Financial Risk Management Research Articles

Efficient estimation of parameters in marginals in semiparametric multivariate models*

We consider a general multivariate model where univariate marginal distributions are known up to a parameter vector and we are interested in estimating that parameter vector without specifying the joint distribution, except for the marginals. If we assume independence between the marginals and maximize the resulting quasi-likelihood, we obtain a consistent but inefficient QMLE estimator. If we assume a parametric copula (other than independence) we obtain a full MLE, which is efficient but only under a correct copula specification and may be biased if the copula is misspecified. Instead we propose a sieve MLE estimator (SMLE) which improves over QMLE but does not have the drawbacks of full MLE. We model the unknown part of the joint distribution using the Bernstein-Kantorovich polynomial copula and assess the resulting improvement over QMLE and over misspecified FMLE in terms of relative efficiency and robustness. We derive the asymptotic distribution of the new estimator and show that it reaches the relevant semiparametric efficiency bound. Simulations suggest that the sieve MLE can be almost as efficient as FMLE relative to QMLE provided there is enough dependence between the marginals. We demonstrate practical value of the new estimator with financial risk management examples, where the use of SMLE leads to superior Value-at-Risk predictions. The paper comes with supplementary materials which include all proofs, codes and datasets, details of implementation and an additional application to insurance claims.

Business risks in managing the economic development of an agro-industrial complex enterprise

Relevance. This scientific article examines the key aspects of financial risk management in the agro-industrial complex. The study is based on an analysis of the price volatility of grain, fertilizers and fuel over the past five years. The results obtained make it possible to identify effective risk management strategies, including the use of financial instruments, and provide practical recommendations for agricultural enterprises. The conclusions of the study can contribute to increasing the stability of the profitability of agribusiness with the variability of prices for raw materials.The purpose of the scientific article is to conduct a comprehensive study to assess financial risks in the agroindustrial complex. The research is aimed at analyzing the price volatility of key raw materials, developing effective risk management strategies, as well as providing practical recommendations for agricultural enterprises.Objectives: development of a model for assessing financial risks in agribusiness based on the analysis of price volatility of grain, fertilizers and fuel over the past five years; research on the effectiveness of financial instruments, such as grain options, to mitigate risks when investing in agricultural production; development of practical recommendations for agricultural enterprises, including diversification of the investment portfolio and the use of financial instruments based on calculations and analysis;Methodology. The methods used in the course of the study include a variety of bibliographic sources, data collection tools, as well as expert approaches.Results. The results of the study include a comprehensive assessment of financial risks in agribusiness based on the analysis of price volatility of key raw materials. This made it possible to identify effective risk management strategies, such as the use of grain options, to ensure stable profitability while reducing the level of financial risk.Conclusions. The results obtained in the course of the study provide practical recommendations for agricultural enterprises on effective financial risk management. Developed strategies, such as the use of grain options, can help reduce the impact of price volatility on profitability, ensuring a more stable financial position of the enterprise.

Literature Study on Financing Risk Management Research in Non-Bank Institutions: Baitul Mal Wat Tamwil

Baitul Mal Wat Tamwil (BMT) plays an important role in serving the financial needs of people who have not been reached by conventional banks, especially the lower economic strata. Along with the growth of BMT in Indonesia since 1984, the main challenge faced is the financing risk that can threaten the sustainability of operations. Previous research on financing risk management in BMT has been widely conducted. However, there has been no literature review on the topic. Therefore, this article aims to conduct a literature review of the results of financing risk management research on BMT. The review was performed on the research results published in national scientific journals indexed in Google Scholar as many as ten articles. The results of the review show that trained human resources play an essential role in risk management in BMT; BMT does not yet own complete regulation and standardized risk control system; submission of financing applications at BMT is carried out by the procedure; Problematic financing is handled gradually starting from a familial approach, the application of rescheduling and restructuring, then the last step is legal settlement. The implication of this study is that increasing human resource capacity and strengthening regulations can improve the sustainability and competitiveness of BMT as a sharia microfinance institution in Indonesia

Impact of Macroeconomic Shocks on Financial Performance and Risk Management: A Case Study of LPP SA During the COVID-19 Pandemic and the Ukraine War

This study analyses the impact of two major macroeconomic shocks—the COVID-19 pandemic and the Ukraine war—on the financial stability of LPP SA, a leading apparel company in Poland and Central and Eastern Europe. This research uses the coefficient of variation to assess changes in financial volatility and evaluates the effectiveness of LPP SA’s risk management strategies. The findings reveal that, while the COVID-19 pandemic had a more destabilising effect on the company’s performance, the Ukraine war’s impact was mitigated by strategic diversification and resource reallocation. The study’s implications are relevant for risk management practices in the apparel industry and other consumer-driven sectors.

Improving realised volatility forecast for emerging markets

AbstractAccurate forecasting of realised volatility is essential for financial risk management and investment decision-making in emerging markets, taking the South African financial market as a benchmark. This study examines the predictive performance of four prominent models: HAR (Heterogeneous AutoRegressive), realised GARCH (Generalized AutoRegressive Conditional Heteroscedasticity), Recurrent Conditional Heteroskedasticity (RECH), and the Rough Fractional Stochastic Volatility (RFSV) models. These models are specifically tailored to capture the complex dynamics and long-range dependence observed in financial time series. We illustrate the challenges and limitations of these models outside the context of established markets. Our empirical findings reveal unique strengths for each model. The HAR model excels in capturing long-term volatility patterns, while realised GARCH models effectively capture volatility clustering and persistence. RECH model showcases their ability to forecast Value-at-Risk, while the RFSV model successfully captures irregular and long-memory characteristics. We provide empirical evidence that the South African financial market is rough. Moreover, this study provides valuable insights into forecasting realised volatility in the South African market, and the findings can assist practitioners and investors in making informed decisions and developing robust risk management strategies.

Strategic Insights into Bank Performance: CAMELS Analysis of Indian Banks

The banking sector is a cornerstone of the global economy, driving economic growth, financial sta-bility, and efficient resource allocation. Assessing the financial health and operational effectiveness of banks is crucial for maintaining the stability of the broader financial system. The CAMEL model, a well-established analytical framework, provides a comprehensive approach to evaluating key aspects of bank-ing performance, including Capital Adequacy, Asset Quality, Management Efficiency, Earnings Quality, and Liquidity. This model highlights areas for improvement and ensures a thorough assessment of a bank’s financial soundness. Extensive research has confirmed the model's applicability across various global contexts, including India, Nepal, Pakistan, Saudi Arabia, and Sri Lanka, consistently identifying asset quality, capital adequacy, and earnings as essential drivers of profitability. The model also reveals that private banks generally outperform public banks in most parameters. Moreover, the CAMEL model has proven to be a valuable tool in promoting financial inclusion, particularly through the analysis of Small Finance Banks, and its adaptability to emerging markets further underscores its relevance. Despite its broad applicability, gaps exist in the literature, particularly regarding the model’s adaptation to diverse regulatory environments and market dynamics. Addressing these gaps will enable more customized strategies for financial analysis and risk management, enhancing the resilience of the global banking system. This study aims to contribute to this discourse by exploring the nuanced use of the CAMEL model in assessing the financial performance of banks in varied contexts. The findings aim to offer ac-tionable insights for improving financial performance, managing risks, and fostering sustainable growth in the banking industry.

Financial Reporting and Analysis: Evaluating Property, Financial Position, and Results in Business Entities

Financial analysis is essential for evaluating company performance, providing insights into assets, capital, and financial health. This research highlights the importance of key indicators like profitability, efficiency, and stability in supporting strategic decisions and managing risks. The paper further examines the utility of discriminant models, such as Altman's Z-score and models tailored to Polish market conditions, for early detection of financial distress and potential bankruptcy. It provides a comparative analysis of multiple models, including those by Gajdka and Stos, Hadasik, Hołda, and Sojak and Stawicki, evaluating their efficacy in forecasting financial risk.The study applies these models to assess the financial standing of Wawel S.A., a prominent confectionery producer in Poland, exploring its financial statements, operational performance, and risk indicators. The findings reveal the nuanced applications and limitations of discriminant models, emphasizing the importance of adapting methodologies to local market conditions and the unique characteristics of individual enterprises. This research contributes to the broader discourse on financial risk management, offering practical insights for businesses, analysts, and policymakers.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Federated Risk-Based Access Control Model for P2P Lending Platforms: A Multi-Agent Systems (MAS) Approach

This study addresses the inherent risk management challenges in decentralized finance, particularly for peer-to-peer (P2P) lending platforms. We propose a novel framework that leverages a Multi-Agent System (MAS) to establish a collaborative network encompassing loan originators, investors, regulators, and service providers. This distributed approach facilitates federated risk management, where risk assessment and mitigation responsibilities are shared across these entities. The MAS employs a comprehensive nine-factor assessment (detailed in Table 5) to evaluate industry risk profiles, considering industry environment, competition, and internal capabilities. This data is further visualized using a color matrix (Tables 5 & 6) and utilized alongside state diagrams (Figure 2) to depict the workflow and manage tasks within the P2P lending process. Additionally, the MAS informs a novel Federated Risk-Based Access Control (FRkBAC) system that tailors access permissions (lending origination, disbursement, etc.) based on dynamic risk assessments of industry trends and individual borrower profiles. This data-driven approach fosters trust within the P2P ecosystem and represents a significant advancement in decentralized finance risk management compared to traditional methods. Doi: 10.28991/ESJ-2024-08-06-05 Full Text: PDF

RESEARCH STATUS AND PROSPECTS OF SHARIAH COMPLIANCE IMPACT ON FIRM RISK, PERFORMANCE AND RESILIENCE

This article performs a bibliometric analysis on the impacts of Shariah compliance on firms’ risk, performance, and resilience. Using Scopus database, a total of 1063 documents have been identified and analyzed utilizing VOSviewer. In the analysis, we evaluate co-authorship, keyword co-occurrence, and citation to identify influential authors, countries, and sources. The findings reveal a consistent increase in publications, particularly noticeable from 2014 onwards, indicating a growing interest in this research domain. Notably, the study highlights the pivotal role of Shariah compliance in enhancing corporate governance, financial risk management, and sustainable development, thus directly impacting the risk, performance, and resilience of firms. Globally it seems that by adhering to Shariah principles, firms can mitigate risks effectively, enhance their performance, and strengthen their capacity to withstand market fluctuations and economic uncertainties, thereby improving their overall resilience. These findings underscore the global significance of Shariah-compliant finance and emphasize the imperative for further research to deepen the understanding of its implications for firms.

Integrating AI in financial risk management: Evaluating the effects of machine learning algorithms on predictive accuracy and regulatory compliance

This research focuses on adopting ML models in risk management and how such factors influence predictive abilities and compliance with relevant rules. With more financial institutions using some of these advanced AI technologies in their decision-making capacities, a clear understanding of their effectiveness and what legal compliance would mean for their growth becomes vital. This research presents a comprehensive literature review of traditional risk management methods compared to the newer, AI-based methodologies by meticulously evaluating difficult standard measurements, including accuracy, precision, and recall. Further, the research analyses the compliance risks that arise with AI, especially concerning significant regulations such as Basel III and GDPR, which are essential in preserving financial stability and customer confidence. The study shows that applying AI approaches enhances predictive efficiency to a very high degree and the pressing and major legal concerns that institutions face. Moreover, the studies reveal the beneficial sectors for applying machine learning for operational risk management and provide guidelines for employing AI. To improve and strengthen risk management approaches and guarantee strict compliance with current and future implementing regulations, this study offers pertinent information to current discourses regarding the future of finance within the rising context of technological advancements.

Unveiling the effect of non-performing loans on lending behaviour: Evidence from Vietnam’s banking system

This study shows how non-performing loans (NPLs) and other variables specific to banks affect loan growth and the risk-weighted composition of assets in the Vietnamese banking sector. We use a large panel dataset of Vietnamese commercial banks from 2008 to 2022 and different econometric estimators to look at how NPLs, loan loss provisions (LLPs), and bank-specific factors affect how banks lend money. Additionally, we conduct a sensitivity analysis to bolster the robustness of our empirical findings. Our analysis reveals a statistically significant inverse relationship between NPLs/LLPs and loan growth rates, underscoring the critical role of effective credit risk management. Furthermore, institutional characteristics such as bank size, liquidity position, profitability metrics, and tangible asset ratios substantially influence lending patterns and risk exposure. Conversely, macroeconomic and broader institutional factors demonstrate mixed effects, with many exhibiting statistical insignificance. This study addresses a notable gap in the extant literature concerning the determinants of bank lending behaviour in Vietnam, offering nuanced insights into the role of credit risk indicators and bank-specific attributes. Our findings yield substantial implications for regulatory bodies, policymakers, and banking executives in their pursuit of enhancing the stability and operational efficiency of Vietnam's financial system. By elucidating these relationships, our research contributes to the ongoing discourse on banking sector dynamics in emerging economies. It provides an empirical foundation for informed decision-making in financial policy and risk management strategies.

Financial Risk Management Based on Big Data Technology

Financial Risk Management Based on Big Data Technology

Determinan Financial Distress: A Study on The Property and Real Estate Sector in The Indonesia Stock Exchange

The property and real estate sector plays a crucial role in the Indonesian economy but faces significant risks of financial distress. This study aims to identify the determinants of financial distress within this sector, focusing on companies listed on the Indonesia Stock Exchange between 2018 and 2022. The research examines the effects of profitability, liquidity, leverage, interest rates, and inflation on financial distress. Using a quantitative approach, this study applies statistical techniques and regression analysis to secondary data from various property and real estate companies. The findings reveal that: (i) profitability has a negative impact on financial distress, indicating that lower profitability increases the likelihood of financial difficulties; (ii) liquidity also shows a negative relationship with financial distress, meaning reduced liquidity elevates financial risk; (iii) leverage negatively influences financial distress, suggesting that higher debt levels exacerbate financial instability; (iv) rising interest rates contribute to financial distress by increasing borrowing costs, thereby straining company resources; and (v) inflation worsens financial distress by eroding purchasing power and dampening property demand. This study provides valuable insights for stakeholders, including investors and policymakers, to enhance their understanding of financial risk management in the property and real estate sector. The novelty of this research lies in its specific focus on a critical period and sector within the Indonesian economic landscape, addressing key areas of concern for financial stability

Time-Varying Spillover Effects of Carbon Prices on China’s Financial Risks

As China’s financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China’s financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China’s financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China’s financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China’s financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes.

Influence of ESG on corporate debt default risk: An analysis of the dual risk scenarios

In the context of sustainable development and high-quality corporate growth, this study examines the impact of Environmental, Social, and Governance (ESG) performance on corporate debt default risk among Chinese A-share listed corporates. It explores the mechanisms by which ESG performance influences debt risk, including diversification of funding sources, optimized capital utilization, and improved market supply–demand dynamics. The mitigating effect of ESG on debt risk is more pronounced in state-owned enterprises, large corporations, and corporations with robust internal controls, especially under conditions of low marketization and adverse macroeconomic circumstances. Moreover, in light of the global emphasis on climate risks, this study assesses their significant influence on debt default risk, particularly through the environmental aspect of ESG. Innovatively, this study incorporates dual climate risk scenarios to comprehensively analyze the interaction between ESG performance and debt default risk. Findings indicate that ESG performance significantly lowers debt risk in corporates facing higher internal climate challenges, while the effects on external supply chain climate risks are less evident. These insights contribute to the theoretical understanding of ESG and financial risk management and provide practical strategies for corporates pursuing sustainability in an unstable climatic environment.

Implementation of Funding Risk Management in Sharia Banking

Activities in the banking world are very susceptible to risk. Especially in fundraising activities because it is closely related to the bank's operations itself. To minimize risk, risk management is needed. This purpose of this paper to determine the implementation of funding risk management at BSI KCP Kediri Hayam Wuruk. The research method used is qualitative research using field observations to collect relevant data and analyze it comprehensively. Data collection was carried out through interviews with funding officer Bank BSI KCP Kediri Hayam Wuruk, and literature studies taken from literature sources relevant to the topic. The research results show that there are 3 funding risk management, namely liquidity risk, reputation risk and return risk. The ways to manage these risks are, 1. Liquidity risk by identifying funding and asset structures, 2. Reputation risk by improving service and managing customer complaints in a timely manner and complying with regulations according to sharia principles, 3. Return risk by managing customer expectations and improving the best service to customers.

Research on the Application of Big Data Technology

The swift advancement of information technology has positioned big data as a crucial instrument for fostering innovation and enhancing optimization across diverse sectors. This essay seeks to investigate the specific applications and effects of big data technology in the financial, healthcare, and educational domains. The research background is that the wide application of big data technology has a profound impact on business processes and decision-making methods in various industries. Research topics cover big data for personalized learning and data parsing in education, disease prediction and personalized medicine in healthcare, and risk management and fraud detection in finance. This paper adopts the method of literature review and case analysis, and the research objects include educational platforms, medical institutions and financial institutions. The data comes from industry reports, academic studies and real-world cases. The findings indicate that big data technology has a noteworthy impact in improving service quality and decision-making efficiency, but concurrently faces issues like protecting data privacy, technical complexity and data quality. The conclusions point out that while big data technology is driving the industry, these challenges need to be addressed to achieve its maximum potential.

A Comprehensive Approach to Financial Risk Management: Analysis of Regulation, Innovation and Sustainability Through Semantic Literature Reviews

Financial risk management is an important element in maintaining global economic stability. This study explores the relationship between regulation, technological innovation, and sustainability as three main pillars in modern financial risk management. Using the Semantic approach Literature Review (SLR), this study analyzes the literature from 50 selected scientific articles published between 2018 and 2024. The results of the study show that regulations such as Solvency II and IFRS 17 strengthen transparency and accountability, while innovative technologies such as parametric insurance and resilience bonds increase the efficiency of risk management. In addition, sustainability, which is realized through initiatives such as green insurance and sharia insurance, is a key pillar in mitigating systemic risk.However, the study identified a number of challenges, including fragmentation of regulations across countries, limited access to technology in developing countries, and moral hazard in implementing sustainability. To overcome these obstacles, a collaborative strategy involving governments, the private sector, and the international community is needed to harmonize global regulations, strengthen technology infrastructure, and improve technology and sustainability literacy. This study contributes to presenting a comprehensive financial risk management framework by recommending strengthening the synergy between regulation, technology, and sustainability. This study also provides practical guidance to address global challenges in financial risk management, while also providing a basis for further in-depth research on specific sectors, geographic regions, and the integration of technology and sustainability.

THE LEGAL PROTECTION OF HOSPITALS IN THE CASE OF NON-BPJS PATIENTS WHO ABSCOND AS A FORM OF BAD FAITH DUE TO THEIR INABILITY TO PAY

Hospitals often encounter financial difficulties when non-BPJS patients evade payment due to financial incapacity, with some institutions unfairly shifting the financial burden onto doctors. This situation raises serious legal, ethical, and employment concerns, especially in light of Law No. 17 of 2023 on Health, which mandates institutional responsibility for patient management. Meanwhile, Artificial Intelligence (AI) offers promising solutions for predicting and mitigating financial risks by identifying high-risk patients, though it introduces new challenges regarding data privacy, accountability, and liability. This study aims to explore the legal protections available to doctors when patients abscond without paying, and to propose a legal framework for integrating AI in healthcare to prevent such incidents. Using a normative legal approach, the research analyzes key provisions of Law No. 17/2023, employment regulations, and relevant case studies involving financial disputes between doctors and hospitals. The results show that imposing financial liability on doctors not only breaches employment principles but also contradicts healthcare regulations. Furthermore, AI can improve financial risk management by helping hospitals predict and prevent non-payment cases, though its implementation requires clear legal guidelines to avoid unintended consequences for medical staff. In conclusion, hospitals must bear financial responsibility for unpaid patient bills to protect doctors' legal rights. Additionally, a comprehensive regulatory framework for AI is essential to ensure that the technology is implemented fairly, safeguarding both healthcare professionals and patient interests.

Corporate Financial Risk Trend Prediction Using ARIMA-Based Machine Learning: A Semantic Literature Review

This research discusses the role of artificial intelligence (AI) technology in improving company financial risk predictions through the application of the Autoregressive Integrated Moving Average (ARIMA) model supported by machine learning. Using systematic literature analysis, this research explores how ARIMA, with the help of AI, can be applied in financial risk management to identify short-term and long-term trends, detect financial anomalies, and improve the quality of decision making. ARIMA models based on machine learning enable faster and more accurate identification of abnormal financial patterns, which is very important in helping companies respond to risks that have the potential to disrupt financial stability. Key findings show that the combination of ARIMA and AI not only makes it easier to analyze large and complex financial data, but also expands the potential of data-driven financial risk management by providing deeper insights. By integrating machine learning, ARIMA becomes more effective in handling dynamic and fluctuating financial data, so companies can anticipate risks more proactively. This study underscores the enormous potential of AI and ARIMA in helping companies build adaptive, high-precision risk prediction systems, offering new opportunities to strengthen financial resilience in an ever-changing business environment.