Risk Measures Research Articles

A New Modified-X family of distributions with applications in modeling biomedical data

In the present article, we derive a new Modified-X (NM-X) family of probability distributions by using a popular approach known as the T-X method. For the NM-X family, some of the main distributional (or statistical) properties such as quantile function, identifiability property, order statistics, residual life and reverse residual life, moments, and moments generating function are determined. A well-known method of estimation such as the Maximum Likelihood estimation method is used for estimating the model parameters of the introduced family of distributions. A Monte Carlo simulation method is incorporated to judge the performance of the estimators with different selected values of parameters. To check the tails behaviors of the proposed model, the risk measurements such as value-at-risk (VaR), tailed-value-at-risk (TVaR), tailed-variance (TV), and tailed-variance-premium (TVP) are investigated. For illustrative purposes, a special sub-model of the proposed class is derived and named a New Modified Weibull distribution (abbreviated as the NM-Weib). Furthermore, four applications from the field of medicine are analyzed to demonstrate the application, and effectiveness of the NM-Weib distribution. By adopting different diagnostic criteria, the NM-Weib distribution is compared with Weibull (Weib) distribution, Alpha Power Transformed-Weib (APTra-Weib) distribution, new Exponent-Weib (NExpo-Weib) distribution, Flexible Reduced Logarithmic-Weib (FRLog-Weib) distribution, Marshal Olkin-Weib (MO-Weib) distribution, and Kumaraswamy-Weib (K-Weib) distribution. Finally, based on the diagnostic criteria, it is observed that the NM-Weib distribution provides a suitable and best fit for each considered biomedical data set.

Evaluating Density Forecasts Using Weighted Multivariate Scores in a Risk Management Context

AbstractScoring rules are commonly applied to assess the accuracy of density forecasts in both univariate and multivariate settings. In a financial risk management context, we are mostly interested in a particular region of the density: the (left) tail of a portfolio’s return distribution. The dependence structure between returns on different assets (associated with a given portfolio) is usually time-varying and asymmetric. In this paper, we conduct a simulation study to compare the discrimination ability between the well-established scores and their threshold-weighted versions with selected regions. This facilitates a comprehensive comparison of the performance of scoring rules in different settings. Our empirical applications also confirm the importance of weighted-threshold scores for accurate estimates of Value-at-risk and related measures of downside risk.

The Cyber Risk Premium

Cyber risk is an important emerging source of risk in the economy. To estimate its impact on the asset market, we use machine learning techniques to develop a firm-level measure of cyber risk. The measure aggregates information from a rich set of firm characteristics and shows superior ability to forecast future cyberattacks on individual firms. We find that firms with higher cyber risk earn higher average stock returns. When these firms underperform, cybersecurity experts tend to have higher concerns about cyber risk, and cybersecurity exchange-traded funds outperform. Further tests strengthen the identification of the cyber risk premium. This paper was accepted by William Cong, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02056 .

Analysis and evaluation of explainable artificial intelligence on suicide risk assessment

This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.

Risk of a post-colonoscopy colorectal cancer in patients with diverticular disease: a population-based cohort study.

Post-colonoscopy colorectal cancers (PCCRCs) may account for up to 30% of all colorectal cancers (CRCs) diagnosed in patients with diverticular disease; however, absolute and relative risks of PCCRC among these patients undergoing colonoscopy remain unknown. We performed a cohort study (1995-2015) including patients with and without diverticular disease who underwent colonoscopy. We calculated 7-36-month cumulative incidence proportions (CIPs) of PCCRC. We used Cox proportional hazards regression models to compute hazard ratios (HRs) of PCCRC, comparing patients with and without diverticular disease, as a measure of relative risk. We calculated 3-year PCCRC rates, as per World Endoscopy Organization recommendations, to estimate the proportion of CRC patients with and without diverticular disease who were considered to have PCCRC. We stratified all analyses by PCCRC location. We observed 373 PCCRCs among 56 642 patients with diverticular disease and 1536 PCCRCs among 306 800 patients without diverticular disease. The PCCRC CIP after first-time colonoscopy was 0.45% (95%CI 0.40%-0.51%) for patients with and 0.36% (95%CI 0.34%-0.38%) for patients without diverticular disease. Comparing patients with and without diverticular disease undergoing first-time colonoscopy, the adjusted HR was 0.84 (95%CI 0.73-0.97) for PCCRC and 1.23 (95%CI 1.01-1.50) for proximal PCCRCs. The 3-year PCCRC rate was 19.0% (22.3% for proximal PCCRCs) for patients with and 6.5% for patients without diverticular disease. Although the absolute risk was low, the relative risk of proximal PCCRCs may be elevated in patients with diverticular disease undergoing colonoscopy compared with patients without the disease.

Value-at-Risk Effectiveness: A High-Frequency Data Approach with Semi-Heavy Tails

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value-at-Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning between 1 January 2017 and 25 October 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing the observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.

Bank liquidity creation, loan concentration and liquidity risk: a comparative analysis of dual banking system

Purpose Keeping in view the robust growth of Islamic banking around the globe, this study aims to comparatively analyze the association between liquidity creation and liquidity risk for Islamic banks (IBANs) and conventional banks (CBANs) in Pakistan and Malaysia over a period of 2004–2021. The moderating role of bank loan concentration on the aforementioned relationship is also studied. Design/methodology/approach Regression estimation methods such as fixed effect, random effect and generalized least square are deployed for obtaining results. Liquidity creation Burger Bouwman measure (cat fat and noncat fat) and Basel-III liquidity risk measure (liquidity coverage ratio) are also used. Findings The results give us insight that liquidity creation is positively and significantly related to liquidity risk in both IBANs and CBANs of Pakistan and Malaysia. This relationship has been moderated negatively (reversed) and significantly by credit concentration showing the importance of risk management and loan portfolio concentration. Practical implications It is analyzed that during the process of liquidity creation, IBANs in Pakistan faced more liquidity risk for both on and off-balance sheet transactions in the presence of moderation of loan concentration than IBANs in Malaysia necessitating strategic policy-making for important aspects of liquidity risk management and loan concentration while creating liquidity. Originality/value Such studies comparing IBANs and CBANs comparison keeping in view liquidity creation, liquidity risk and loan concentration are either limited or nonexistent.

Machine learning insights into thrombo-ischemic risks and bleeding events through platelet lysophospholipids and acylcarnitine species

Coronary artery disease (CAD) often leads to adverse events resulting in significant disease burdens. Underlying risk factors often remain inapparent prior to disease incidence and the cardiovascular (CV) risk is not exclusively explained by traditional risk factors. Platelets inherently promote atheroprogression and enhanced platelet functions and distinct platelet lipid species are associated with disease severity in patients with CAD. Lipidomics data were acquired using mass spectrometry and processed alongside clinical data applying machine learning to model estimates of an increased CV risk in a consecutive CAD cohort (n = 595). By training machine learning models on CV risk measurements, stratification of CAD patients resulted in a phenotyping of risk groups. We found that distinct platelet lipids are associated with an increased CV or bleeding risk and independently predict adverse events. Notably, the addition of platelet lipids to conventional risk factors resulted in an increased diagnostic accuracy of patients with adverse CV events. Thus, patients with aberrant platelet lipid signatures and platelet functions are at elevated risk to develop adverse CV events. Machine learning combining platelet lipidome data and common clinical parameters demonstrated an increased diagnostic value in patients with CAD and might improve early risk discrimination and classification for CV events.

Tail mean-variance portfolio selection with estimation risk

Tail Mean-Variance (TMV) has emerged from the actuarial community as a criterion for risk management and portfolio selection, with a focus on extreme losses. The existing literature on portfolio optimization under the TMV criterion relies on the plug-in approach that substitutes the unknown mean vector and covariance matrix of asset returns in the optimal portfolio weights with their sample counterparts. However, the plug-in method inevitably introduces estimation risk and usually leads to poor out-of-sample portfolio performance. To address this issue, we propose a combination of the plug-in and 1/N rules and optimize its expected out-of-sample performance. Our study is based on the Mean-Variance-Standard-deviation (MVS) performance measure, which encompasses the TMV, classical Mean-Variance, and Mean-Standard-Deviation (MStD) as special cases. The MStD criterion is particularly relevant to mean-risk portfolio selection when risk is measured by quantile-based risk measures. Our proposed combined portfolio consistently outperforms both the plug-in MVS and 1/N portfolios in simulated and real-world datasets.

Parabolic PDE-constrained optimal control under uncertainty with entropic risk measure using quasi-Monte Carlo integration

We study the application of a tailored quasi-Monte Carlo (QMC) method to a class of optimal control problems subject to parabolic partial differential equation (PDE) constraints under uncertainty: the state in our setting is the solution of a parabolic PDE with a random thermal diffusion coefficient, steered by a control function. To account for the presence of uncertainty in the optimal control problem, the objective function is composed with a risk measure. We focus on two risk measures, both involving high-dimensional integrals over the stochastic variables: the expected value and the (nonlinear) entropic risk measure. The high-dimensional integrals are computed numerically using specially designed QMC methods and, under moderate assumptions on the input random field, the error rate is shown to be essentially linear, independently of the stochastic dimension of the problem—and thereby superior to ordinary Monte Carlo methods. Numerical results demonstrate the effectiveness of our method.

Board gender diversity, firm risk, and the intermediate mechanisms: A meta‐analysis

AbstractResearch questionThe primary focus of this meta‐analysis is to synthesize previously discordant findings on the relationship between board gender diversity (BGD) and different types of firm risk and to explore potential moderating and mediating mechanisms underlying these relationships.Research findingsWe statistically combine the results from 193 studies and find a negative association between BGD and firm risk. Further investigation indicates that different measures of risk lead to systematically different effect sizes. Our meta‐analysis structural equation modeling (MASEM) analysis reveals that BGD's impact on risk operates primarily through the monitoring rather than advising function of the board. Regarding the moderating role of national institutions, we find that several aspects of the national institutional context (e.g., investor protection, gender equality, and national culture) influence the relationship between BGD and different types of risk.Theoretical implicationsOverall, our results suggest that agency theory has more explanatory power than resource dependence theory for understanding the association between BGD and risk, and women's board representation is more likely to reduce downside risk than upside risk. Our moderating effect analysis also highlights interesting avenues for further research on the interplay of BGD and different risks in national environments with varying institutional attributes.Practitioner/policy implicationsOur meta‐analysis offers important practical implications for corporate risk management, suggesting that BGD significantly mitigates downside risks associated with poor corporate transparency without stifling board support for corporate decisions shaping future growth potential. In an era of rising board vulnerability to litigation for insufficient transparency, this study contributes evidence supporting trends toward greater gender diversity.

Low Geriatric Nutritional Risk Index Predicts Early Complications following Distal Radius Fracture Open Reduction Internal Fixation

Abstract Background While previous studies have investigated the association between the Geriatric Nutritional Risk Index (GNRI) and functional outcomes following distal radius fracture (DRF), no large-scale studies have investigated the associations between GNRI and postoperative outcomes following DRFs. Objectives The purpose of this study is to investigate the association between GNRI, a simple multifaceted measure of malnutrition risk, and 30-day postoperative complications following DRF open reduction internal fixation (ORIF). Materials and Methods The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent DRF ORIF from 2015 to 2021. Patients were organized into three groups based on preoperative GNRI: normal/reference (GNRI > 98), moderate malnutrition (92 ≤ GNRI ≤ 98), and severe malnutrition (GNRI < 92). Logistic regression analysis was conducted to investigate the relationship between preoperative GNRI and postoperative complications. Results Severe malnutrition was independently associated with a greater likelihood of any complication (odds ratio [OR]: 3.22, 95% confidence interval [CI]: 2.48–4.17; p < 0.001), sepsis (OR: 15.41, 95% CI: 1.66–143.32; p = 0.016), cardiac arrest or myocardial infarction (OR: 20.58, 95% CI: 1.62–261.26; p = 0.020), pulmonary embolism (OR: 9.40, 95% CI: 1.76–50.11; p = 0.009), surgical site infection (OR: 7.73, 95% CI: 1.99–30.02; p = 0.003), nonhome discharge (OR: 2.55, 95% CI: 1.87–3.49; p < 0.001), readmission (OR: 2.47, 95% CI: 1.47–4.14; p < 0.001), and length of stay > 2 days (OR: 3.51, 95% CI: 2.67–4.62; p < 0.001). Conclusions Malnutrition is an independent significant predictor of early complications following DRF ORIF. Level of Evidence Level III; retrospective cohort comparison; prognosis study.

Stackelberg equilibria with multiple policyholders

We examine Pareto-efficient contracts and Stackelberg Equilibria (SE) in a sequential-move insurance market in which a central monopolistic insurer on the supply side contracts with multiple policyholders on the demand side. We obtain a representation of Pareto-efficient contracts when the monopolistic insurer's preferences are represented by a coherent risk measure. We then obtain a representation of SE in this market, and we show that the contracts induced by an SE are Pareto-efficient. However, we note that SE do not induce a welfare gain to the policyholders in this case, echoing the conclusions of recent work in the literature. The social welfare implications of this finding are examined through an application to the flood insurance market of the United States of America, in which we find that the central insurer has a strong incentive to raise premia to the detriment of the policyholders. Accordingly, we argue that monopolistic insurance markets are problematic, and must be appropriately addressed by external regulation.

Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures

AbstractIn recent years, the semiparametric methods for the joint estimation and prediction of value at risk (VaR) and expected shortfall (ES) have triggered great interests and attention. Compared to existing literature which usually incorporates realized volatility (RV) into the dynamic semiparametric risk models, this paper considers three more robust proxies (medRV, BPV, and RK) of intraday volatility in the models to verify whether high‐frequency information can improve the joint prediction ability of risk measures. To strengthen the persuasion of conclusions, four international stock indices (S&P500, Nikkei225, GDAXI, and DJIA) are applied to these models to estimate and forecast VaR and ES at different probability levels (1%, 2.5%, 5%, and 10%). Then, the predicted VaR and ES are backtested by several methods individually, and the popular score function FZ0 and MCS test are used to compare the effects of jointly predicting risk measures. Our results confirm that these semiparametric models containing intraday information outperform the benchmark models for four stocks and various probability levels, and medRV is the best volatility measure in improving the effects of models.

Adaptation pathways for effective responses to climate change risks

AbstractClimate related changes are already affecting every area of our world and will increasingly do so as global warming increases, resulting in compounding and cascading risks across multiple locations and sectors. Deliberative processes and anticipatory actions are required to adapt to the associated complex and uncertain systemic risks, with dynamic and long‐term planning needed even where there is limited knowledge of the effectiveness of adaptation. In this focus article, we examine the adaptation pathways developed for the Europe Chapter of the IPCC AR6. We argue that illustrative pathways built on quantitative and qualitative assessment of adaptation effectiveness can inform adaptation planning to manage the increasing severity of risks. We find that as the global warming level increases adaptation pathways can diverge, leading to radically different futures, for example, adaptation responses to sea level rise. We illustrate how adaptation measures for different risks interact resulting in trade‐offs, for example, increasing water scarcity. Although pathways offer a useful framework to address multiple adaptation challenges, other supporting conditions are needed for the successful implementation of adaptation, such as establishing legitimacy and buy‐in through collaboration of various actors and effective governance. Ultimately, adaptation will be increasingly more complex and constrained in a warmer world, increasing risks of losses and damages to people and nature.This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for Adaptation The Social Status of Climate Change Knowledge > Climate Science and Decision Making Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies

Methodology for Risk-Benefit Analysis in Research

Risk-benefit analysis in the context of research activities has recently become a mandatory exercise. It is an integral part of the more extensive exercise, the scientific and ethical review for research approval and subsequent monitoring and evaluation exercises. However, how to undertake the process remains a largely subjective process that results in somewhat 'incomplete' inventories of potential risks and benefits for many research activities. There is also the challenge of computing an overall study risk-benefit score when objective and subjective measures of risks, benefits, severity, and magnitude are involved. This study employed the integrative literature review methodology to summarize past empirical and theoretical literature to provide a more comprehensive understanding of a phenomenon. The result is a simple, concise, and practical guide for identifying research risks and benefits, the computation of risk scores, benefit scores, and the overall risk-benefit score in a research activity. These scores are subsequently utilized to compute the study's overall risk-benefit analysis score. This step-by-step process for the computation of a risk-benefit ratio will introduce clarity in the process of risk-benefit analysis to help ascertain if specific risk probabilities and their severity have been correctly assessed. Conversely, the approach is also helpful in ascertaining if study benefits and their magnitude have been adequately assessed and rated.

A FAST Method for Nested Estimation

Nested estimation involves estimating an expectation of a function of a conditional expectation and has many important applications in operations research and machine learning. Nested simulation is a classic approach to this estimation, and the convergence rate of the mean squared error (MSE) of nested simulation estimators is only of order [Formula: see text], where Γ is the simulation budget. To accelerate the convergence, in this paper, we establish a jackkniFe-bAsed neSted simulaTion (FAST) method for nested estimation, and a unified theoretical analysis for general functions in the nested estimation shows that the MSE of the proposed method converges at the faster rate of [Formula: see text] or even [Formula: see text]. We also provide an efficient algorithm that ensures the estimator’s MSE decays at its optimal rate in practice. In numerical experiments, we apply the proposed estimator in portfolio risk measurement and Bayesian experimental design in operations research and machine learning areas, respectively, and numerical results are consistent with the theory presented. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72031006, 72101260, and 72394375]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0118 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0118 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Gender Identity and Mental Health Symptom Severity Among Adolescents Admitted to an Inpatient Psychiatric Hospital.

Transgender youth are at an increased risk of suicide, substance use, experiencing violent assaults, and reporting major depressive episodes and greater psychological distress compared to their cisgender counterparts. This study examined mental health symptom severity in adolescents admitted to an inpatient psychiatric hospital who wished they were of a different gender compared to those who did not. A group of 180 adolescents admitted to an inpatient psychiatric hospital completed assessments to measure mental health symptom severity at admission. Gender diverse (n = 90) and cisgender (n = 90) groups were established. Analyses of variance (ANOVA) were used to examine between group (gender diverse vs. cisgender) difference on depression, anxiety, suicide risk, nighttime sleep quality, and emotion regulation problems. Results revealed significant differences in emotion regulation difficulties at admission, specifically in nonacceptance and awareness. There were no significant differences on measures of depression, anxiety, suicide risk, and nighttime sleep quality at admission. This study is one of the first to measure mental health symptom severity in gender diverse adolescents while admitted to an inpatient psychiatric setting. Adolescents in the gender diverse group had significantly higher level of difficulty with emotion regulation, which may indicate an increased risk of developing psychiatric symptoms such as depression and anxiety. This paper demonstrates the importance of using targeted interventions to address difficulties with emotion regulation in at-risk adolescents.

Association between maternal employment status and presence of children with major congenital anomalies in Denmark

ImportanceThe burden of caring for children with complex medical problems such as major congenital anomalies falls principally on mothers, who in turn suffer a variety of potentially severe economic consequences. As well, health consequences of caregiving often further impact the social and economic prospects of mothers of children with major congenital anomalies (MCMCAs). Evaluating the long-term economic consequences of extensive in-home caregiving among MCMCAs can inform strategies to mitigate these effects.ObjectiveTo assess whether MCMCAs face reduced employment and increased need for disability benefits over a 20-year period.DesignA population-based matched cohort study.SettingDenmark.ParticipantsAll women who gave birth to a singleton child with a major congenital anomaly in Denmark between January 1, 1997 and December 31, 2017 (n = 23,637) and a comparison cohort of mothers matched by maternal age, parity, and infant’s year of birth (n = 234,586).ExposuresLiveborn infant with a major congenital anomaly.Main outcomes and measuresThe primary outcome was mothers’ employment status, stratified by their child’s age. Employment status was categorized as employed, outside the workforce (on temporary leave, holding a flexible job, or pursuing education), or unemployed; the number of weeks in each category was measured over time. The secondary outcome was time to receipt of a disability pension, which in Denmark implies permanent exit from the labor market. We used a negative binomial regression model to estimate the number of weeks in each employment category, stratified by the child’s age (i.e., 0–1 year, > 1–6 years, 7–13 years, 14–18 years). A Cox proportional hazards regression model was used to compute hazard ratios as a measure of the relative risk of receiving a disability pension. Rate ratios and hazard ratios were adjusted for maternal demographics, pregnancy history, health, and infant’s year of birth.ResultsDuring 1–6 years after delivery, MCMCAs were outside the workforce for a median of 50 weeks (IQR, 6–107 weeks), while members of the comparison cohort were outside the workforce for a median of 48 weeks (IQR, 4–98 weeks), corresponding to an adjusted rate ratio [ARR] of 1.05 (95% confidence interval [CI], 1.04–1.07). During the first year after delivery, MCMCAs were more likely to be employed than mothers in the comparison cohort (ARR, 1.08; 95% CI, 1.06–1.10). At all timepoints thereafter, MCMCAs had a lower rate of workforce participation. The rate of being outside the workforce was 5% higher than mothers in the comparison cohort during 1–6 years after delivery (ARR, 1.05; 95% CI, 1.04–1.07), 9% higher during 7–13 years after delivery (ARR, 1.09; 95% CI, 1.06–1.12), and 12% higher during 14–18 years after delivery (ARR, 1.12; 95% CI, 1.07–1.18). Overall, MCMCAs had a 20% increased risk of receiving a disability pension during follow-up than mothers in the matched comparison cohort [incidence rates 3.10 per 1000 person-years (95% CI, 2.89–3.32) vs. 2.34 per 1000 person-years (95% CI, 2.29–2.40), adjusted hazard ratio, 1.20; 95% CI, 1.11–1.29].Conclusion and relevanceMCMCAs were less likely to participate in the Danish workforce, less likely to be employed, and more likely to receive disability pensions than mothers of unaffected children. The rate of leaving the workforce intensified as their affected children grew older. The high demands of caregiving among MCMCAs may have long-term employment consequences even in nations with comprehensive and heavily tax-supported childcare systems, such as Denmark.

Analysis of Business Risk Measurement and Factors Influencing Plantation-Based Farming Cooperatives: Evidence from Guizhou Province, China

Plantation-based farming cooperatives are important carriers to promote agricultural and rural modernization and increase farmers’ income. Their risk management is related not only to their own sustainable development but also to the practical interests of farmers. Based on the survey data of 226 sample cooperatives in Guizhou Province, this study measures the business risk of plantation-based farming cooperatives via factor analysis and empirically tests its influencing factors using a structural equation model. This research shows that there are different types of operational risks in plantation-based farming cooperatives, and the order of these risks is as follows: market risk (0.334) > policy risk (0.162) > natural risk (0.140) > technical risk (0.104) > management risk (0.097). In terms of the factors influencing business risk, the impact effect value of the economic environment is 0.522, making it the most important external interference factor. The technology environment and policy environment have a greater impact on business risk (effect values of 0.323 and 0.219, respectively). The effect of the social service environment (an effect value of 0.114) is relatively weak. The internal factor, operator characteristics, is the core factor (an effect value of 0.533) that affects the business risk of plantation-based farming cooperatives, which determines the development prospects of the cooperatives. Resource endowment is an important internal factor affecting the business risk of cooperatives (an effect value of 0.331). According to the conclusions of our research, some policy implications on how to promote the high-quality development of plantation-based farming cooperatives are presented, i.e., to refine the policy support for plantation-based farming cooperatives, optimize the economic environment of the agricultural market, further improve agricultural infrastructure, deepen the reform of the agricultural land transfer system, and improve the internal management level of cooperatives.