Risks

Chililov, Abdula M.

doi: 10.3390/risks12090134pmid: N/A

The research determines the level of financial risk in the Russian healthcare system and identifies prospects for improving the current Russian practice of financial risk management in healthcare when providing high-tech medical care for sustainable development (using Russia as an example). The author summarizes the advanced experience of the top 20 largest healthcare organizations in Russia by revenue in 2022. Based on this experience, the author developed an SEM model of the financial risks in healthcare during the provision of high-tech medical care in Russia from a sustainable development perspective. The theoretical significance of the developed model lies in uncovering the previously unknown causal relationships between the implementation of the ICT, sustainable development support, and financial risks in healthcare. The model reveals a new market dimension of financial risks for healthcare organizations in Russia. The main conclusion is that implementing the ICT and support for sustainable development helps to reduce the financial risks in healthcare. The identified potential for reducing financial risks in providing high-tech medical care in Russia until 2026 is practically significant. This prospect can be practically applied as a roadmap for the digital modernization and sustainable development of healthcare until 2026, enhancing the state healthcare policy in Russia. The established systemic relationship between ICT implementation, sustainable development support, and financial risks in healthcare is of managerial importance because it will increase the predictability of the financial risks in the market dimension of healthcare in Russia. The newly developed approach to risk management in healthcare during the provision of high-tech medical care in Russia has expanded the instrumental framework of risk management for healthcare organizations in Russia and revealed further opportunities for improving its efficiency.

Uzhga-Rebrov, Oleg

doi: 10.3390/risks12090135pmid: N/A

In risk assessment, numerous subfactors influence the probabilities of the main factors. These main factors reflect adverse outcomes, which are essential in risk assessment. A Bayesian network can model the entire set of subfactors and their interconnections. To assess the probabilities of all possible states of the main factors (adverse consequences), complete information about the probabilities of all relevant subfactor states in the network nodes must be utilized. This is a typical task of probabilistic inference. The algorithm proposed by J. Pearl is widely used for point estimates of relevant probabilities. However, in many practical problems, including environmental risk assessment, it is not possible to assign crisp probabilities for relevant events due to the lack of sufficient statistical data. In such situations, expert assignment of probabilities is widely used. Uncertainty in expert assessments can be successfully modeled using triangular fuzzy numbers. That is why this article proposes a fuzzy version of this algorithm, which can solve the problem of probabilistic inference on a Bayesian network when the initial probability values are given as triangular fuzzy numbers.

Lindquist, W. Brent;Rachev, Svetlozar T.;Gnawali, Jagdish;Fabozzi, Frank J.

doi: 10.3390/risks12090136pmid: N/A

We present a unified, market-complete model that integrates both Bachelier and Black–Scholes–Merton frameworks for asset pricing. The model allows for the study, within a unified framework, of asset pricing in a natural world that experiences the possibility of negative security prices or riskless rates. Unlike the classical Black–Scholes–Merton, we show that option pricing in the unified model differs depending on whether the replicating, self-financing portfolio uses riskless bonds or a single riskless bank account. We derive option price formulas and extend our analysis to the term structure of interest rates by deriving the pricing of zero-coupon bonds, forward contracts, and futures contracts. We identify a necessary condition for the unified model to support a perpetual derivative. Discrete binomial pricing under the unified model is also developed. In every scenario analyzed, we show that the unified model simplifies to the standard Black–Scholes–Merton pricing under specific limits and provides pricing in the Bachelier model limit. We note that the Bachelier limit within the unified model allows for positive riskless rates. The unified model prompts us to speculate on the possibility of a mixed multiplicative and additive deflator model for risk-neutral option pricing.

Usman, Farha;Chan, Jennifer S. K.;Makov, Udi E.;Wang, Yang;Dong, Alice X. D.

doi: 10.3390/risks12090137pmid: N/A

We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving habits and establish premiums that accurately reflect their driving risk. To accomplish our goal, we employ the two-stage Poisson model, the Poisson mixture model, and the Zero-Inflated Poisson model to analyse the telematics data. These models are further enhanced by incorporating regularisation techniques such as lasso, adaptive lasso, elastic net, and adaptive elastic net. Our empirical findings demonstrate that the Poisson mixture model with the adaptive lasso regularisation outperforms other models. Based on predicted claim frequencies and drivers’ risk groups, we introduce a novel usage-based experience rating premium pricing method. This method enables more frequent premium updates based on recent driving behaviour, providing instant rewards and incentivising responsible driving practices. Consequently, it helps to alleviate cross-subsidization among risky drivers and improves the accuracy of loss reserving for auto insurance companies.

Xiao, Menghan;Guo, Xiaojing;Chen, Gonghang;Ji, Xiangfeng;Sun, Wenqing

doi: 10.3390/risks12090138pmid: N/A

Under the “dual carbon” goal, the core issue at present is to improve the environment while ensuring economic development. As a result, green finance, that is a tool that integrates finance and environmental protection, has shown increasingly significant carbon reduction effects. With the panel data of 30 provinces in China from 2012 to 2021 being the research object, this study employs a spatial Durbin model to examine the impact of green finance on carbon emissions and further discusses its mechanism effects. The empirical results indicate the following: firstly, the development of green finance effectively suppresses carbon emissions; secondly, by decomposing the spatial effect of green finance on carbon emissions, it is found that green finance also reduces carbon emissions in neighboring regions due to the spillover effects; finally, green finance can suppress carbon emissions through technological innovation and industrial structure upgrading. Therefore, it is imperative to actively engage in practical work related to green finance, to establish a sound system for green finance, and simultaneously, to enhance cooperation among regions in terms of green finance, in order to fully leverage its role in suppressing carbon emissions.

Iqbal, Farhat;Koutmos, Dimitrios;Ahmed, Eman A.;Al-Essa, Lulwah M.

doi: 10.3390/risks12090139pmid: N/A

The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging.

Abdlkareem Ibrahim, Narjis;Salehi, Mahdi;Amran Naji Al-Refiay, Hussen;Lari Dashtbayaz, Mahmoud

doi: 10.3390/risks12090140pmid: N/A

The current study aims to develop a financial stability model in Iraq; after reviewing the relevant literature and sources related to financial stability and considering Iraq’s social, economic, political, and cultural conditions, a conceptual model and a research questionnaire have been developed. Based on the developed conceptual model, macro variables at the level of the economy, micro variables at the level of companies, the environmental variables of companies, and corporate governance have been selected as model dimensions. Each dimension has several components, including several indicators; 39 indicators were measured through questions in 2024. The research questionnaire was subjected to the opinion of 21 experts with sufficient experimental and academic records on this subject, and by using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, the results were analyzed, and the final model was extracted. In this model, the scientific method used to analyze the results determines the weight of each dimension, component, and indicator. The results of this research show that the dimensions of corporate governance, the variables of the company environment, micro variables at the company level, and macro variables at the economic level with coefficients of 0.345, 0.251, 0.236, and 0.168, respectively, have the most significant impact on the ranking of the company’s financial stability. So far, research has yet to be conducted to present the financial stability model of Iraqi companies. Therefore, the present research is one of the first studies in this respect, which presents a model both qualitatively (by designing a questionnaire and conceptual model) and quantitatively (through a mathematical model) to measure financial stability that can help the development of science and knowledge in this field.

Jamotton, Charlotte;Hainaut, Donatien;Hames, Thomas

doi: 10.3390/risks12090141pmid: N/A

The K-means algorithm and its variants are well-known clustering techniques. In actuarial applications, these partitioning methods can identify clusters of policies with similar attributes. The resulting partitions provide an actuarial framework for creating maps of dominant risks and unsupervised pricing grids. This research article aims to adapt well-established clustering methods to complex insurance datasets containing both categorical and numerical variables. To achieve this, we propose a novel approach based on Burt distance. We begin by reviewing the K-means algorithm to establish the foundation for our Burt distance-based framework. Next, we extend the scope of application of the mini-batch and fuzzy K-means variants to heterogeneous insurance data. Additionally, we adapt spectral clustering, a technique based on graph theory that accommodates non-convex cluster shapes. To mitigate the computational complexity associated with spectral clustering’s O(n3) runtime, we introduce a data reduction method for large-scale datasets using our Burt distance-based approach.

Betzuen Álvarez, Amaia Jone;Betzuen Zalbidegoitia, Amancio

doi: 10.3390/risks12090142pmid: N/A

This paper aims to assess the return and downside risk of a decumulation portfolio established at the retirement age of a senior, with a determined lifetime horizon differentiated by the sex of the citizen. To measure the portfolio’s return and downside risk, two ratios conditioned by seniors’ risk attitude towards portfolio failure are employed: the downside Sortino ratio and the downside risk–return ratio. Unlike other research in the field, this manuscript provides three portfolio compositions catering to different senior investment profiles: aggressive, moderate, and conservative. Additionally, it offers a decumulation horizon conditioned by the sex-specific life expectancy of the individual, instead of offering different scenarios for conducting a sensitivity analysis. Lastly, this study was conducted across three socioeconomically distinct countries: the US, Spain, and Japan. The results clearly demonstrate that both sex and nationality significantly influence the selection of the optimal decumulation portfolio composition aimed at exhausting funds by the senior’s demise.

García-Nieto, Manuel;Bueno-Rodríguez, Vicente;Ramón-Jerónimo, Juan Manuel;Flórez-López, Raquel

doi: 10.3390/risks12090143pmid: N/A

This study examines risk factors in mergers and acquisitions (M&As) identified in the recent literature, addressing the following question: “What risk factors associated with M&A transactions are discussed in the recent academic literature?” A semi-systematic literature review was conducted using a comprehensive search strategy with targeted keywords related to M&A risks. Papers from 2020 to 2024 were selected based on quality and relevance, with detailed review of abstracts and titles. Co-occurrence analysis using VOSviewer software (version 1.6.20) was applied to categorize key themes. The review of 118 papers identified four main risk categories: information asymmetry; performance and corporate reputation; litigation and investor protection; and geopolitical factors. Findings reveal complex interdependencies among these risks, highlighting the need for a holistic approach to risk management. Corporate social responsibility (CSR) is crucial for mitigating risks, improving transparency, and enhancing reputation. This study offers recommendations for better financial disclosures, robust environmental, social and governance strategies, and the integration of digital finance technologies as blockchain in M&A activity. Future research should include longitudinal studies on M&A risk dynamics, case studies on corporate governance, advanced valuation methods, and comparative analyses across regions and industries, focusing on emerging technologies like AI and blockchain.