting multiple maximum levels.

Private social media usage of employees: Implications for corporate risk management to protect corporate reputation

In an increasingly complex business environment, risk management has become essential for corporate survival. Given the rapidly changing environmental conditions and trends, new risks are constantly emerging and need to be assessed and managed. The influence of risks extends beyond the corporate sphere, for example, with the private use of social media by employees and the resulting potential damage to the company’s reputation. According to the literature, there is little to no knowledge on this topic. Therefore, in this study, 20 experts at different positions in their companies were interviewed about the significance, handling, mapping, consideration, and measurement of potential reputational risks associated with the private use of social media by employees. The results show that there is a certain awareness of the problem, but only a few mechanisms have been implemented to manage such risks. Based on the findings, risk management measures are identified by examining the employees’ journey, which can be used in operational practice to reduce the risk of potential reputational damage.

Enhancing the performance of runoff prediction in data-scarce hydrological domains using advanced transfer learning

Accurate hydrological predictions are often hindered by the lack of stream gauges in data-scarce regions, where traditional transfer learning (TL) models like Long Short-Term Memory (LSTM) networks often face limitations due to reduced accuracy and adaptability. To enhance runoff prediction in such regions, we developed DAformer, a novel TL approach that integrates domain adversarial neural networks with the Informer model. Trained on comprehensive runoff data from U.S. basins, DAformer was applied to three basins in Chile and the Chaersen basin in China, demonstrating an effective transfer from data-rich to data-scarce environments. Results show that DAformer significantly outperforms LSTM-based models, improving forecast accuracy by 16.1% for 1-day lead time and by 100.5% for 5-day lead time. These improvements indicate that the DAformer model not only enhances prediction accuracy but also holds substantial practical implications for flood risk management and water resource planning in regions with limited data availability. By clustering basins based on Shuttle Radar Topography Mission (SRTM) and other geographical data, we found that relying on multiple source basins further enhances the performance. DAformer, therefore, serves as a robust and scalable method for enhancing runoff prediction for regions with limited data.

Forecasting the volatility of crude oil futures: New evidence from jump-induced volatility

This paper proposes an augmented heterogenous autoregressive (HAR) model with time-varying jumps to forecast the realized volatility (RV) of crude oil futures. Jump-induced volatility of crude oil futures is obtained from a GARCH-jump process, then used to augment the HAR model. The results based on both the in-sample and out-of-sample analyses suggest that jumps offer added information for forecasting the RV of crude oil futures, surpassing the incremental information contained in the crude oil implied volatility index (OVX). Various robustness tests confirm these findings. Our findings have key implications for energy market investors, risk managers, and policymakers.

Estimating the predictability limit of meteorological variables using permutation entropy

Understanding and quantifying the predictability of various meteorological variables remains a significant challenge in atmospheric sciences, particularly in regions with complex climates. This study aims to assess the predictability limit of wind speed, pressure, temperature, and relative humidity using permutation entropy (PermEn) analysis. By analyzing the statistical properties of PermEn values associated with each variable, our results revealed distinctive patterns of complexity and predictability. It shows clear evidence that pressure generally exhibits the lowest PermEn value, while wind speed tends to have the largest PermEn. However, the variation of PermEn values remains largely stable across different terrain conditions, which is primarily attribute to the use of daily observational data. The study also discusses the impact of window length on PermEn calculations, highlighting that longer window lengths are more likely to capture a broader range of dynamics, including more intricate and diverse temporal structures. This expanded pattern diversity leads to increased complexity of the time series, resulting in higher PermEn values. Additionally, the seasonal variation patterns of permutation entropy were found to differ among the meteorological variables considered. These findings provide valuable insight into the relative predictability of various meteorological variables, which can have important implications for atmospheric modeling, weather forecasting, and risk management.

Dynamic response of flood risk in urban-township complex to future uncertainty

Flooding has emerged as a critical global issue exacerbated by climate change and urban development. This study addresses the significant spatial heterogeneity in flood severity along the urban-rural gradient, which is influenced by variations in ecohydrological and geochemical structures. Despite the importance of understanding the dynamic evolution of flood risk under different disaster scenarios, its influence by urban-rural gradients and response to future uncertainty remain inadequately explored. By developing a comprehensive vulnerability and hazard index framework integrated with a hydrodynamic model, this study not only simulated the dynamic evolution of Urban Vulnerability Value (UV) and Township Hazard Value (TH) during heavy rainfall events but also highlighted their wider implications for flood risk management under future uncertainty. The findings revealed that urban vulnerability exhibited a stable and concentrated distribution, while township hazard values demonstrated significant sensitivity and variability. Notably, the study underscored that under future uncertainty, flood risk responses are likely to intensify (UV and TH increasing by 2.4 and 2.7 times, respectively), with township areas becoming more sensitive to flooding. These insights contributed to a deeper understanding of how urban-rural gradient influences dynamic flood risk, offering valuable guidance for effective flood management strategies in similar regions.

Evaluation of WRF model performance with different microphysics schemes for extreme rainfall prediction in Lagos, Nigeria: Implications for urban flood risk management

In Nigeria, particularly in urban areas like Lagos, flooding is a frequent natural hazard. In 2011, Lagos experienced one of its worst floods resulting in significant economic losses and displacement of people. In recent years, Lagos has continued to grapple with flooding challenges, with an equally significant flood episode occurring in 2021. This study focuses on predicting floods and forecasting extremely heavy rainfall in West Africa's equatorial zone using the Weather Research and Forecasting (WRF) model, particularly in humid tropical environments like Lagos. The study discusses the need to review existing flood models and adopt alternative flood models to address the limitations of flood prediction. As potential causes of these rainfall episodes, the interconnections between synoptic systems such as tropical easterly waves, southwesterly winds related to the West African Monsoon, and local topography and oceanic conditions are investigated. Three key metrics: root mean square error (RMSE), mean bias (MB), and mean absolute error (MAE) are used to assess the effectiveness of the computational model. Results indicate that the WRF model, specifically when using the Thompson parameterisation, can estimate the amount of rainfall accumulated over a 24-h period. This suggests that the model can predict the size of daily precipitation during intense rain events. The Thompson scheme shows better performance compared to the WSM6 scheme while evaluating the stations and episodes. During the rainfall episode on July 10, 2011, Thompson's spatial rainfall predictions were better than WSM6, resulting in a decrease in root mean square error (RMSE) of 15–31% depending on the area. Simulations of the July 2021 episode also show better performance, with a decrease in RMSE of 11–25% when comparing Thompson to WSM6 scheme. The Thompson scheme’s improved ability is directly linked to a more accurate depiction of the microphysical mechanisms that control the rainfall formation. By explicitly simulating the dynamics of ice crystals and graupel, it is possible to accurately replicate the processes of orographic lifting and moist convection that are responsible for driving intense monsoon precipitation. In addition, Thompson scheme shows a reduced degree of systemic bias in comparison to WSM6, with a 75% reduction in the average bias in rainfall accumulation over the research area. The combination of the advanced Thompson microphysics method and WRF's atmospheric dynamics shows a high level of accuracy in predicting intense rainfall and the risk of floods in this area with diverse tropical topography.

Convolution Bounds on Quantile Aggregation

Advancing Risk Assessment: New Ways To Compute Quantile Aggregation This issue features a pivotal study on quantile aggregation amid dependence uncertainty, an area critical to finance, risk management, and statistics. The authors introduce “convolution bounds,” derived from a recent inf-convolution formula of quantiles and related risk measures. The obtained analytical tools unify existing results and enhance the understanding of quantile methods by providing general, sharp, and computationally efficient solutions. The results offer insights into the extremal dependence structures, with several implications in risk management and economic analysis applications. For more detailed insights, read the full paper, “Convolution Bounds on Quantile Aggregation” (reference: [insert reference]).

The impact of the Russia-Ukraine conflict on market volatility: stability of Islamic cryptocurrency

Purpose - This research investigates how the Russia-Ukraine conflict affects the volatility of cryptocurrencies, with a specific focus on the comparative stability of Islamic gold-backed cryptocurrencies versus conventional cryptocurrencies, such as Bitcoin.Method - Utilizing the GARCH model, this study examines the risk factors and volatility transmission among cryptocurrencies (Bitcoin and Tether-gold), traditional financial markets (gold and stock markets), and their interrelationships during the conflict period. The study employs the daily closing prices of Bitcoin, Tether-gold, gold, the SP 500, and the Dow Jones Islamic Market Index from February 7, 2020, to November 30, 2023.Result - Bitcoin experienced significant volatility during the conflict, while the Tether-gold remained more stable. Islamic gold-backed cryptocurrencies have proven to be more stable than conventional ones during geopolitical crises.Implication - The findings offer valuable insights for investors seeking safe-haven assets during periods of economic uncertainty. Gold-backed cryptocurrencies present a more stable investment option compared to conventional cryptocurrencies, especially for investors adhering to Shariah principles.Originality - This research highlights the stability of Islamic gold-backed cryptocurrencies during geopolitical events, contributing to the understanding of safe-haven assets and offering practical implications for portfolio diversification and risk management.

Digitalize and stabilize: corporate digital transformation and performance volatility

ABSTRACT Digital transformation has emerged worldwide as a defining element of corporate strategies, driven by the rapid evolution of technologies. Understanding how digital transformation impacts firm performance and uncovering its underlying mechanisms has become essential for both theoretical exploration and practical application. This study investigates the impact of corporate digital transformation on performance volatility. We find that digitalization significantly attenuates firm performance fluctuations. This effect is primarily attributable to the synergistic interplay of strategic guidance, organizational empowerment, environmental support, and digital achievement. We show that digital transformation enhances performance stability by reducing information asymmetry between firms and their stakeholders. Our results indicate that these stabilizing effects are particularly pronounced in state-owned enterprises. The results are robust after addressing endogeneity concerns. This research contributes to the discourse on digital transformation strategies and their implications for corporate performance and risk management in emerging markets.

Sherpas and Sagarmatha: complexity theory, ‘Edge of Chaos’ and implications for tourism risk management

ABSTRACT Sagarmatha (Everest) has long been recognised as one of the principal goals of global adventure tourism. Supported by high-altitude workers, paying climbers on Sagarmatha have the opportunity to tread in the footsteps of colonial pioneers such as George Mallory and Sir Edmund Hillary. Ever since the first attempt was made to summit Sagarmatha there has been an appreciation amongst the climbing community of the risks involved. However, approaches to risk management on Sagarmatha have often not considered the social and cultural circumstances around a climbing expedition’s relationship to its local support staff. This paper uses the notion of an ‘edge of chaos’ to explore the evolving relationship between climbers and other stakeholders on Sagarmatha. Then, through reference to Holling's (2001) adaptive cycle and concept of panarchy, we consider on what basis might one stakeholder on Sagarmatha impose a greater risk burden on another stakeholder.

Exploring the nexus of Information Systems Management and strategic planning: a bibliometric perspective using the Web of Science database

In modern organizational studies, the incorporation of Information Systems (IS) and strategic planning has gained prominence. This hybrid literature review seeks to explore the literature on such incorporation and its implications for risk management, business continuity, and organizational development. First, keyword co-occurrence analysis was conducted on 105 SJR-ranked journal articles sampled from the Web of Science database. This study identified and analyzed the major research areas within the domain. Through content analysis, after refining our sample by applying bibliographic coupling of the most globally cited contributions, our sample was restricted to a selection of 32 articles, with all the articles matching or exceeding a threshold of at least 20 total global citations (TGCs). Our review indicated that research on the integration of IS and strategic planning focuses more on the factors affecting the adoption and continuance of these practices, while most papers contributing to risk management and organizational development focus on a discussion of the theoretical framework that underlies their implementation. Moreover, the review has signaled a constant number of challenges relative to this integration, especially concerning perceived effectiveness and the differential effects of cultural and generational variables on its practice in organizational settings.

Risk Retention and Management Implications of Medical Malpractice in the Italian Health Service

This work provides an economic exploration of the multifaceted world of medical malpractice risk. Third party liability insurance plays a central role in protecting healthcare providers and public care institutions from the financial consequences of medical malpractice claims, although in recent years, the industry landscape has been characterised by periods of distress for this type of protection, with rising litigations and reimbursement costs, resulting in a peculiarly complex market. For the Italian context, the study focuses on the financial repercussions for healthcare institutions of the growing trend towards risk retention practises, legally empowered by the introduction of Law No. 24/2017. The analysis employs Generalised Linear Models for the regressive approach to incorporate the structural and organisational characteristics of hospitals and uses quantitative simulations to explore different scenarios at a regional aggregate level. Due to the limited existing literature and data on the topic, this research aims to provide new methods for effectively understanding and managing this type of risk, thereby supporting decision-making processes in the healthcare sector.

Detection of Volcanic Ash Using Goes-16 Satellite Imagery: Case Study of Popocatépetl Volcano, Mexico

Objective: The objective of this study is to investigate the accuracy of volcanic ash detection using satellite imagery, to improve existing monitoring tools to adapt them to the Popocatépetl volcano in Mexico. Theoretical Framework: Volcanic ash detection via remote sensing employs Pavolonis' algorithms that compare radiances in the thermal and mid-infrared spectrum. This approach differentiates ash from clouds and water vapor by leveraging its differential absorption. Satellites such as GOES-16, which capture multispectral data, optimize the continuous detection and monitoring of ash dispersion, even in areas of low concentration. Method: Three volcanic ash detection algorithms were compared using images from the Popocatépetl eruption event on May 21, 2023: two developed by Pavolonis, which combine thermal and mid-infrared bands, and a third, a spectral adaptation designed to enhance precision at the edges of the ash cloud, tailored to the study area. Results were validated with an RGB ash image provided by NOAA. Results and Discussion: Pavolonis' algorithms provided a solid foundation for general detection, but the third algorithm, specifically designed for this study, significantly improved edge detection where classification conditions are more complex. This improvement was reflected in a higher agreement with the RGB ash image used as a reference. However, limitations related to atmospheric interference were identified, which require further adjustments in low ash concentration scenarios. Research Implications: The findings of this research have significant practical and theoretical implications for volcanic risk management and environmental protection. Enhanced accuracy in volcanic ash detection can optimize monitoring and early warning strategies, reducing risks to public health and infrastructure. Originality/Value: This study contributes to the literature by introducing a specific spectral adaptation to improve edge detection of volcanic ash, a novel approach not previously addressed. The research's relevance lies in its ability to refine monitoring methodology, providing more effective tools for volcanic risk management and offering a model applicable to other areas facing similar remote sensing challenges.

The impact of dissolved organic nitrogen (DON) retention in the vadose zone on nitrogen leaching losses

Leaching of dissolved organic nitrogen (DON) is a significant pathway for nitrogen (N) loss in agricultural ecosystems. The excessive application of N for enhanceing agricultural productivity often results in the leaching of N into groundwater. Yet not well understood, the extent of retention in the vadose zone has critical implications for risk management and remediation strategies. This study aims to advance simulation techniques for modelling the transport process of reactive DON within a heterogeneous vadose zone. Through a combination of laboratory experiments and numerical simulations, the study firstly examines the extent of DON retention in the vadose zone and quantitatively analyse groundwater contamination risk from this kind of accumulation. Our findings indicate that heavy N fertilizer application and high-intensity rainfall events led to elevated contents of DON in the vadose zone and increased DON leaching fluxes into groundwater. Besides, intensifier rainfall reduced the N storage more quickly in scenarios devoid of DON application with higher mineralization rate, while DON slowly mineralized to other forms, largely accumulated in the top layer and migrated deeper with intensifier rainfall after input of urea. In our scenarios, DON accounted for a substantial portion (33–68%) of the total dissolved nitrogen (TDN) leaching fluxes, with exogenous DON content contributing significantly (25–85%) to the overall DON leaching into the aquifer. These results underscore the need for effective strategies to mitigate groundwater contamination risks associated with agricultural N use.