Risk Measures Research Articles

A comprehensive framework for evaluating and predicting investment risks in renewable energy projects: a case study of Maynak hydropower station

ABSTRACT This study introduces a holistic analysis framework designed to evaluate and predict the investment risks associated with foreign renewable energy initiatives. This framework aims to reduce the uncertainties inherent in renewable energy projects. To achieve this, we utilize the variable weight matter-element extension model to establish the project’s fundamental reliability function. We then refine this model by incorporating evidence theory to assess the project’s risk level and derive precise risk index measurements. We utilize the GM model to forecast potential future risks associated with the project. In addition, a case study is presented on the risk assessment and prediction for the Maynak Hydropower Station. Our findings reveals that the project encountered significant investment risks in 2008, 2014, 2020, and 2022. Key risk indicators include political turmoil，policy change，imperfect legislation，cultural risk，exchange rate changes，technical risks and management risks. Furthermore, the investment risk level declined from 2023 to 2027, with our risk measurement outcomes closely aligning with actual conditions, thereby validating the effectiveness and applicability of our model.

Enhancing Portfolio Decarbonization Through SensitivityVaR and Distorted Stochastic Dominance

Recent trends in portfolio management emphasize the importance of reducing carbon footprints and aligning investments with sustainable practices. This paper introduces SensitivityVaR (SensitivityVaR), an advanced distortion risk measure that combines Value-at-Risk (VaR) and Expected Shortfall (ES) with the Cornish–Fisher expansion. SensitivityVaR provides a more robust framework for managing risk, particularly under extreme market conditions. By incorporating first- and second-order distorted stochastic dominance criteria, we enhance portfolio decarbonization strategies, aligning financial objectives with environmental targets such as the Paris Agreement’s goal of a 7% annual reduction in carbon intensity from 2019 to 2050. Our empirical analysis evaluates the impact of integrating carbon intensity data—including Scope 1, Scope 2, and Scope 3 emissions—on portfolio optimization, focusing on key sectors like technology, energy, and consumer goods. The results demonstrate the effectiveness of SensitivityVaR in managing both risk and environmental impact. The methodology led to significant reductions in carbon intensity across different portfolio configurations, while preserving competitive risk-adjusted returns. By optimizing tail risks and limiting exposure to carbon-intensive assets, this approach produced more balanced and efficient portfolios that aligned with both financial and sustainability goals. These findings offer valuable insights for institutional investors and asset managers aiming to integrate climate considerations into their investment strategies without compromising financial performance.

Is Bowman’s paradox an empirical artifact? Evidence from Asian emerging countries

PurposeThis study aims to reconcile and address Bowman’s paradox empirical criticism from the lens of financial theory, corporate strategy and their econometric adversaries based on three issues, i.e. risk conceptualization, measurement and econometric modeling in Asian emerging countries (AEC).Design/methodology/approachThe study is conducted on panel data sampling from 2,872 firms across four Asian Emerging Countries (AEC) and employs a two-stage least squares (2SLS) estimation technique. We proposed a theoretical framework based on triangulation that outlines four risk-return relationships based on proxies derived from capital market and firm-level data and used different econometric models to answer Bowman’s paradox ongoing criticism.FindingsThe empirical results negate the empirical artifact viewpoint in AEC. The risk-return relationship estimated on firm accounting-based ratios or its combination with market-based measures supports Bowman’s paradox and thus upholds the corporate strategy point of view. Whereas the risk-return relationship based on market-based ratios upholds the financial theory point of view. However, the results are mixed when risk is subdivided into systematic and business risk. Our results are robust across standard deviation and semi-standard deviation-based measures of risk, and there is no evidence of a non-linear relationship.Originality/valueA compelling debate exists that Bowman’s paradox is an empirical artifact. We provide an innovative approach that aims to reconcile and address the ongoing debate by employing diverse risk-return proxies and econometric models in Asian emerging countries. Methodological issues such as endogeneity, sample biases, temporal fluctuations, downside risk variations, multiple moments of a variable and model misspecification are also addressed. This triangulation enhances the robustness of our analysis, providing a comprehensive perspective on AEC and laying the groundwork for future researchers to explore Bowman’s paradox through alternative measures and models.

Insurance Coverage and Flood Exposure in the Gulf of Mexico: Scale, Social Vulnerability, Urban Form, and Risk Measures

Increasing flood losses in the Gulf of Mexico related to development patterns and climate hazards pose serious threats to resilience and insurability. The purpose of this study is to understand how scale, social vulnerability, risk, and urban form relate to National Flood Insurance Program (NFIP) policy coverage and flood exposure. Our multilevel models identify that flooding is significantly clustered by region and counties, especially shoreline counties. Our measures of risk suggest that the Federal Emergency Management Agency (FEMA) special flood hazard area (SFHA) underestimates risk and exposure when compared with the Flood Factor and that there is some compensation in terms of insurance coverage, suggesting a pattern of adverse selection. Older housing stock appears both less insured and less exposed, raising questions of whether current growth patterns are increasing risk independent of environmental change. Our models suggest that census tracts with higher percentages of black residents are less insured and more exposed, and a similar pattern exists for rural areas. Our results highlight the need to seek common solutions across the Gulf of Mexico, concentrating on the most flood-exposed counties, and that specific resilience strategies may be necessary to protect areas with socially vulnerable populations, especially in rural areas. Underlying challenges exist due to the spatial relationship between exposure and social vulnerability and the potential for adverse selection in insurance markets due to different measures of risk.

BANKRUPTCY RISK AND SHARE PRICES: A CASE STUDY ON ENERGY COMPANIES IN KUWAIT

<p>This study aims to investigate how investors' perceptions of risk impact the stock prices of energy companies listed on the Kuwait Stock Exchange (KSE) from 2016 to 2023. Investors' belief in the future financial stability of the companies they invest in influences their risk perception. Therefore, if investors hold pessimistic views, they will decrease their investment in these companies, causing share prices to drop, and vice versa. The Zmijewski x-score model is utilized in this study as a measure of risk to assess the financial stability of Kuwaiti energy companies and its impact on their stock prices. The study employs ordinary least square regression (OLS) to analyze the correlation between the x-score and share prices. Results from this research indicated that there was no statistically significant relation between Zmijewski's x-score and stock price and that investors look at other factors when choosing their investments. This is evident because the company with the greatest bankruptcy risk in the following two years also had the second-highest share price.</p><p> </p><p><strong>JEL: </strong>G1, G4, M21</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0699/a.php" alt="Hit counter" /></p>

Risk assessment for the surface water quality evaluation of a hydrological basin

This paper proposes a risk assessment methodology for evaluating the surface water quality of hydrological basins based on physico-chemical parameter concentrations. Considering the Douro River basin in Portugal and monthly recorded dissolved oxygen and conductivity parameter measurements in 18 water sampling stations from January 2002 to December 2013, the work intends to answer the research question of identifying the riskiest periods for water pollution in the year and classifying the water sampling stations in terms of risk for water pollution. The methodology consists first in determining the pollution risk implied by the physico-chemical parameters, based on the monthly water station measurements, using six different risk measures, namely mean, variance, loss probability, entropy, mean excess loss and value at risk. The risk values are ordered according to each risk measure and a final ranking is established through a ranking aggregation method. The final ranking permitted identifying the high risk period as ranging from May to October and the low risk period from November to April. Furthermore, July was classified as riskiest month concerning the dissolved oxygen concentration, and August as riskiest month regarding the conductivity levels. On the other hand, the ranking allowed classifying the water sampling stations, previously grouped in clusters, in terms of similar risk for water pollution: six sampling stations in the west of the basin formed the riskiest cluster in the dry period considering the dissolved oxygen concentrations, and four of those stations formed also the riskiest cluster concerning the conductivity levels.

A new Bayesian method for estimation of value at risk and conditional value at risk

AbstractValue at Risk (VaR) and Conditional Value at Risk (CVaR) have become the most popular measures of market risk in Financial and Insurance fields. However, the estimation of both risk measures is challenging, because it requires the knowledge of the tail of the distribution. Therefore, Extreme Value Theory initially seemed to be one of the best tools for this kind of problems, because using peaks-over-threshold method, we can assume the tail data approximately follow a Generalized Pareto distribution (GPD). The main objection to its use is that it only employs observations over the threshold, which are usually scarce. With the aim of improving the inference process, we propose a new Bayesian method that computes estimates built with all the information available. Informative prior Bayesian (IPB) method employs the existing relations between the parameters of the loss distribution and the parameters of the GPD that models the tail data to define informative priors in order to perform Metropolis–Hastings algorithm. We show how to apply IPB when the distribution of the observations is Exponential, stable or Gamma, to make inference and predictions. .Afterwards, we perform a thorough simulation study to compare the accuracy and precision of the estimates computed by IPB and the most employed methods to estimate VaR and CVaR. Results show that IPB provides the most accurate, precise and least biased estimates, especially when there are very few tail data. Finally, data from two real examples are analysed to show the practical application of the method.

Health Risks and Improvement Measures of Construction Site Environment for Aging Construction Workers in China

Health Risks and Improvement Measures of Construction Site Environment for Aging Construction Workers in China

Near field tsunamis on volcanic islands: blueprint for risk management using Stromboli as a test bed

At Stromboli, coastline populations are exposed to near-field tsunami generated by flank collapses. Landslides on the Sciara del Fuoco can generate waves arriving in the village in less than a few minutes. Due to the hazard posed on Stromboli by tsunami, we have focused our research on answering three key questions in terms of mitigation: (1) How can a tsunami be detected and characterized in real-time? (2) What will be the likely event scenario and on-island impact of the resulting tsunami? (3) When and where will the tsunami arrive, and what is the evacuation capacity? To this end, we have convolved a method to assess escape times from tsunami-exposed coastal areas, with wave travel times and inundation assessments output from numerical simulations. Here we review the local situation in terms of hazard, risk and mitigation measures, and assess progress to date in preparing for tsunami on Stromboli to explore a blueprint for management actions at any near-field tsunami-exposed community.

Validation study of risk-reduction activities after personalized breast cancer education tool in the WISDOM study

Breast cancer risk reduction strategies have been well-validated, but barriers remain for high-risk individuals to adopt them. We performed a study among participants with high risk of breast cancer to validate whether a virtual breast health decision tool impacted a participant’s willingness to start risk-reducing activities, identify barriers to adopting these strategies, and understand if it affects breast cancer anxiety. The study sample was 318 participants in the personalized (investigational) arm of the Women Informed to Screen Depending on Measures of risk (WISDOM) clinical trial. After reviewing the tool, these participants completed a feedback survey. We demonstrated that 15 (4.7%) women were taking endocrine risk reduction, 123 (38.7%) were reducing alcohol intake, and 199 (62.6%) were exercising. In the three-month follow-up survey of 109 respondents, only 8 of 61 (13.1%) women who considered endocrine risk reduction pursued it. In contrast, 11 of 16 (68%) participants who considered alcohol reduction pursued the activity, and 14 of 24 (58%) women who considered exercise followed through. Participants listed fear of side effects as the most common barrier to endocrine risk reduction. We also present further steps to be taken to improve the effectiveness of the Breast Health Decisions tool.

Social Status, Portfolio Externalities, and International Risk Sharing

We show that a model of “the spirit of capitalism”, or the concern for social status, can generate a high degree of international risk sharing as measured by asset prices, even when consumption and portfolio holdings exhibit “home bias”. We also show how portfolio externalities can arise in the model and highlight the caution that one needs in interpreting asset-price-based measures of international risk sharing: in the presence of portfolio externalities, even when the measured degree of risk sharing is perfect, it is still possible for government policies to induce investors to hold better-diversified portfolios and attain higher welfare.

A study on the backtesting and correlation of systemic risk in Chinese financial institutions: an empirical analysis based on MES and Delta CoVaR

Effectively measuring the risk level of financial institutions is the key to preventing and defusing systemic risks. This paper uses the unconditional coverage test method to backtest the systemic risk of 35 listed financial institutions in China, and uses the quantile coherence method to construct a quantile frequency domain correlation network to explore the frequency domain mechanism of systemic risk correlation of financial institutions. The study found that: (1) During the financial market crisis, the Delta CoVaR commonly used systemic risk measurement indicators such as MES, CoVaR, and failed to effectively measure the systemic risk of China's financial institutions. (2) Compared with the short-term correlation layer and the medium-term correlation layer, China's financial institutions have obvious extreme risk linkage effects in the long-term correlation layer.(3) In the short-term, medium-term and long-term correlation layers, small and medium-sized financial institutions such as Ningbo Bank, Industrial Securities and Dongfang Energy all have high correlation importance. Relevant departments should pay attention to preventing the risk of "too correlated to fail" of small and medium-sized financial institutions in my country.

Analysis of Financial Investment Risk and Preventive Measures

With the development of economic cycle, financial investment has become one of the key exploration directions of enterprises. Scientific financial investment can help enterprises increase the value of assets and expand the scale of operation. However, due to the increasingly complex economic market, financial investment risks are more complex. Based on this, this paper takes the risks faced by enterprises in the process of financial investment as the research basis, sorts out the causes of risks, and puts forward targeted preventive measures, aiming at providing support for enterprises to stabilize operation and obtain more investment returns.

Scalarized utility-based multi-asset risk measures

We introduce a risk measure that simultaneously allows to minimize hedging costs and maximize expected utility in the presence of a risk constraint. We call it the scalarized utility-based multi-asset (SUBMA) risk measure. For the SUBMA risk measure we state the following results: If the utility function has constant relative risk aversion and the risk constraint is coherent, then the SUBMA risk measure is coherent. In a one-period financial market setup, we present a sufficient condition for the SUBMA risk measure to be finite-valued. We derive results about the existence of optimal payoffs. Finally, we present a dual representation for a map that generalizes classical risk measures and has not been analyzed so far. In particular, it gives us the dual representation for the SUBMA risk measure.

Cryptocurrency Portfolio Allocation under Credibilistic CVaR Criterion and Practical Constraints

The cryptocurrency market offers attractive but risky investment opportunities, characterized by rapid growth, extreme volatility, and uncertainty. Traditional risk management models, which rely on probabilistic assumptions and historical data, often fail to capture the market’s unique dynamics and unpredictability. In response to these challenges, this paper introduces a novel portfolio optimization model tailored for the cryptocurrency market, leveraging a credibilistic CVaR framework. CVaR was chosen as the primary risk measure because it is a downside risk measure that focuses on extreme losses, making it particularly effective in managing the heightened risk of significant downturns in volatile markets like cryptocurrencies. The model employs credibility theory and trapezoidal fuzzy variables to more accurately capture the high levels of uncertainty and volatility that characterize digital assets. Unlike traditional probabilistic approaches, this model provides a more adaptive and precise risk management strategy. The proposed approach also incorporates practical constraints, including cardinality and floor and ceiling constraints, ensuring that the portfolio remains diversified, balanced, and aligned with real-world considerations such as transaction costs and regulatory requirements. Empirical analysis demonstrates the model’s effectiveness in constructing well-diversified portfolios that balance risk and return, offering significant advantages for investors in the rapidly evolving cryptocurrency market. This research contributes to the field of investment management by advancing the application of sophisticated portfolio optimization techniques to digital assets, providing a robust framework for managing risk in an increasingly complex financial landscape.

Distortion Risk Measures of Increasing Rearrangement

Increasing rearrangement is a rewarding instrument in financial risk management. In practice, risks must be managed from different perspectives. A common example is the portfolio risk, which often can be seen from at least two perspectives: market value and book value. Different perspectives with different distributions can be coupled by increasing rearrangement. One distribution is regarded as underlying, and the other distribution can be expressed as an increasing rearrangement of the underlying distribution. Then, the risk measure for the latter can be expressed in terms of the underlying distribution. Our first objective is to introduce increasing rearrangement for application in financial risk management and to apply increasing rearrangement to the class of distortion risk measures. We derive formulae to compute risk measures in terms of the underlying distribution. Afterwards, we apply our results to a series of special distortion risk measures, namely the value at risk, expected shortfall, range value at risk, conditional value at risk, and Wang’s risk measure. Finally, we present the connection of increasing rearrangement with inverse transform sampling, Monte Carlo simulation, and cost-efficient strategies. Butterfly options serve as an illustrative example of the method.

Drug-induced increase in dispersion of ventricular repolarization in patients with heart failure with preserved ejection fraction

BackgroundHeart failure (HF) with preserved ejection fraction (HFpEF) is associated with enhanced response to drug-induced QT interval lengthening. We determined the influence of HFpEF on drug-induced lengthening of dispersion of repolarization, a measure of proarrhythmic risk. MethodsWe administered intravenous ibutilide 0.003 mg/kg to 10 patients with HFpEF and 10 age- and sex-matched controls without HF. Twelve‑lead electrocardiograms were obtained prior to ibutilide and serially for 8 h post-ibutilide. Tpeak-Tend, a measure of dispersion of ventricular repolarization, and heart rate-corrected J-Tpeak (J-Tpeakc), representing early repolarization, were measured by an investigator blinded to study groups. ResultsBaseline (pre-ibutilide) Tpeak-Tend and J-Tpeakc were not significantly different in the HFpEF and control groups. Maximum Tpeak-Tend was longer in the HFpEF group than in the control group (85 ± 10 vs 73 ± 8 ms, p = 0.01). Additionally, % change from baseline in Tpeak-Tend was greater in the HFpEF group [median (IQR) 17 (11) vs 8 (3)%, p = 0.003]. The area under the effect curve from 0 to 8 hours following ibutilide (including the 10-minute infusion) (AUEC0-8.17) for Tpeak-Tend was also larger in the HFpEF group (600 ± 42 vs. 543 ± 49 ms•hr, p = 0.03). Maximum J-Tpeakc, % change from baseline in J-Tpeakc and AUEC0-8.17 for J-Tpeakc in the two groups were not significantly different. ConclusionHFpEF is associated with enhanced response to drug-induced increases in dispersion of repolarization.

Prognostic Prediction and Immune Microenvironment Characterization in Uveal Melanoma: A Novel Mitochondrial Metabolism-Related Gene Signature.

Uveal Melanoma (UM), a highly aggressive and metastatic intraocular cancer with a strong propensity for liver metastasis, presents limited therapeutic alternatives and unfavorable survival outcomes. Despite its low incidence, the underlying mechanisms of UM pathogenesis and the precise role of mitochondrial metabolism in UM remain inadequately understood. Utilizing Cox proportional hazards regression analysis was used to assess prognostic relevance, and consensus clustering was employed for molecular subtyping. A risk signature was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We further conducted comparative analyses on clinicopathological characteristics, somatic mutation profiles, drug sensitivity, gene expression patterns, and tumor microenvironment features across different molecular subtypes. Moreover, a nomogram was developed and evaluated. Among 1234 mitochondria metabolism-related genes (MMRGs), 343 were identified as significantly associated with the prognosis of UM. These prognosis-associated MMRGs facilitated the classification of UM into two distinct molecular subtypes, which displayed notable differences in prognosis and pathological staging. Furthermore, an index termed the MMRGs-derived index (MMI) was derived from eight MMRGs, serving as a quantitative measure for poor prognosis risk in UM. MMI demonstrated significant associations with clinicopathological characteristics, somatic mutations, drug responsiveness, and the tumor microenvironment, where higher MMI levels corresponded to worse prognosis, advanced pathological stages, and increased immune cell infiltration. The nomogram built upon MMI provided a potential tool for clinical prognosis assessment in UM patients. This study demonstrated the potential value of MMRGs in predicting prognosis and molecular stratification within UM; however, additional clinical and basic research is warranted to validate their applicability and elucidate the related mechanisms.

The relationship between repeated measurements of HbA1c and risk of coronary events among the common haptoglobin phenotype groups: the Action for Health in Diabetes (Look AHEAD) study

BackgroundIn the ACCORD study, participants with the haptoglobin (Hp) 2–2 phenotype and glycated hemoglobin (HbA1c) ≥ 8.0% had a higher risk of coronary artery disease (CAD) compared to those with HbA1c 7.0–7.9%. However, this association was not observed in participants without the Hp2-2 phenotype. The optimal glycemic target for CAD prevention for the Hp phenotypes remains uncertain and may vary based on demographic and clinical factors.ObjectiveTo investigate how reaching clinically relevant HbA1c targets relates to the risk of CAD in different Hp phenotype groups among a diverse cohort of individuals with T2DM (the Look AHEAD study, HbA1c ≤ 11% at baseline).MethodsCox regression models with time-varying covariables were used to quantify the association between time-varying achieved HbA1c (< 6.5%, 6.5–6.9%, and ≥ 8.0% compared to 7.0-7.9%), updated at years 1–4, 6, 8, and 10, and incident CAD in the Hp2-2 (n = 1,587) and non-Hp2-2 (n = 2,944) phenotypes separately. Further pre-specified subgroup analyses by age, sex, history of cardiovascular disease (CVD), race, and diabetes duration were performed in each Hp phenotype group separately.ResultsCompared with HbA1c 7.0-7.9%, having HbA1c < 6.5% was associated with a 29% lower CAD risk among participants with the non-Hp2-2 phenotype (adjusted HR 0.71, 95% CI 0.55–0.90). In subgroup analyses, this association was present in participants with the non-Hp2-2 phenotype who were male (0.60, 0.44–0.83), who did not have a history of CVD (0.65, 0.47–0.90), who were aged ≥ 65 years (0.64, 0.44–0.94), who were White (0.68, 0.51–0.91), or who had diabetes duration > 10 years (0.58, 0.35–0.95). HbA1c ≥ 8.0% was associated with CAD risk only among participants with the Hp2-2 phenotype who had a history of CVD (1.79, 1.00-3.20). No associations were found between the other HbA1c targets and CAD risk when participants with the Hp2-2 phenotype were grouped together or divided into subgroups.ConclusionThe differences in our results compared to our previous findings may be due to variations in the study populations and factors associated with weight loss, making it difficult to draw definitive conclusions. Our current findings should be considered in the context of hypothesis generation, and ideally, will encourage additional research in this field.

Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data

Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades.