System Risk Model Research Articles

A new prognostic scoring system for newly diagnosed multiple myeloma in the era of new drugs.

We developed a new predictive staging system to explore the heterogeneity of survival in newly diagnosed multiple myeloma (NDMM) patients in the real world. In this retrospective study, we evaluated the predictive value of cytogenetic abnormal and clinical data in 375 patients with NDMM at our center. Established a weighted MM prognostic scoring system risk model and validated its predicted PFS and OS by external cohort. Elevated lactate dehydrogenase levels (1 point), international staging system stage II/III (1 point), 1q21+ ≥ 52.75% (0.5 point), del (17p) ≥ 3.5% (0.5 point), and t (14;16) ≥ 35.25% (1 point) had independent prognostic significance. Patients were further divided into three risk groups: low (I) (score 0-0.5, 16.5%), intermediate (II) (score 1, 46.7%), and high (III) (score 1.5-3, 36.8%). In the training cohort, the 3-year PFS was 79.5% vs. 65.3% vs. 40.3% (p < 0.001), and the 3-year OS was 87.7% vs.70.1% vs. 55% (p < 0.001) for the three risk groups. In the external validation cohort, the 3-year PFS was 85.5% vs.61.2% vs. 43.1% (p < 0.001) and the 3-year OS was 91.4% vs.83.5% vs. 56.9% (p < 0.001) for the three risk groups. The risk stratification of this model shows good discrimination and calibration, and its application in clinical practice can improve the risk assessment of patients with NDMM and guide personalized treatment strategies.

Identification of two distinct head and neck squamous cell carcinoma subtypes based on fatty acid metabolism-related signatures: Implications for immunotherapy and chemotherapy.

The dysregulation of lipid metabolism is a critical factor in the initiation and progression of tumors. In this investigation, we aim to characterize the molecular subtypes of head and neck squamous cell carcinoma (HNSCC) based on their association with fatty acid metabolism and develop a prognostic risk model. The transcriptomic and clinical data about HNSCC were obtained from public databases. Clustering analysis was conducted on fatty acid metabolism genes (FAMG) associated with prognosis, utilizing the non-negative matrix factorization algorithm. The immune infiltration, response to immune therapy, and drug sensitivity between molecular subtypes were evaluated. Differential expression genes were identified between subtypes, and a prognostic model was constructed using Cox regression analyses. A nomogram for HNSCC was constructed and evaluated. Thirty FAMGs have been found to exhibit differential expression in HNSCC, out of which three are associated with HNSCC prognosis. By performing clustering analysis on these 3 genes, 2 distinct molecular subtypes of HNSCC were identified that exhibit significant heterogeneity in prognosis, immune landscape, and treatment response. Using a set of 7778 genes that displayed differential expression between the 2 molecular subtypes, a prognostic risk model for HNSCC was constructed comprising 11 genes. This model has the ability to stratify HNSCC patients into high-risk and low-risk groups, which exhibit significant differences in prognosis, immune infiltration, and immune therapy response. Moreover, our data suggest that this risk model is negatively correlated with B cells and most T cells, but positively correlated with macrophages, mast cells, and dendritic cells. Ultimately, we constructed a nomogram incorporating both the risk signature and radiotherapy, which has demonstrated exceptional performance in predicting prognosis for HNSCC patients. A molecular classification system and prognostic risk models were developed for HNSCC based on FAMGs. This study revealed the potential involvement of FAMGs in modulating tumor immune microenvironment and response to treatment.

Development and validation of an individualized and weighted Myeloma Prognostic Score System (MPSS) in patients with newly diagnosed multiple myeloma.

Current standard predictive models of disease risk do not adequately account for the heterogeneity of survival outcomes in patients with new-diagnosed multiple myeloma (NDMM). In this retrospective, multicohort study, we collected clinical and genetic data from 1792 NDMM patients and identified the prognostic impact of all features. Using the top-ranked predictive features, a weighted Myeloma Prognostic Score System (MPSS) risk model was formulated and validated to predict overall survival (OS). In the training cohort, elevated lactate dehydrogenase level (LDH), International Staging System (ISS) Stage III, thrombocytopenia, and cumulative high-risk cytogenetic aberration (HRA) numbers were found to have independent prognostic significance. Each risk factor was defined as its weighted value respectively according to their hazard ratio for OS (thrombocytopenia 2, elevated LDH 1, ISS III 2, one HRA 1, and ≥2 HRA 2, points). Patients were further stratified into four risk groups: MPSS I (22.5%, 0 points), II (17.6%, 1 points), III (38.6%, 2-3 points), and IV (21.3%, 4-7 points). MPSS risk stratification showed optimal discrimination, as well as calibration, of four risk groups with median OS of 91.0, 69.8, 45.0, and 28.0 months, for patients in MPSS I to IV groups (p < .001), respectively. Importantly, the MPSS model retained its prognostic value in the internal validation cohort and an independent external validation cohort, and exhibited significant risk distribution compared with conventional prognostic models (R-ISS, R2-ISS, and MASS). Utilization of the MPSS model in clinical practice could improve risk estimation in NDMM patients, thus prompting individualized treatment strategies.

A Study of Systemic Financial Risk Prediction Models in the Insurance Industry

Abstract The influence of the stability of the insurance industry on the strength of the financial market cannot be ignored, and the healthy and strong growth of the insurance industry plays an indispensable role in maintaining the stability of the whole society. To fully analyze the influence of the stability of the insurance industry on the strength of the financial market, this paper proposes a prediction model of systemic financial risk. The insurance industry’s systemic financial risk prediction indicators are determined based on the principle of selecting systemic risk factors and indicators. After preprocessing the collected relevant research data, relevant research variables are determined, a Logit prediction model for the systemic risk of the insurance industry is constructed, and a case study is conducted. The data show that the average value of GDP during these 21 years is 379642.64, and it reaches about 2.4 times the average value in 2023. The insurance industry’s economy grows steadily and rapidly. It reached the maximum response in the 10th period (0.04616), i.e., the development of China’s life insurance industry, which has a more obvious role in promoting the development of the economy. It is predicted that China’s financial risk in the insurance industry in the second half of 2024 will have a great probability of being in a low-risk state. Its predicted probability value is close to 1. The insurance industry’s healthy development has a positive impact on economic development, and the insurance industry’s stable development is of great importance to economic development, as evidenced by this study.

Development and Validation of an Individualized and Weighted Prognostic Model in Patients with Newly Diagnosed Multiple Myeloma

Introduction Multiple myeloma (MM) is a malignancy of terminally differentiated antibody-secreting plasma cells in the bone marrow with highly variable survival outcomes. Precise risk stratification not only plays a vital role in predicting patient prognoses, but critically can be used to develop risk-adapted treatment regimens. However, current risk stratification models do not adequately account for the heterogeneity of patients with new-diagnosed multiple myeloma (NDMM). Methods In this retrospective, multicohort study, we collected clinical data from 1792 NDMM patients and formulated a weighted Myeloma Prognostic Score System (MPSS) risk model to predict overall survival (OS). Construction of the MPSS model was carried out based on the National Longitudinal Cohort of Hematological Diseases in China (NCT04645199) cohort, external validation cohort was created using TT2, TT3a, and TT3b cohorts from a series of randomized prospective studies from the University of Arkansas for Medical Sciences (UAMS). Results Each risk factor was defined as its weighted value respectively according to their hazard ratio for OS (thrombocytopenia 2, elevated LDH 1, ISS III 2, one high-risk cytogenetic aberration [HRA] 1, and ≥2 HRA 2 points). In the training cohort, patients were furtherly stratified into four risk groups: MPSS I (22.5%, 0 points), II (17.6%, 1 points), III (38.6%, 2-3 points), and IV (21.3%, 4-7 points). MPSS risk stratification showed optimal discrimination, as well as calibration, of four risk groups with median OS of 91.0, 69.8, 45.0, and 28.0 months, for patients in MPSS I to IV groups (P&amp;lt; 0.001), respectively. Importantly, the MPSS model retained its prognostic value in the internal validation cohort and an independent external validation cohort, and exhibited significant risk distribution compared with conventional prognostic models (R-ISS, R2-ISS, and MASS). Conclusions In conclusion, we formulated and validated the MPSS risk model to predict the prognosis of patients with MM using readily available standard clinical and genetic test data. The established MPSS profile shows a better performance in risk discrimination than the current R-ISS, R2-ISS, and MASS. A score-based risk stratification is derived, and identifies patients with high and ultra-high risk of death after diagnosis and may therefore aid the development of more personalized treatment strategies, especially for patients for whom current therapies are likely to fail.

Mathematical Modeling and Stability Analysis of Systemic Risk in the Banking Ecosystem

This paper investigates the dynamics of systemic risk in banking networks by analyzing equilibrium points and stability conditions. The focus is on a model that incorporates interactions among distressed and undistressed banks. The equilibrium points are determined by solving a reduced system of equations, considering both homogeneous and heterogeneous scenarios. Local and global stability analyses reveal conditions under which equilibrium points are stable or unstable. Numerical simulations further illustrate the dynamics of systemic risk, while the theoretical findings offer insights into the behavior of distressed banks under varying conditions. Overall, the model enhances our understanding of systemic financial risk and offers valuable insights for risk management and policymaking in the banking sector.

Risk evolution model of marine traffic via STPA method and MC simulation: A case of MASS along coastal setting

Marine traffic safety for Maritime Autonomous Surface Ship (MASS)is affected by the maritime environment and complex human-machine technical systems, and it is necessary to reveal its risk evolution characteristics involving new technology. The novel risk evolution model based on the system-theoretic process analysis (STPA)method for specific scenarios is established to determine the risk mechanism in random processes. First, by combining Markov chain (MC) with cloud model based on the information transmission path, the Markov process hypothesis is proposed to devise a coupling effect model of system components and external interference environment. Second, operation and control modes for MASS under different scenarios based on STPA are constructed and analyzed, which are based on clarifications regarding two models for the parallel control of MASS. Third, a systemic risk model using safety control methodology is constructed considering the operation scenario of MASS. Last and not least, the risk performance in the MASS navigation process is simulated to reveal the emergence characteristics of ship navigation process risk after applying specific scenarios under three modes. Application examples show that the process risk emergence of MASS navigation is characterized by the dependence on safety information transmission path. Different transmission paths lead to inconsistent risk evolution results. The randomness and complexity of external environmental disturbances are the main factors involved in the formation of navigation risks in MASS.

Propagation of minimality in the supercooled Stefan problem

Supercooled Stefan problems describe the evolution of the boundary between the solid and liquid phases of a substance, where the liquid is assumed to be cooled below its freezing point. Following the methodology of Delarue, Nadtochiy and Shkolnikov, we construct solutions to the one-phase one-dimensional supercooled Stefan problem through a certain McKean-Vlasov equation, which allows to define global solutions even in the presence of blow-ups. Solutions to the McKean-Vlasov equation arise as mean-field limits of particle systems interacting through hitting times, which is important for systemic risk modeling. Our main contributions are: (i) we prove a general tightness theorem for the Skorokhod M1-topology which applies to processes that can be decomposed into a continuous and a monotone part. (ii) We prove propagation of chaos for a perturbed version of the particle system for general initial conditions. (iii) We prove a conjecture of Delarue, Nadtochiy and Shkolnikov, relating the solution concepts of so-called minimal and physical solutions, showing that minimal solutions of the McKean-Vlasov equation are physical whenever the initial condition is integrable.

Systemic Risk with Multi-Channel Risk Contagion in the Interbank Market

The systematicness of banks is an important driver of financial crisis. Overlapping portfolios and assets correlation of banks’ investment are important reasons for systemic risk contagion. The existing systemic risk models are all analyzed from one aspect and cannot reflect the real situation of the banking system. In the present paper, considering the overlapping portfolios and assets correlation, a contagion network model with multi-channel risk is proposed, which is with interbank lending (direct contagion channel), overlapping portfolios (indirect contagion channel), and assets correlation (indirect contagion channel). In addition, the model takes investment risk as an impact factor and learns the operation rules of the banking system to help banks compensate for liquidity through asset depreciation. Based on the proposed model, the effects of assets correlation, assets diversity, assets investment strategy, interbank network structure, and the impact of market density on risk contagion are studied and analyzed quantitatively. The method in this paper can more truly reflect the banking system risk than the existing model. This paper provides a solution for quantitative analysis of systemic risk, which provides powerful tools for macroprudential stress testing and a reference for regulatory authorities to prevent systemic risk.

Convergence of a time-stepping scheme to the free boundary in the supercooled Stefan problem

The supercooled Stefan problem and its variants describe the freezing of a supercooled liquid in physics, as well as the large system limits of systemic risk models in finance and of integrate-and-fire models in neuroscience. Adopting the physics terminology, the supercooled Stefan problem is known to feature a finite-time blow-up of the freezing rate for a wide range of initial temperature distributions in the liquid. Such a blow-up can result in a discontinuity of the liquid-solid boundary. In this paper, we prove that the natural Euler time-stepping scheme applied to a probabilistic formulation of the supercooled Stefan problem converges to the liquid-solid boundary of its physical solution globally in time, in the Skorokhod M1 topology. In the course of the proof, we give an explicit bound on the rate of local convergence for the time-stepping scheme. We also run numerical tests to compare our theoretical results to the practically observed convergence behavior.

Understanding the current global regime shift and the standing of the macro-financial system resilience

Understanding the current global regime shift and the standing of the macro-financial system resilience

Generation and evolution mechanism of systemic risk (SR) induced by extreme precipitation in Chinese Urban system: A case study of Zhengzhou “7 20” incident

With global warming, the risk of natural disasters is further aggravated, and extreme weather is becoming stronger and more frequent, which is more likely to induce SR. As a comprehensive and complex giant system, cities are typical vulnerable and risk areas affected by climate change. To promote the targeted prevention and control of SR induced by extreme weather in urban system, this paper studies the generation and evolution mechanism of SR induced by extreme precipitation in Chinese urban system. Firstly, from a multidisciplinary perspective, the general process of SR generation and evolution in urban system is proposed. Secondly, the causation analysis of SR generation and evolution in Chinese urban system induced by extreme precipitation is conducted from the perspective of system safety theory and systematic management theory, including causation factors and paths. Thirdly, combined with the reality, the generation and evolution mechanism model of SR induced by extreme precipitation in Chinese urban system is established. Finally, an empirical study is carried out with Zhengzhou “7·20” incident as an example. The results show that generation and evolution of SR is caused by the continuous impact of the Swiss cheese effect, the trajectory intersection effect, and the cascading effect. In the future, targeted responding measures should be made, including the balance of urban development and SR prevention, the design of SR generation prevention mechanism, the design of SR early warning mechanism, and the design of collaborative linkage mechanism.

Systemic risk models for disjoint and overlapping groups with equilibrium strategies

Abstract We analyze the systemic risk for disjoint and overlapping groups of financial institutions by proposing new models with realistic game features. Specifically, we generalize the systemic risk measure proposed in [F. Biagini, J.-P. Fouque, M. Frittelli and T. Meyer-Brandis, On fairness of systemic risk measures, Finance Stoch. 24 (2020), 2, 513–564] by allowing individual banks to choose their preferred groups instead of being assigned to certain groups. We introduce the concept of Nash equilibrium for these new models, and analyze the optimal solution under Gaussian distribution of the risk factor. We also provide an explicit solution for the risk allocation of the individual banks and study the existence and uniqueness of Nash equilibrium both theoretically and numerically. The developed numerical algorithm can simulate scenarios of equilibrium, and we apply it to study the banking structure with real data and show the validity of the proposed model.

Coal mine safety management index system and environmental risk model based on sustainable operation

With the increasing use of coal mine resources, some modern large coal mines have advanced and perfect safety equipment, reasonable personnel structure, and perfect rules and regulations, but why do major accidents start to occur? In addition to the complex geological conditions of China's coal mines, another important factor has attracted our attention, that is, the efficiency of coal mine safety management. This paper aims to study the coal mine safety management index system and environmental risk model based on sustainable operation. Based on the coal mine safety index of sustainable management, this paper proposes a mining algorithm for coal mine safety text classification and hazard source association rules. And this paper analyzes the safety management efficiency of the safety index system based on DEA and DEA-Malmquist. The analysis results prove that the evaluation values of coal mine safety management of the selected 20 coal mining enterprises from 2012 to 2017 were 0.602, 0.645, 0.570, 0.667, 0.771 and 0.705, respectively. The interval range of the data is between [0.55, 0.85], indicating that the evaluation level of safety management efficiency of Chinese coal mining enterprises is general and at a medium level.

Network formation and systemic risk

This paper introduces a model of endogenous network formation and systemic risk. In it, firms form joint ventures called ‘links’ which are subsequently subjected to either good or bad shocks. Bad shocks incentivize default. Links yield full benefits only if the counter-party does not subsequently default on the project. Accordingly, defaults triggered by bad shocks render firms insolvent and defaults propagate via links. The model yields three insights. First, stable networks with ex-ante identical agents exhibit a core–periphery structure. Second, an increase in the probability of good shocks increases systemic risk. Third, because the network formed depends on the correlation between shocks to links, an observer who misconceives the correlation will underestimate the probability of system-wide default by a factor of a half.

ARE POLISH BANKS STABLE? A SYSTEMIC RISK ANALYSIS

The financial crisis that began in 2007 pointed out deficiencies in policy-makers’ responses to systemic risk. It turned out that not only individual bank insolvencies but also spillovers from negative externalities among entities can cause serious threats to the financial sector. During the last 10 years, many international and national initiatives were taken to strengthen the soundness of the financial system, introducing a macroprudential perspective to financial supervision. However, the recent COVID19 pandemic resulted in a serious negative shock for many economies and their financial sectors. In this paper, using the network model we try to analyse how these recent unexpected developments affected the Polish banking sector with systemic risk. To analyse Polish bank stability we developed a formal stress-testing framework based on the network model that allowed systemic risk identification, modelling and measurement. We tried to integrate analysis of time and the cross-sectional nature of systemic risk.

System Dynamics Model for Systematic Evaluation of China’s Financial Risk

Finance is becoming more important in the national economy. Maintaining financial stability is critical not only for the financial industry’s prosperity and development, but also for a country’s political, economic, and social development. This paper will look at the mechanisms that cause systemic risk to develop and evolve, as well as how to measure systemic financial risk in multiple dimensions. To begin, create a system for evaluating financial risk in a systematic manner. Second, using the AHP and CRITIC methods to determine various indicators and market weights, create a systemic financial risk evaluation model based on the system dynamics model, and calculate the system from 2010 to 2019 comprehensive financial risk index. Finally, simulation research is conducted using the system dynamics model of systemic financial risk, and the simulation results are analyzed. The findings show that China’s financial risk has been gradually increasing since 2016, with relatively small fluctuations in risk state.

An Equilibrium-Based Measure of Systemic Risk

This paper develops and implements an equilibrium model of systemic risk. The model derives a systemic risk measure, loss beta, in characterizing all too-big-to-fail banks using a capital insurance equilibrium. By constructing each bank’s loss portfolio with a recent accounting approach, we perform a comprehensive empirical study of this loss beta measure and document all TBTF banks from 2002 to 2019. Our empirical findings suggest a significant number of too-big-to-fail banks in 2018–2019.

Relationship Between Skeletal Muscle Mass to Visceral Fat Area Ratio and Cardiovascular Risk in Type 2 Diabetes.

PurposeEither visceral fat or muscle mass is identified to be correlated with cardiometabolic diseases, especially in type 2 diabetes (T2DM). But, the synergistical effect of visceral fat along with skeletal muscle on the risk of cardiovascular diseases (CVD) in T2DM still remains controversial. Thus, we investigated the relationship between skeletal muscle mass to visceral fat area ratio (SVR) and 10-yr CVD risk scores.Patients and MethodsA total of 291 T2DM patients aged 40–80 years were enrolled in the current study. SVR was evaluated based on bioelectrical impedance measurements. Both Framingham risk score system and China-PAR risk model were applied to estimate future 10-yr CVD risk in T2DM population.ResultsThe 10-yr CVD risk scores increased with the decreased SVR tertiles in T2DM (All P<0.001). SVR value was obviously lower in the high-risk group than that of low- or moderate-risk group (All P<0.05). However, no significant differences were observed in BMI among different CVD risk groups. Besides, SVR was correlated with Framingham risk score (r=−0.408; P<0.001) and China-PAR risk score (r=−0.336; P<0.001). HOMA-IR, triglycerides and blood pressure were also inversely related to SVR (All P<0.05). Furthermore, SVR value was independently correlated with both Framingham 10-yr CVD risk score (β=−0.074, P=0.047) and China-PAR risk score (β=−0.100, P=0.004) after adjustment for confounding factors, including age, gender, BMI, FPG, HbA1c, diabetes duration, albumin, creatinine, uric acid, smoking, blood pressure and blood lipid. The linear regression analysis was also conducted for men and women, respectively, indicating that the negative relationship between SVR and 10-yr CVD risk was observed in men but not in women.ConclusionT2DM populations who have lower SVR value are more likely to increase CVD risk. SVR levels show marked and inverse correlation with estimated 10-yr CVD risk in T2DM, indicating that SVR could be a valuable parameter to assess the risk of CVD events in clinical practice, especially in men.

A dynamic MST-deltaCoVaR model of systemic risk in the European insurance sector

Abstract This work is a response to the EIOPA paper entitled ‘Systemic risk and macroprudential policy in insurance’, which asserts that in order to evaluate the potential systemic risk (SR), the build-up of risk, especially risk arising over time, should be taken into account, as well as the interlinkages occurring in the financial sector and the whole economy. The topological indices of minimum spanning trees (MST) and the deltaCoVaR measure are the main tools used to analyse the systemic risk dynamics in the European insurance sector in the years 2005-2019. The article analyses the contribution of each of the 28 largest European insurance companies, including those appearing on the G-SIIs list, to systemic risk. Moreover, the paper aims to determine whether the most important contribution to systemic risk is made by companies with the highest betweenness centrality or the highest degree in the obtained MST.