Application Of Risk Management Research Articles

Research and Examples on the Application of Financial Derivatives in Risk Avoidance

The fragility of the global financial system, exposed during the 2008 financial crisis, highlighted the challenges posed by the complexity and opacity of the derivatives market. This research examines the application of financial derivatives in risk management, focusing on their role in mitigating risks through the case of JPMorgan Chase during the crisis. The paper focuses on the types and functions of financial derivatives, especially the use of credit default swaps (CDS) and interest rate swaps (IRS) and their risk management applications in financial institutions. Data sources include JPMorgan Chase's public annual report, financial report and related news reports. The results show that JPMorgan Chase has reduced some subprime risk exposure and successfully avoided potential losses of about US$5 billion through the effective use of financial derivatives. However, the use of derivatives also exposes problems such as market liquidity and counterparty risk, reminding financial institutions to carefully manage related risks when using derivatives. In conclusion, while derivatives can be powerful risk management tools, their over-reliance may introduce new systemic risks.

Integration of PSPU-Net gravity inversion neural network with gravity data for enhanced 3D basement relief estimation

Estimating the sediment-basement interface is critical to understanding basin evolution and its applications in energy, water resources, and seismic risk management. We develop a PSPU-Net gravity inversion (GI) network, a deep-learning approach combining the Pyramid Scene Parsing Network and U-Net, for gravity data to recover the sediment-basement interface. Training and validation involve smoothed basement models generated from random rectangles followed by filtering. We also incorporate uplifted basements and intrusions to enhance performance in complex geologic contexts. Numerical results for synthetic models determine PSPU-Net GI’s effective recovery of sediment-basement interface relief. To improve field data predictions, we implement transfer learning and normalization strategies. Transfer learning constructs a small number of additional basement models based on site-specific prior information and fine-tunes the neural network trained on the original general models. The normalization strategy provides a convenient way of harnessing depth information from seismic data and wells. We apply our framework to the gravity data from the western margin of the Pannonian Basin (Austria). The predictions from three implementations, baseline PSPU-Net GI, PSPU-Net GI + transfer learning, and PSPU-Net GI + normalization, successfully characterize the basement relief and are consistent with the results in previous publications. Compared with the prediction from the baseline PSPU-Net GI, the prediction accuracies obtained from the PSPU-Net GI implementations with the additional transfer learning and normalization components are notably improved.

Crop Diversification for Ensuring Sustainable Agriculture, Risk Management and Food Security

AbstractAgriculture faces growing challenges from climate change, pest pressures, and market instability. Crop diversification offers a sustainable strategy to enhance resilience and reduce the risks of monoculture. This review examines crop diversification as a response to these challenges, with a focus on its applications in sustainable agriculture, risk management, and food security. Strategies such as spatial, temporal, genetic, and intercropping diversification enhance soil health, improve pest management, and boost resilience to climate variability. The review highlights key principles, including ecological resilience, risk distribution, and resource optimization. By adopting diverse crops, farmers can mitigate soil degradation, reduce pest outbreaks, and stabilize incomes. Successful case studies from various regions, such as integrated rice‐fish farming and agroforestry, demonstrate how diversification can improve productivity and sustainability. However, challenges remain, such as knowledge gaps, market access issues, and policy limitations. The review concludes with recommendations for future research and policy interventions, stressing the need for tailored diversification strategies, better support systems, and further exploration of innovative practices. This overview underscores the potential of crop diversification to build resilient, sustainable agricultural systems while addressing global food security concerns.

Increased sagittal abdominal diameter is associated with a higher risk of kidney stones.

This study investigates the relationship between sagittal abdominal diameter (SAD), a measure of abdominal obesity, and kidney stone disease (KSD) in the U.S. population. Additionally, it explores potential underlying mechanisms and evaluates the clinical utility of a predictive model. Data were collected from 11,671 participants, including 1,136 cases of KSD. Univariate and multivariate logistic regression analyses, dose-response curves, and mediation effect assessments were employed to examine the association between SAD and KSD. A predictive model was developed and validated using calibration curves, receiver operating characteristic (ROC) curves, and clinical decision curves. Additionally, hematological indicators were analyzed to identify potential mediating factors. SAD showed a strong and positive association with KSD, even after adjusting for confounders such as gender, age, and education. The predictive model demonstrated moderate accuracy (AUC = 0.661) and clinical utility. Hematological analyses indicated that granulocyte count (GRAN) significantly mediated the relationship between SAD and KSD (P < 0.001). SAD is a significant risk factor for KSD, underscoring the role of abdominal obesity in kidney stone formation. The predictive model demonstrates potential clinical applications for early risk assessment and management of KSD.

Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential

This review article aims to discuss the current status and future potential of Geographic Information Systems (GIS) in map-based technology of tsunami risk management, especially in seismically active, well-known tsunami regions of the world. It presents GIS technologies for hazard mapping, risk assessment, and information generation for disaster-response operations. These are important tools for accurately mapping vulnerable areas by integrating real-time and historical data to develop accurate forecasts for possible tsunamis. Demographic and geographic data were also analyzed by GIS to determine the optimum route to develop evacuation strategies. A set of case studies demonstrates how GIS improves community resilience by supporting informed decision-making. In addition, suggestions are made for how future steeps as the integration of machine learning techniques as emerging tools for analyzing and classifying complex and vast datasets, which may enhance GIS applications in tsunami risk management to improve the accuracy and utility of these tools.

HOW RISK MANAGEMENT CAN BE IMPLEMENTED IN NEW AGRO-PRODUCT DEVELOPMENT (NAPD)?: A SYSTEMATIC LITERATURE REVIEW

New product development is highly complex because it integrates technical challenges from both internal and external sources. The potential for uncertainty or failure exists at every step of the product development process. One of the important tools to minimize risks in product development is risk management in new product development. This paper aims to review some research literature on the concept, process, and category of risk management in new agro-product development. This research uses a literature review analysis to analyze the concept and process of risk management in new product development. The articles were selected from Emerald, Elsevier, Springer, MDPI, and Taylor &amp; Francis databases. The findings of this study demonstrate the critical role of risk management in minimizing risks associated with new product development through the implementation of diverse strategies. Risk management involves identifying, analyzing, evaluating, and treating risks. A variety of internal and external factors influence risk in product development. Internal factors are design, production, financial, management (organizational), marketing, market, technology, and supplier risks. External factors include suppliers, customers, and retailers outside the company. Many businesses, including small and medium-sized enterprises (SMEs), have adopted risk management to prevent product failures and financial losses. This paper provides a research framework for applying risk management to new agroindustrial product development.

Ensuring psychological health of the participants in the educational process in the crisis wartime conditions

The study raises the issue of ensuring psychological health of participants in the educational process of higher education institutions (HEIs) who carry out educational activities in crisis conditions caused by the military events in Ukraine in 2014 and Russia's full-scale military aggression in 2022. The article examines essential characteristics of the military-political crisis and identifies range of problems that a person faces when making decisions in a crisis situation. Attention is focused on the problem of human resource management during a crisis. Based on the analysis of publications by Ukrainian scholars, the article discusses challenges faced by academic staff and students of Ukrainian universities in the context of war. The higher education institutions that were temporarily displaced from Luhansk and Donetsk regions twice in 2014 and 2022 were analyzed in a particular way as the educational institutions that were affected by negative factors for the longest time and were most affected by the war. The dynamics of changes in the number of students and the number of research and teaching staff were studied. The dynamics of changes in the number of full-time employees in public HEIs of the first wave of displacement is compared with the data on HEIs located near the combat zone. It is concluded that the participants in the educational process in higher education institutions and the educational institutions themselves need more support from the government and international communities to preserve the scientific potential and human capital of Ukraine as a whole. Attention is focused on the need to apply risk management in the management of higher education institutions in the context of the military-political crisis, and to address the problem of preserving the psychological health of students and academic staff not only at the level of the university management, but also at the state level. Recommendations for maintaining psychological stability of students and academic staff of higher education institutions in wartime are proposed.

An Overview of the Research Literature on the Topic of Supply Chain Management in the Shipping Trade

In consequence of the relentless advance of globalization and the ascendant position of the digital economy, the great significance of the management of supply chains against the background of international shipping trade is becoming increasingly critical. Optimizing supply chain management technology is now regarded as a necessary step for the development of enterprises.This paper presents a systematic and detailed review of the literature on the integration of shipping trade and supply chain management technology. Meanwhile, this paper examines how supply chain management is influenced by the globalization and digitalization trend, involving risk management and technology application. Additionally, it investigates the contribution of green supply chain management in reducing environmental impact as well as enhancing corporate social image. Through a comprehensive analysis of existing literature, this paper proposes future research directions and feasible solutions, aiming to provide a valuable reference for enterprises and scholars. Finally, this paper combines the current situation of the shipping trade industry with projections about the future, and proffers recommendations for how shipping companies can curtail costs and enhance efficiency, enhance environmental protection and competitiveness.

A Hybrid LSTM-KNN Framework for Detecting Market Microstructure Anomalies:

This paper proposes a novel hybrid LSTM-KNN framework for detecting market microstructure anomalies in high-frequency credit default swap (CDS) markets. The framework integrates the temporal learning capabilities of Long Short-Term Memory networks with the pattern recognition strengths of K-Nearest Neighbors classification to identify price jumps and market anomalies. Through analysis of high-frequency CDS market data spanning from 2020 to 2023, encompassing over 2.5 million data points from five major CDS indices, the research demonstrates significant improvements in jump detection accuracy. The hybrid model achieves a 92.8% accuracy rate, representing a 15.2% improvement over traditional statistical methods and an 8.5% enhancement compared to standalone deep learning approaches. The framework maintains computational efficiency with an average processing latency of 48.2 milliseconds, enabling real-time market applications. The empirical analysis reveals strong correlations between detected jumps and market liquidity conditions, with bid-ask spreads and order book imbalances identified as critical predictive indicators. The research contributes to both theoretical understanding of market microstructure dynamics and practical applications in risk management and market surveillance.

Current State of Operational Risk Management in Higher Education Institutions

Objective: Showing the shortcomings of operational risk management in universities that inhibit the achievement of strategic objectives and organizational goals is the central objective of this research. Method: To this end, a diagnosis of the current state of the associated variables was carried out in accounting and administration faculties in Mexico, Ecuador and Cuba. Results and Discussion: Among the main findings, it is highlighted that the most affected dimension is the Strategic orientation design of the organization for risk management, although in general, none of the dimensions evaluated presents a favorable behavior, which indicates the need to apply risk management tools with a strategic and integrated approach and a greater follow-up of prevention plans to achieve a better correspondence between planning and control. Originality/Value: This study contributes to the literature by providing a systematization of knowledge available in scientific literature on risk management in higher education institutions to subsequently verify the behavior of these variables in higher education institutions that operate in contexts as diverse and different as they belong to countries like Mexico, Ecuador and Cuba that respond to different socio-economic environments.

PENERAPAN MANAJEMEN RISIKO TERHADAP PERSAINGAN INDUSTRI CAFE DALAM UPAYA MEMPERTAHANKAN LOYALITAS PELANGGAN PADA CAFE MAHONI

The rapid growth of the café industry in urban areas has intensified competition, requiring each cafe to retain customer loyalty through effective business strategies. Cafe Mahoni, located in a strategic area, faces external challenges such as environmental noise and intense competition, as well as internal challenges related to managerial oversight and limited facilities. This study aims to analyze the application of risk management at Cafe Mahoni to maintain customer loyalty. Using Primary Data Analysis (PDA) methods through interviews, surveys, and observations, this research identifies risk factors and their handling strategies. The results show that facility rejuvenation, increased supervision frequency, and the implementation of service and product quality assurance standards play a vital role in maintaining customer satisfaction and loyalty, thereby enhancing Cafe Mahoni's competitiveness in the café industry.

Integrating Policy Instruments for Enhanced Urban Resilience: A Machine Learning and IoT-Based Approach to Flood Mitigation

In the context of global urbanization, the interconnected architecture of economic, social, and administrative activities in modern cities cultivates a complex web of interdependencies. This intricacy amplifies the impacts of natural disasters such as urban flooding, presenting unprecedented challenges in risk management and disaster responsiveness. To address these challenges, this study defines the concept of urban flood resilience and outlines its practical applications in flood risk management, proposing an integrated resilience governance framework. The framework systematically enhances urban flood management by combining structural flood mitigation methods with advanced technologies, including the Internet of Things (IoT) and non-structural decision-support tools powered by Machine Learning Algorithms (MLAs). This integrated approach aims to improve early flood warning systems, optimize urban infrastructure planning, and reduce flood-related risks. The case study of the Cypress Creek watershed validates the framework’s effectiveness under specific scenarios, achieving reductions of 25% in inundation area, 30% in peak flow, and 20% in total flood volume. These results not only demonstrate the framework’s efficacy in mitigating flood impacts but also provide empirical support for developing resilient urban governance models, highlighting the essential role of adaptive policy instruments in urban flood management.

Aggregate: fast, accurate, and flexible approximation of compound probability distributions

Abstract Aggregate implements an efficient fast Fourier transform (FFT)-based algorithm to approximate compound probability distributions. Leveraging FFT-based methods offers advantages over recursion and simulation-based approaches, providing speed and accuracy to otherwise time-consuming calculations. Combining user-friendly features and an expressive domain-specific language called DecL, Aggregate enables practitioners and nonprogrammers to work with complex distributions effortlessly. The software verifies the accuracy of its FFT-based numerical approximations by comparing their first three moments to those calculated analytically from the specified frequency and severity. This moment-based validation, combined with carefully chosen default parameters, allows users without in-depth knowledge of the underlying algorithm to be confident in the results. Aggregate supports a wide range of frequency and severity distributions, policy limits and deductibles, and reinsurance structures and has applications in pricing, reserving, risk management, teaching, and research. It is written in Python.

Modelling and Forecasting of Exchange Rate Pairs Using the Kalman Filter

ABSTRACTDeveloping and employing practically useful and easy to calibrate models for prediction of exchange rates remains a challenging task, especially for highly volatile emerging market currencies. In this paper, we propose a novel approach for joint prediction of correlated exchange rates for two different currencies with respect to the same base currency. For this purpose, we reformulate a generalized version of a bivariate ARMA model into a state space model and use the Kalman filter for estimation and forecasting of the underlying exchange rates as latent variables. With extensive numerical experiments spanning 18 different exchange rates (across both emerging markets, developing and developed economies), we demonstrate that our approach consistently outperforms univariate ARMA models as well as the random walk model in short term out‐of‐sample prediction for various exchange rate pairs. Our study fills a gap in the empirical finance literature in terms of robust, explainable, accurate, and easy to calibrate models for forecasting correlated exchange rates. The proposed methodology has applications in exchange rate risk management as well as pricing of financial derivatives based on two exchange rates.

The Role of Sharia-Compliant Financial Institutions in Promoting Economic Stability: Insights from Recent Scholarly Contributions

This research examines the role of Islamic financial institutions in promoting economic stability through a literature study of recent scholarly contributions. Islamic financial institutions are seen as alternative financial instruments that operate under Islamic principles, which prohibit usury, excessive speculation, and non-transparent activities. This research aims to understand the mechanisms used by Islamic financial institutions in creating economic stability and evaluate the latest empirical evidence supporting such contributions. The research method used is a literature study with a qualitative approach, where various journals and research reports on Islamic finance, economic stability, and monetary policy are reviewed and analysed. The results show that Islamic financial institutions play an important role in economic stability through asset-based financing, portfolio diversification, as well as the application of risk management in accordance with Islamic principles. This research contributes to providing policy makers, academics, and financial practitioners with a deeper understanding of the potential of Islamic financial institutions as instruments that can strengthen economic stability.

Exploring the predictive power of artificial neural networks in linking global Islamic indices with a local Islamic index

This study aims to analyze the correlations and information flow between a local Sharia-compliant index and global Sharia-compliant indices using Artificial Neural Network (ANN) models, shedding light on the integration of international and national Sharia-compliant markets. Given recent market conditions and uncertainties at both local and global levels, the research investigates the predictive power of ANNs and examines the relationship between the Dubai Financial Market Sharia Index (DFMSI) and three well-known global Islamic indices: the Dow Jones Islamic Market World Index, the Standard and Poor’s Global Sharia Index, and the Financial Times Stock Exchange Sharia All World Index. By incorporating data from multiple global Sharia indices, the study expands data sources beyond the local market, providing a comprehensive analysis of factors influencing the DFMSI’s price. This approach reduces reliance on a single dataset and strengthens the robustness of the research findings. Additionally, the inclusion of the three global Sharia indices offers benefits such as access to global market insights, risk management, and practical applications for investors. The study demonstrates that ANNs, particularly the Nonlinear Autoregressive with External Input Neural Network (NARX), outperform classical models in accurately predicting the DFMSI. This research contributes significantly to the understanding of Sharia-compliant investments by providing a comprehensive analysis of how multiple global indices affect a local market index. It also offers valuable insights for investors and portfolio managers, suggesting that the integration of advanced machine learning models can enhance decision-making processes and improve risk management strategies.

Unveiling the Burden: A Six-Year Retrospective Analysis of Pressure Ulcer Epidemiology in a ICU.

This study describes the epidemiological changes in pressure ulcers (PUs) in a Portuguese intensive care unit (ICU) from January 2017 to June 2023, characterizes critically ill patients with PUs, identifies specific risk factors, and assesses the effectiveness of implemented preventive measures. A retrospective observational cohort study was conducted, analyzing records of ICU patients with PUs during the specified period. Data were extracted from the institution's Global Risk Management application and the ICU's electronic PU registry. The study included patients with ICU stays longer than 24 h and excluded those with ineligible clinical records or incomplete characterization data. Among 3816 evaluated patients, 257 developed a total of 345 PUs, averaging 1.4 PUs per patient. The average PU prevalence rate was 6.81%, with the highest prevalence in 2020 (11.0%) and the lowest in 2022 (3.48%). The average incidence rate was 3.76%, peaking at 5.71% in 2020 and declining to 2.54% in 2023. The sacrum and heels were the most commonly affected areas, with Category 2 PUs being the most frequent. Key intrinsic risk factors included systemic diseases and sensory deficits, with pressure identified as a significant extrinsic factor. Preventive measures focused on risk assessment and pressure control. The study reveals a PU prevalence of 6.81% and an average incidence of 3.76%, underscoring the need for enhanced preventive strategies, especially in anatomical areas like the sacrum and heels. It emphasizes the importance of personalized assessments, continuous education for nursing staff, and a multidisciplinary approach to improve patient outcomes and care quality in the ICU.

Implementation of Risk Management in Asset Management Governance Towards Optimizing Asset Utilization at Public Service Agency State Universities (Case Study of UPN Veteran Yogyakarta)

This study aims to analyze the application of risk management in asset governance at UPN Veteran Yogyakarta to achieve optimal asset utilization. As a Public Service Agency State University (PTN-BLU), UPN Veteran Yogyakarta faces various challenges in asset management, including budget constraints, lack of consistent monitoring, and the unavailability of formal risk evaluation standards. Qualitative descriptive research methods are used to gain in-depth understanding through interviews with asset managers and field observations. The results show that the implementation of risk management can have a significant impact on the efficiency of budget allocation, which includes more appropriate budget distribution based on priority needs and risks. Risk management also has the potential to extend the useful life of assets, with risk mitigation measures taken on assets that show signs of wear or early damage, thus preventing major repair costs due to serious damage. This research highlights that a technology-based monitoring system is an indispensable aspect to support the effectiveness of risk management in asset management. The implementation of structured risk assessment procedures helps in determining asset maintenance priorities, so that the assets that are most critical to the sustainability of campus operations receive greater attention. With the implementation of this comprehensive risk management, optimization of asset utilization can be achieved, which in turn supports the stability and sustainability of UPN Veteran Yogyakarta's operations as a quality higher education institution.

Analysis of the Implementation of Risk Management to Minimize the Risk of Bad Credit at the Nias Rural Development Savings and Loans Cooperative (KSP3) Gunungsitoli Branch

This study aims to determine the application of risk management carried out by the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch in minimizing the risk of bad loans. In this study, the type of research used is a descriptive type with a qualitative approach. Data collection techniques are used through observation, interviews and documentation. To obtain information, the researcher conducted direct interviews with 6 informants, namely branch heads, 2 employees and 3 members of the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch. The data analysis techniques used in qualitative research include transcripts of interview results, data reduction, data presentation and conclusion drawn. The results of the study show that the implementation of risk management carried out by the Nias Rural Development Savings and Loan Cooperative (KSP3) Gunungsitoli Branch is carried out through a series of stages, namely risk identification, risk measurement, risk monitoring and risk control. The implementation of risk management to minimize the risk of bad loans has been effectively implemented by KSP3 Nias Gunungsitoli Branch as evidenced by the NPL (Non-Performing Loan) rate from 2020-2022 which is still below 5%.

Strategic behavior in multi-criteria sorting with trust relationships-based consensus mechanism: Application in supply chain risk management

Strategic behavior is common in multi-criteria sorting, where manipulated alternatives are allocated to the target category to achieve the intended goal. Engaging in such strategic behavior in sorting consensus often comes at a cost, which is closely tied to the degree of preference adjustments and the level of trust relationships among decision makers. This study investigates strategic behavior using piecewise cost-based consensus mechanism in social trust network. Firstly, the trust relationship dependent piecewise cost is formally defined and formulated by considering the impact of trust relationship on the preference adjustments willingness to achieve sorting consensus. Then, based on the trust relationship dependent piecewise cost, a preference strategic manipulation model is proposed to achieve strategic behavior in sorting consensus, which is implemented by minimizing the cost of strategically guiding decision makers in adjusting their preferences. Considering that social trust network affects strategic manipulation, a preference-network collaborative strategic manipulation model is further proposed, which is designed to strategically guide decision makers in adjusting both their preferences and social trust networks. Moreover, the practicality of proposed strategic manipulation models is exemplified through a case study on supply chain risk management and their performance is validated via simulation analysis.