Dynamic Risk Management Research Articles

Dynamic supply chain risk management plans for mitigating the impacts of the COVID-19 pandemic

COVID-19 pandemic prompted supply chain (SC) disruptions and heightened demand for crucial items like facemasks and ventilators. Lockdowns and border closures hindered raw material supply and manufacturing capacity expansion. Consequently, manufacturers faced challenges in inventory, transport, and delivery, resulting in higher shortage costs, elevated SC expenses, and reduced SC efficacy. Using an integrated agent-based model (ABM) and optimisation, this paper examines COVID-19's multifaceted impacts on facemask SCs. It assesses four primary resilience strategies: enhancing manufacturing capacity, improving raw material supply, increasing transportation and distribution facilities, and maintaining dynamic inventory policy. Moreover, the model tested the proposed strategies under different scenarios by optimising the inventory policy and transportation strategies, leading to improved facemask production and delivery during extreme events. Our study found that increased production capacity through an optimal inventory and transportation strategy for a long period reduced the multiple impacts of the pandemic on facemask SCs, resulting in diminished total SC costs and increased consumer access to finished products. Based on demand forecasts, maintaining dynamically optimal reordering points and order up to levels can help maximise raw material supply and inventory levels, thereby minimising risks. Using these findings, future risks related to outbreaks and pandemics can be more effectively planned.

Efficiency of risk management for tunnel security of megaprojects construction in China based on system dynamics

ABSTRACT There have been repeated security incidents in megaproject tunnels, construction security issues are particularly serious, and the management of security risks is receiving increasing attention. This study was conducted to improve the efficiency of tunnel security risk management, the security risk management process was adopted as an entry point, based on the Observe-Orient-Decision-Action (OODA) model, the influencing factors index system for the efficiency of security risk management in megaproject tunnels from four dimensions: observe, orient, decision and action. The AHP-EWM method is applied to assign index weights to model the system dynamics of security risk management efficiency. The Vensim software was used to simulate and analyse the effect of different indices on the efficiency of security risk management under different conditions, which provides a reference for improving the efficiency of security risk management. The results of the study show that building a comprehensive and uninterrupted risk information awareness network, real-time adaptation of risk control strategies based on the complexity of risks, and shortening risk management delays can effectively increase the efficiency of dynamic security risk management. This provides a theoretical and practical reference for effectively improving the efficiency of risk management for tunnel security in megaproject construction.

BIM-based digital platform and risk management system for mountain tunnel construction

During the construction of mountain tunnels, there are often various intricate and mutable potential hazards, the management and control of which are crucial to ensuring the safety of such construction. With the rapid advancement of engineering information technologies, including Building Information Model (BIM), the internet, big data, and cloud computing, dynamic management of mountain tunnel construction will inevitably become a prevailing trend. This paper proposes a new digital approach to realize the informatization and visualization of risk management in mountain tunnel construction, by combining monitoring measurement with advanced geological prediction based on BIM technology. The proposed approach suggests a BIM-based digital platform architecture for mountain tunnel construction, which is comprised of five layers—basic, model, data, application, and user. The integration of these five layers can realize risk management information during the construction of mountain tunnels. In addition, a set of dynamic risk management systems, including risk monitoring, identification, and assessment, can be established based on the digital platform. The digital platform and dynamic risk management system proposed in this paper have certain advantages in the construction of mountain tunnels, providing a new and significant way for the management of safety risks in such construction projects.

Dynamic Carbon Emission Reduction, Investment, and Risk Management

Dynamic Carbon Emission Reduction, Investment, and Risk Management

Research on crowd dynamic risk management based on the psychological stress perception function

Number of people, density and so on are often used as a risk indicator in crowd dynamic risk management, but crowd size cannot truly reflect the change process of psychological stress perception that leads to crowd behavior disorder. Therefore, this research calculates risk index thresholds from the perspective of psychological stress perception to help managers assess crowd-gathering risks. On the one hand, the behavior model and psychological stress perception model are constructed based on corresponding experiments at the micro layer. On the other hand, risk decision models, including the average density and phubbers ratio, are built at the macro level. Finally, a long subway transfer channel was used as a simulation scenario, and the critical value of the decision quantity was determined by taking the psychological stress perception of the crowd as the objective function. The simulation shows that the perceived pressure of the crowd changes nonlinearly with time. The simulation result of the critical threshold can be divided into four zones, namely, the safety area, warning area, disposal area and dangerous area, so that managers can take targeted measures. The results are validated by applying psychological stress perception to practical population risk management.

A dynamic individual risk management method considering spatial and temporal synergistic effect of toxic substance leakage and fire accidents

Actual chemical industrial park accidents usually involve multi-hazard accidents. Synergistic effect of accidents could lead to serious casualties. In order to reduce the individual risk of accidents, this work proposes a method for managing the dynamic individual risk when toxic substance leakage and fire accidents occur concurrently, in which spatial and temporal synergistic effect of above two accidents is considered. The synergistic effect may reduce individual escape ability, and is modeled by quantifying the relationship between escape ability and toxic substance dose. In addition, the method analyzes dynamic individual risk of a person receiving the thermal radiation dose and toxic substance dose when escaping along a certain route. The casualty degree is described by the injury degree based on dose thresholds and the total fatality probability based on Probit model. Combined with the accident scenario, the critical safety escape distance is obtained. The model is proposed to plan the individual escape route based on the risk assessment results. The critical values of toxic substance concentration and thermal radiation intensity are defined as the basis for planning alternative escape routes. Finally, the optimal escape route is obtained according to the total fatality probability of each route. The range of safe escape direction can be used when wind direction information is inaccurate. A case study demonstrates the effectiveness of the proposed method and risk management strategy.

Evaluation of the critical success factors of dynamic enterprise risk management in manufacturing SMEs using an integrated fuzzy decision-making model

To succeed, a firm essentially needs to take the right amount of risk. Thus, the great significance of risk management has attracted many researchers to focus on how to implement enterprise risk management (ERM) systems most effectively. Generally, smaller entrepreneurial firms have to cope with this challenge more informally. Most manufacturing small and medium-sized enterprises (SMEs) are family companies; as a result, family dynamics greatly impact how such companies do business. The q-rung orthopair fuzzy set (q-ROFS) provides a wide window for the preference elicitation of decision-makers (DMs). Inspired by the advantages of the q-ROFS, in the current paper, a novel decision framework, the q-ROF-entropy-rank sum (RS)-additive ratio assessment (ARAS), called the “q-ROF-entropy-RS-ARAS” approach, is developed. The q-ROF-entropy-RS approach is applied to compute the weights of critical success factors (CSFs) for dynamic ERM in SMEs, and the q-ROF-ARAS model is used to assess enterprise preferences. An empirical case study is conducted to evaluate the CSFs for dynamic enterprise risk management in SMEs. Additionally, the comparison and sensitivity investigation are carried out to show the superiority of the developed framework.

Dynamic risk management in European energy portfolios: Evolution of the role of clean and carbon markets

This paper examines the potential of clean energy stocks and emission permits to reduce downside risk when combining them in a portfolio with dirty energy assets. We propose a strategy for building portfolios that are well diversified between equity energy and carbon markets that takes into account their dynamic price relationship. The asset allocation proposed is framed in a volatility-timing context, which reacts to changing market conditions, holding different weights at different times. To achieve this objective, we use multivariate GARCH models, specifically the Asymmetric Dynamic Conditional Correlations family, which allow us to obtain good estimations of the conditional covariance matrices of the daily asset returns. To determine the weights of the optimum minimum-risk portfolio, we use a method based on Engle and Colacito (2006) to compare the portfolio volatilities obtained with different models. The analysed period runs from January 19, 2010, to April 4, 2022, which, on the one hand, includes more than twelve years of the EU Emissions Trading System (EU ETS) beyond the Phase I pilot; and, on the other, considers the latest crisis episodes (Sovereign debt crisis, Brexit COVID-19, and the recent Russo–Ukrainian war). Our findings show that investing in clean energy companies is now valuable not only because of its contribution to a sustainable energy transition to renewable sources, but also due to its attractiveness from a financial point of view. This fact provides a ray of hope in terms of the climate emergency and avoiding the current geopolitical conflicts principally caused by certain countries’ energy dependence because their energy mix is still heavily overpowered by fossil fuels. The results of this research should encourage investors to decarbonise their equity portfolios, thus promoting the needed alignment of the financial system with the requirements of the energy transition.

Environmental, social, and governance (ESG) investing and commodities: dynamic connectedness and risk management strategies

PurposeThe purpose of this study is to examine the dynamic connectedness and volatility spillovers between commodities and corporations exhibiting the best environmental, social and governance (ESG) practices. In addition, the authors determine the optimal hedge ratios and portfolio weights for ESG and commodity investors and portfolio managers.Design/methodology/approachThis study uses the novel frequency connectedness framework to point out volatility spillover between ESG indices covering the USA, developed and emerging markets and commodity indices, including energy (crude oil, natural gas and heating oil), industrial metals (aluminum, copper, zinc, nickel and lead) and precious metals (gold and silver) by using daily data between January 3, 2011 and May 26, 2021, covering significant socio-economic developments and the COVID-19 outbreak.FindingsThe results of this study suggest a total connectedness index at a mediocre level, mainly driven by the shocks creating uncertainty in the short term. And the results indicate that all ESG indices are net volatility transmitters, and all commodity indices other than crude oil and copper are net volatility receivers.Practical implicationsThe results imply statistically significant hedging and portfolio diversification opportunities to investors and portfolio managers across the asset classes, proven by the hedging effectiveness analyses.Social implicationsThis study provides implications for policymakers focusing on the risk of contagion among the commodity and ESG markets during turbulent periods to ensure international financial stability.Originality/valueThis study contributes to the existing literature by differentiating ESG portfolios as the USA, developed and developing markets and examining dynamic connectedness and volatility spillovers between ESG portfolios and commodities with a different technique. This study also contributes by considering COVID-19 outbreak.

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

In today’s world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information. Previous researchers relied on trend identification before making any decision to buy or sell stocks but fail to make accurate decisions due to complex systems. Some studies showed analysis to apply to stop loss on every stock transaction that got wrong levels due to limited features scaling that relied on single indicators without checking the performance metrics such as mean, standard deviation, and value at risk. Some existing models are based on theoretical implementation and they possess inaccurate success in real-time stock market transactions. Earlier risk management techniques were based on fundamental statistics of the company performance based on specific quarters that propose the future expects in the positive direction that is not every true which results in huge financial loss. Previous researchers failed to consider dynamic risk management parameters to ensure minimum loss for decision-making in fast-moving stock variations. Machine learning simply refers to learning about computers and making predictions from data. Identifying and analyzing the risk factors in the stock market are the major and crucial stage for predicting the company stock values at the national and international levels. In existing research, all risk management-related factors are analyzed based on fundamental statistics of the company performance which are measured as quarterly results, which will not give long-term true predictions and will not provide positive directions to invest in further stocks. This research majorly focused on risk management for national stock companies using the machine learning methodology and algorithms. The objective is to determine if stock market indicators are suitable decision-aid tools within the context of intraday risk management. The review of the literature revealed that while there are many studies looking to foresee changes in the stock market, there are few studies looking to improve stock market risk management methods using machine learning algorithms. The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements. It has described the association between machine learning models and implicated the data with respect to discrete models based on supportive, dependable, nondependable parameters along with the name and type of the stock. This research has integrated a few crucial dependable parameters such as oil prices, on-hand projects, and future projects. It has integrated with the simple, multiple linear regression models to generate a signal for SPY growth. The proposed ML-based model has been evaluated by comparing two states of training and testing and achieved 96.3% of accuracy. The parameters used for evaluation are closing price, price differences, and daily return. The performance range of the proposed multiple regression model lies along the maximum drawn down which is 0.04411 for test cases and 1.2533 for training cases. Compare the performance of the proposed approach with that existing models with respect to the number of keys and methods associated with training and testing the data.

Optimal Cyber-Insurance Contract Design for Dynamic Risk Management and Mitigation

With the recent growing number of cyberattacks and the constant lack of effective defense methods, cyber risks have become ubiquitous in enterprise networks, manufacturing plants, and government computer systems. Cyber insurance provides a valuable approach to transfer the cyber risks to insurance companies and further improve the security status of the insured. The designation of effective cyber-insurance contracts requires considerations from both the insurance market and the dynamic properties of the cyber risks. To capture the interactions between the users and the insurers, we present a dynamic moral-hazard type of principal–agent model incorporated with Markov decision processes, which are used to capture the dynamics and correlations of the cyber risks as well as the user’s decisions on the protections. We study and fully analyze a case with a two-state two-action user under linear coverage insurance and further show the risk compensation, Peltzman effect, linear insurance contract principle, and zero-operating profit principle in this case. Numerical experiments are provided to verify our conclusions and further extend to cases of a four-state three-action user under linear coverage insurance and threshold coverage insurance.

Temporal-spatial characterization of mining-induced seismicity in the vicinity of a dyke – A case study

Major faults and dykes are common geological discontinuities in underground mines and can often contribute to significant failures such as coal bursts during mining. The failure mechanisms of faults have been widely studied, whereas the seismic characteristics associated with dykes have been rarely investigated for mine dynamic hazard risk management. This research aimed to characterize the seismic behavior near an intrusive dyke that intersected with the roadways in an underground longwall coal mine in Australia. The temporal-spatial variations of seismic multi-parameters around the stiff dyke were analyzed, including clustered seismicity, event locations, Es/Ep ratio, b-value and correlation with the released seismic energy. Four stages were defined based on the evolutions of seismic characteristics. The rock fractures initiated near the dyke when the longwall face was 400 m from the dyke. The seismicity started to increase when the longwall was <100 m from the dyke and decreased drastically after the longwall mined 50 m past the dyke. The seismic events were concentrated at the inbye side of the dyke and propagated to the outbye side of the dyke as the longwall mined through the dyke. The spatial distribution of Es/Ep ratios and temporal variations of the b-value indicated that higher magnitude seismic events dominated by shear failures occurred in the hanging wall of the dyke, suggesting more sudden seismic energy release and escalating seismic risks around the roadways at the inbye side of the dyke. Meanwhile, when the longwall was mining through the dyke, even though the seismicity was intensive, seismic energy was more frequently and gently released so that stress was not significantly accumulated to induce any major failures at the outbye side of the dyke. The results demonstrated that the geological features of the dyke and the longwall mining progress had a controlling effect on the seismic characteristics in the time-space domain. The comprehensive analysis of seismic multi-parameters reliably revealed the rock mass response near a major dyke and can provide guidance for assessing and managing the risks of dynamic mining hazards around dykes.

Optimal hedging in the presence of internal flexibility

AbstractInternal flexibility aids in risk management and has a broader application than hedging instruments. This paper demonstrates how optimal hedging policies are affected by it. We develop a dynamic risk management model to capture financial and operational decisions. We first show that internal flexibility reduces the marginal value of hedging instruments. As a result, optimal financial hedging is selective and dependent on investment opportunities. These opportunities account for the majority of the difference between hedged and unhedged firms. By incorporating internal flexibility, the model becomes more realistic but also generates a complex interaction between financial hedging and marketing strategy. During the growth phase, hedging instruments are partially substitutive but have a synergistic effect on investment. In the mature or declining phase, the remedial effect of marketing strategy maintains investment, thereby increasing operating risk and the marginal value of financial hedging. These results are applicable to firms free of agency conflicts and provide a solid theoretical basis for future empirical tests. We advise that scholars thoroughly examine internal flexibility and development stages in the process.

INVESTIGATING THE INTERRELATION OF COST RISKS IN LARGE-SCALE PROJECTS WITH A SYSTEM DYNAMICS APPROACH

Large-scale projects are considered the riskiest projects due to their multidisciplinary nature and complexity. The main risk factors include a high project budget, technical challenges during project implementation that did not exist before, continuous project change orders, and environmental factors governing them. On the other hand, the project manager sometimes cannot correctly manage project risks and solve problems by applying traditional approaches despite spending enormous resources. Limitations of traditional risk management methods based on separate consideration of the impact of each risk and not considering the interrelations of risks, causal relationships between them, and the dynamic behavior of risk factors during the project lead project managers to resort to new methods such as dynamic risk management and the interaction of risks. Therefore, considering the number and position of large-scale projects within Iran and their role in economic and technical development, the present study is essential. In this study, by presenting a classified list of risks of large-scale projects, risks interaction will be identified, and then using the obtained relationships, we will present a dynamic model with the interaction of all risk factors on each other.

Dynamic risk management and asset comovement

When dealers’ hedging demand in one market depends on movements in another market it can drive a correlation between returns in the two markets. I show that changes in dealers’ demand for CDS protection for the purpose of counterparty risk management induce a non-fundamental correlation between credit and currency markets. The effects are economically significant. For example, I show that counterparty risk hedging associated with SoftBank’s currency swap portfolio substantially lowers the correlation between SoftBank’s CDS spread and the USD/JPY exchange rate and accounts for around 25% of the weekly volatility of CDS returns.

INVESTIGATING THE INTERRELATION OF COST RISKS IN LARGE-SCALE PROJECTS WITH A SYSTEM DYNAMICS APPROACH

Large-scale projects are considered the riskiest projects due to their multidisciplinary nature and complexity. The main risk factors include a high project budget, technical challenges during project implementation that did not exist before, continuous project change orders, and environmental factors governing them. On the other hand, the project manager sometimes cannot correctly manage project risks and solve problems by applying traditional approaches despite spending enormous resources. Limitations of traditional risk management methods based on separate consideration of the impact of each risk and not considering the interrelations of risks, causal relationships between them, and the dynamic behavior of risk factors during the project lead project managers to resort to new methods such as dynamic risk management and the interaction of risks. Therefore, considering the number and position of large-scale projects within Iran and their role in economic and technical development, the present study is essential. In this study, by presenting a classified list of risks of large-scale projects, risks interaction will be identified, and then using the obtained relationships, we will present a dynamic model with the interaction of all risk factors on each other.

Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market

With the advancement of Power Market reform and the opening of the Demand Side market, the dynamic risk management of electricity retailers has attracted more attention. This paper designs a risk prevention linkage mechanism of credit evaluation-risk measurement for retailers. Firstly, this paper constructs a retailers' credit evaluation index system containing 18 indexes, and proposes a retailers' credit rating technology based on Bayesian Best Worst Method (BBWM)- Cloud model. On the basis of Best Worst Method (BWM), BBWM uses Multinomial Distribution to model the input indexes, so as to obtain more reliable weights in the group decision-making environment. Secondly, a credit risk measurement model based on improved Credit Metrics model and CVaR method for electricity retailers is constructed. The Credit Metrics model adopts the Marked-to-Market system, which can consider the retailers' default risk and the risk of credit rating changes, and CVaR method can accurately describe the retailers’ credit risk. Finally, four electricity retailers are taken as examples to verify the effectiveness and scientificity of the model. This paper can provide guarantee and theoretical basis for the credit management of Power Sales market, standardize the behavior of electricity retailers and reduce the transaction risk of Power Market.

Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study

The price risk related to trading in electricity markets has increased significantly in the recent years, due to the ongoing markets liberalization and the growing renewable energy sources production. In this paper we propose a short-term risk management strategy for an electricity supplier, that utilizes diversification of the markets for electricity trade. Based on the day-ahead probabilistic forecasts of electricity prices we calculate predictions of different risk and profit measures taking into account a possible split of the traded energy among markets. Strategies aiming at the risk minimization, profit maximization or finding optimal trade-off between risk and return are applied for the German and Polish electricity markets. The obtained results show that diversifying the markets at which electricity is traded leads to higher profits than trading on the day-ahead market and, at the same time, lower risk than associated with trading on the intraday or balancing market. In each of the considered cases, except for volatility as a risk measure for the German market, the goal of the strategy has been achieved. Implementation of the dynamic strategies has improved the outcomes in terms of risk or profit, compared to the static ones.

A Convergent Approach to the Viability of the Dynamical Systems: The Cognitive Value of Complexity

The integration of information and industrial technologies, digitalization and differentiation of sciences are accompanied by an increase in various types of complexity. This limits the capabilities of computer modelling, data mining, and predictive analytics. The increasing cognitive complexity of information flows and their diversity creates problems of safety, reliability and stability of the functioning of a complex dynamic system in extreme conditions. Here we show the possibility of cognitive visualization of signals of different nature through their geometrization in the form of a topological 3D model of functioning. Its projections are spatio-temporal signatures, the configurations of which reflect the dynamic, energetic and structural features of the model. An increase in the number of components of the signature configuration and its area under external influence indicates an increase in structural and functional complexity. Therefore, the signal structure can be analyzed in real time using complementary probabilistic and deterministic methods. A set of tools for the synthesis and analysis of 3D models has innovative potential for monitoring the functioning of elements of complex dynamic systems, risk management and predictive analytics.

Commitment, agency costs and dynamic capital structure

This paper studies leverage dynamics when shareholders commit to optimizing total enterprise value and face debt adjustment friction. Debt adjustment costs render the leverage commitment a double-edged sword. High-levered firms benefit from the commitment due to active debt repurchase. However, such debt buyback incurs a heavy burden and constrains financial flexibility. With high debt adjustment costs, it could be inefficient for the enterprise to maintain a firm-optimal debt policy. Interestingly, the incentive alignment effect from commitment makes shareholders act as if creditors in normal times. For instance, shareholders exhibit precautionary motives and overinvest. Finally, we show that dynamic risk management exacerbates the debt-equity conflicts and improves the commitment value. Highlights The firm-optimal debt policy eliminates the leverage ratchet effect by accelerating debt repayment should the firm's fundamental deteriorates. The commitment to the firm-optimal debt policy hurts enterprise value when facing high debt adjustment costs and short-term debt. Incentive alignment effect from the firm-optimal commitment causes shareholders to overinvest and exhibit risk-aversion in normal times.