Risk Measures Research Articles

The Causality Relationship between Credit Default Swaps (CDS) and Portfolio Investments: The Case of Türkiye

This study examines the causality relationship between portfolio investments and credit default swaps (CDS) in Türkiye. Analysing the dynamics between portfolio investments and CDS premiums, two important variables for financial markets is critical to understanding how risk perception and investment decisions are affected. While portfolio investments are generally considered an indicator of the confidence of foreign investors in the country's economy, CDS premiums are an important risk measure that reflects the country's debt risk and the risk perception of market participants. In this context, examining the relationships between the two variables contributes to the understanding of the effects of investor behavior and risk perception on macroeconomic indicators in financial markets. In the study, the Granger causality test was applied using data from the period 2014Q1-2024Q1. The results obtained show that CDS premiums have a significant and unidirectional causal effect on portfolio investments. Increases in CDS premiums increase investors' risk perception and lead to a decrease in portfolio investments. On the other hand, no causal effect of portfolio investments on CDS premiums was found. These findings emphasize the importance of risk management in terms of portfolio investments in Türkiye and reveal that CDS premiums play a role in investor decisions.

A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk

Traditional approaches to flood risk management assume flood events follow an independent, identically distributed (i.i.d.) random process from which static risk measures are computed. Modern risk accounting strategies also consider nonstationarity or long-term trends in the mean and moments of the associated flood probability distributions. However, few approaches consider how extreme hydroclimatic events cluster in both space and time, compounding damage risks. Here we introduce a compound flood risk simulator that models and conditionally forecasts future variability in regional flooding events that cluster in time, given trends and oscillations in a variable climate signal. A modular, novel integration of wavelet signal processing, nonstationary time series forecasting, k-nearest neighbor (KNN) bootstrapping, multivariate copulas, and modified Neyman-Scott (NS) event clustering process provides users the ability to model interannual and sub-annual clustering of flood risk. Our semi-parametric flood generator specifically targets the clustered temporal dynamics of jointly modeled flood intensity, duration, and frequency over a finite future period of a decade or more, thereby providing a foundation for adaptation approaches that integrate temporally clustered flood risk into planning, response and recovery.

Impact of Sharia‐Based Screening on Equity Risk: International Evidence

ABSTRACTThis paper investigates the impact of mainstream Sharia‐based screening methodologies on equity risk. The study is based on the daily returns of 23 pairs of stock indices belonging to different index providers, namely Dow Jones, FTSE, and S&P. For risk measurement, the study uses five Value at Risk (VaR) methods. Moreover, the conditional VaR (CVaR) is used to analyze the tail behavior of return distributions. The results show that Sharia‐based screening is a source of significant differences in risk. There is strong evidence that Islamic stock indices (ISIs) tend to be less risky than their conventional peers. Also, the screening process allows for keeping extreme negative returns at a lower level relative to unscreened indices. The analysis during the Dotcom and Subprime crises shows that, globally, the results are time‐invariant. In sum, Islamic screening is tantamount to prudential rules that lead to low‐risk portfolios; it is, in its essence, a risk‐reduction strategy. Findings suggest that Sharia‐compliant stocks would be of great attractiveness to risk‐averse investors. They will find their claim in this asset class, as its riskiness is relatively low. For banks, a lower VaR of Islamic investing implies fewer capital requirements for market risk when holding portfolios that include Sharia‐compliant stocks. Fewer capital requirements will enhance the banks' ability to invest and make profits, which will, in turn, result in reducing the cost of capital.

Cross-Validation of the Financial Exploitation Vulnerability Scale

ABSTRACT Objectives The present study is a cross-validation of the Financial Exploitation Vulnerability Scale (FEVS), a measure of contextual risk for financial exploitation. Methods The sample was drawn from both the community and the SAFE program, a service for older adults who have been financially scammed. FEVS was administered within a larger assessment battery. The total score ability to differentiate exploitation groups and its correlates were examined. ROC analysis and logistic regression evaluated the clinical utility of the FEVS to detect exploitation. Results were compared to the initial validation study. Results FEVS score was significantly higher for those who were exploited and correlated with age. ROC analysis revealed adequate detection of financial exploitation. FEVS total score remained a strong predictor of exploitation when compared to demographic factors and several measures of cognitive functioning. Conclusions Cross-validation demonstrates strong evidence that the FEVS detects financial exploitation in older adults, beyond the ability of many known risk factors. Clinical Implications FEVS is an evidence-based tool for identifying exploitation and is accessible to many professionals working with older adults. Items query contextual factors that allow professionals to support clients with the appropriate standard of care.

The waist-to-hip ratio is a superior predictor of complications in breast reconstruction: A prospective pilot study

IntroductionBreast reconstruction remains common. Identifying the key risk factors for preoperative counseling is key to improving surgical outcomes. The body mass index is a commonly used measure of preoperative risk but remains controversial. We propose that the waist-to-hip ratio, a measure of central adiposity, is superior to the body mass index in predicting complications. MethodsA single surgeon’s breast reconstruction patients were prospectively analyzed from 2022-2023. Demographic data, medical comorbidities, and surgical variables were recorded, as were complications including infection, wound breakdown, and loss of reconstruction. Height, weight, waist circumference, and hip circumference were collected to calculate the waist-to-hip ratio, waist-to-height ratio, and body surface area. Statistical analysis and multivariate analysis were performed. ResultsOne hundred patients were evaluated, 50 for alloplastic and 50 for autologous, abdominally based reconstruction. For alloplastic patients, 26% had complications, with a higher waist-to-hip ratio (0.96v 0.88). On multivariate regression, the waist-to-hip was the only significant independent predictor of complications. For autologous reconstruction, again 26% had complications, with a higher waist-to-hip ratio (0.96v 0.88). On multivariate regression, the waist-to-hip was the only significant predictor of complications. For both groups, waist-to-hip did not strongly correlate with other morphometric measures. Age and other morphometric measures were not predictive of complications for either set. ConclusionThe waist-to-hip ratio is a superior predictor of surgical risk to the body mass index in our cohort and could be integrated in plastic surgery practices to improve outcomes. Level of EvidenceIV

Effects of climate change on firm credit risk: evidence from China

ABSTRACT Our study examines the impact of extreme weather on corporate credit risk, identifies the impact pathway, and explores the influences of different heterogeneity contexts. We propose a modified version of the climate risk measure, Modified Regional Wise Climate Risk Index for Company (MRW-CRIc), which considers not only the climate risk on financial loss but also company-specific factors. We find that climate risk significantly affects enterprise credit risk and that the impact of extreme weather varies across three industries. This research contributes to regulatory efforts by enhancing the measurement of climate risk and deepening understanding of its implications for market stability.

Preliminary Evaluation of Self-Reported Training Volume as an Adjunct Measure of Female Athlete Triad Risk in Division 1 Collegiate Female Runners.

Background/Objectives: This study tested whether self-reported training volume is predictive of female athlete triad risk collected using an established twelve-question triad screening tool in National Collegiate Athletic Association (NCAA) Division I (DI) collegiate female runners. Methods: A total of 319 institutions were initially contacted, seven of which agreed to distribute surveys to their female cross-country and track and field athletes. A total of 41 of 149 respondents completed the survey and met all inclusion criteria. Linear and binomial logistic regressions examined the relationships between self-reported training volumes and estimated triad risk. Independent samples t-tests were also used to compare training volumes across the high (> 50th percentile for risk factor counts) vs. low-risk groups. Results: Total weekly competition and conditioning resistance training hours were associated with the total number of triad risk factors (p = 0.044) and were also predictive of the triad risk group (p = 0.037). Likewise, both competition and conditioning resistance training hours (p = 0.034) were higher in the high-risk group versus the low-risk group. Conclusions: These findings suggest that self-reported resistance training volume is predictive of triad risk, but additional research is required to determine if monitoring training volume can provide valuable, real-time assessments of triad risk in DI collegiate female runners.

Structured factor copulas for modeling the systemic risk of European and United States banks

In this paper, we employ Credit Default Swaps (CDS) to model the joint and conditional distress probabilities of banks in Europe and the U.S. using factor copulas. We propose multi-factor, structured factor, and factor-vine models where the banks in the sample are clustered according to their geographic location. We find that within each region, the co-dependence between banks is best described using both, systematic and idiosyncratic, financial contagion channels. However, if we consider the banking system as a whole, then the systematic contagion channel prevails, meaning that the distress probabilities are driven by a latent global factor and region-specific factors. In all cases, the co-dependence structure of bank CDS spreads is highly correlated in the tail. The out-of-sample forecasts of several measures of systemic risk allow us to identify the periods of distress in the banking sector over the recent years including the COVID-19 pandemic, the interest rate hikes in 2022, and the banking crisis in 2023.

Optimal reinsurance with multivariate risks and dependence uncertainty

In this paper, we study the optimal reinsurance design from the perspective of an insurer with multiple lines of business, where the reinsurance is purchased by the insurer for each line of business, respectively. For the risk vector generated by the multiple lines of business, we suppose that the marginal distributions are fixed, but the dependence structure between these risks is unknown. Due to the unknown dependence structure, the optimal strategy is investigated for the worst-case scenario. We consider two types of risk measures: Value-at-Risk (VaR) and Range-Value-at-Risk (RVaR) including Expected Shortfall (ES) as a special case, and general premium principles satisfying certain conditions. To be more practical, the minimization of the total risk is conducted under some budget constraint. For the VaR-based model with only two risks, it turns out that the limited stop-loss reinsurance treaty is optimal for each line of business. For the model with more than two risks, we obtain two types of optimal reinsurance strategies if the marginals have convex or concave distributions on their tail parts by constraining the ceded loss functions to be convex or concave. Moreover, as a special case, the optimal quota-share reinsurance with dependence uncertainty has been studied. Finally, after applying our findings to two risks, some studies have been implemented to obtain both the analytical and numerical optimal reinsurance policies.

Identifying Potential Factors Associated With Racial Disparities in COVID-19 Outcomes: Retrospective Cohort Study Using Machine Learning on Real-World Data.

Racial disparities in COVID-19 incidence and outcomes have been widely reported. Non-Hispanic Black patients endured worse outcomes disproportionately compared with non-Hispanic White patients, but the epidemiological basis for these observations was complex and multifaceted. This study aimed to elucidate the potential reasons behind the worse outcomes of COVID-19 experienced by non-Hispanic Black patients compared with non-Hispanic White patients and how these variables interact using an explainable machine learning approach. In this retrospective cohort study, we examined 28,943 laboratory-confirmed COVID-19 cases from the OneFlorida Research Consortium's data trust of health care recipients in Florida through April 28, 2021. We assessed the prevalence of pre-existing comorbid conditions, geo-socioeconomic factors, and health outcomes in the structured electronic health records of COVID-19 cases. The primary outcome was a composite of hospitalization, intensive care unit admission, and mortality at index admission. We developed and validated a machine learning model using Extreme Gradient Boosting to evaluate predictors of worse outcomes of COVID-19 and rank them by importance. Compared to non-Hispanic White patients, non-Hispanic Blacks patients were younger, more likely to be uninsured, had a higher prevalence of emergency department and inpatient visits, and were in regions with higher area deprivation index rankings and pollutant concentrations. Non-Hispanic Black patients had the highest burden of comorbidities and rates of the primary outcome. Age was a key predictor in all models, ranking highest in non-Hispanic White patients. However, for non-Hispanic Black patients, congestive heart failure was a primary predictor. Other variables, such as food environment measures and air pollution indicators, also ranked high. By consolidating comorbidities into the Elixhauser Comorbidity Index, this became the top predictor, providing a comprehensive risk measure. The study reveals that individual and geo-socioeconomic factors significantly influence the outcomes of COVID-19. It also highlights varying risk profiles among different racial groups. While these findings suggest potential disparities, further causal inference and statistical testing are needed to fully substantiate these observations. Recognizing these relationships is vital for creating effective, tailored interventions that reduce disparities and enhance health outcomes across all racial and socioeconomic groups.

The capital-on-capital cost in solvency II risk margin

AbstractThis work contributes to the literature on time consistent valuation of insurance liabilities and to the ongoing discussion on revisions of risk margin (RM) calculation, by formally defining the concept of capital-on-capital cost. We describe the capital-on-capital as the amount required to cover unexpected variations in future regulatory capitals from the current time to liabilities maturity. That is, the capital-on-capital cost is the RM component dedicated to cover the risk of future RMs and not to cover variations of the best estimate of liabilities cash-flows. We mathematically formalize the capital-on-capital cost as the difference between two alternative time consistent liabilities valuation formula. The first is obtained through backward iteration of the one-period market-consistent valuation operator, by iterating the solvency capital requirement (SCR) risk measure. We propose a second alternative valuation formula for liabilities, based on a new time consistent dynamic formulation of the SCR risk measure, called additive-SCR (ASCR). The ASCR represents the expected total capital requirement from current time to liabilities maturity. We prove that the second valuation formula, based on ASCR, is time consistent, unless it is not based on iteration of the one-period SCR risk measure. Finally, we apply the proposed approach to a portfolio of long term equity linked life-insurance contracts. In particular, we estimate the capital-on-capital cost by calculating the difference between the two valuation formulas. Numerical results show that for liabilities with long maturities the capital-on-capital cost is a non negligible component of the RM.

Assessment of Racial Bias within the Risk Analysis Index of Frailty

Objective: Our objective was to assess potential racial bias within the Risk Analysis Index (RAI). Background: Patient risk measures are rarely tested for racial bias. Measures of frailty, like the RAI, need to be evaluated for poor predictive performance among Black patients. Methods: Retrospective cohort study using April 2010–March 2019 Veterans Affairs Surgical Quality Improvement Program and 2010–2019 National Surgical Quality Improvement Program data. The performance of the RAI and several potential variants were compared between Black and White cases using various metrics to predict mortality (180-day for Veterans Affairs Surgical Quality Improvement Program, 30-day for National Surgical Quality Improvement Program). Results: Using the current, clinical threshold, the RAI performed as good or better among Black cases across various performance metrics versus White. When a higher threshold was used, Black cases had higher true positive rates but lower true negative rates, yielding 2.0% higher balanced accuracy. No RAI variant noticeably eliminated bias, improved parity across both true positives and true negatives, or improved overall model performance. Conclusions: The RAI tends to predict mortality among Black patients better than it predicts mortality among White patients. As existing bias-reducing techniques were not effective, further research into bias-reducing techniques is needed, especially for clinical risk predictions. We recommend using the RAI for both statistical analysis of surgical cohorts and quality improvement programs, such as the Surgical Pause.

ESG risk, economic policy uncertainty, and the downside risk: Evidence from US firms

This study investigates the relationship between risk factors of Environmental, Social, and Governance (ESG) and the downside risk of U.S. firms and tackles the role of economic policy uncertainty (EPU) in this relationship. Our results show that the downside risk of firms, as measured by value-at-risk and lower partial moment, is positively affected by ESG risk scores, and that this effect becomes more pronounced as EPU rises. These results are robust to two other alternative measures of ESG risk. We argue that enhancing ESG management and adaptability to economic uncertainty is crucial for aligning with sustainable development goals. Our findings provide valuable insights for U.S. firms, investors, and policymakers looking to navigate the evolving risk landscape.

Liquidity risk analysis via drawdown-based measures

Trading volumes are key variables in determining the degree of an asset's liquidity. We examine the volume drawdown process and crash recovery measures in rolling-time windows to assess exposure to liquidity risk. The time-varying windows protect our financial indicators from the massive amount of volume transactions that characterize the opening and closing of the stock market. The empirical study is carried out for three Nasdaq-listed assets from April to September 2022. Firstly, we shape all of the volume time series using a weighted-indexed semi-Markov (WISMC) model, as well as the EGARCH and GJR models for comparisons. Next, we calculate drawdown-based risk measures on real and synthetic data, simulated from all the considered econometric models. Finally, we employ the Kullback-Leibler divergence to compare real and simulated risk indicators. Results reveal that the WISMC model reproduces all the drawdown-based risk measures better than the EGARCH and GJR models do for all the considered stocks.

Sustainable Entrepreneurship: Interval Analysis in Risk Management and Uncertain Economies

Sustainable management in high-tech enterprises is a key aspect of successfully operating modern companies, especially under conditions of risk and uncertainty. This study reviews the field of sustainable management and interval analysis and identifies the main trends and challenges facing high-tech enterprises in the modern world. This study emphasizes the importance of applying interval analysis in making strategic decisions and developing sustainable business models that can adapt to variable environments. This paper presents empirical data, illustrating the practical application of interval analysis tools in the management in high-tech enterprises. It analyzes the effectiveness and potential of this approach to increase the levels of sustainability and competitiveness of organizations in constantly changing business environments. In general, this article is a valuable contribution to the development of sustainable management theory and practice for high-tech enterprises, enriching the existing knowledge in this area and offering new perspectives for research and practical application. Our research has been validated and is presented in the results section. The purpose of this study is to present current developments in methodologies and tools for risk measurement within the probabilistic paradigm of uncertainty, which are supposed to be used in relation to the economic evaluation of real investment projects. The methodological directions or approaches to risk measurement formed in this context are (1) based on quantile measures, within which the quantitative aspect of risk is modeled using quantile quantiles of the distribution of a random variable describing the possible (predicted) results of economic activity; (2) the Monte Carlo method, which is a tool for evaluating the indicators of economic efficiency and risk in justifying real investments, taking into account different distribution laws and mutual relations for the financial and economic parameters of the investment project, as well as its computational and instrumental elaboration.

Fuzzy-stochastic distributional robust approach for microalgae-based bio-plastic supply chain with new sustainability aspects

The current study offers a sustainable supply chain model that can be used to identify opportunities and constraints for the future of the microalgae-based bio-diesel and bio-plastic industry. The network’s carbon emissions are restricted by an uncertain constraint that allows for flexibility in controlling emissions as needed. The social constraints of the model provides a transparent depiction of labour needs in dynamic business landscape and foster women’s empowerment through a preference coefficient. The proposed model introduces a new upside risk measures under fuzzy-stochastic setting to quantify the upward financial risk. To characterize and manage the model uncertainty, a fuzzy-stochastic distributional robust model is presented. Credibility distribution is utilized to handle fuzzy uncertainty, while box and polyhedral uncertainty sets are included to handle stochastic uncertainty. After that, tractable deterministic formulations of the uncertain model for two ambiguity sets are presented to solve the model. Finally, a number of insights are obtained by means of five test problems that are implemented in Python and resolved by one exact and five meta-heuristic algorithms. Experiments indicate that, when faced with uncertainty, two robust models have produced more stable solutions (16.23% and 19.67% higher stability, respectively) than nominal stochastic model. Further, a 4.68% decline in total cost and 3.27% increment in bio-diesel production can be achieved by increasing bio-diesel conversion rate to 10%. In addition, a 9.09% downturn in total cost and 10.44% rise in bio-plastic production can be obtained by 10% increment in bio-plastic conversion rate. Moreover, 45.38% more woman labours can be recruited by increasing the preference coefficient from −0.2 to +0.2.

Systemic Contagion

This article unifies existing systemic risk measures to present a novel intersecting notion of systemic contagion which captures institutional risk transmission amplified by a distressed financial system. The article constructs a test statistic for systemic contagion which demonstrates its systemic capacity in testing contagion under a series of numerical experiments. Using the new test the article constructs a forward-looking directed network to empirically measure the scale of systemic contagion between leading U.S. financial institutions. The proposed systemic contagion measure stands out as it provides timely warning signals of market crashes including that driven by the recent health-induced financial crisis.

Preoperative Posterior Stromal Ripples as Predictive Biomarkers of Visual Recovery After DMEK.

To investigate the role of preoperative posterior stromal ripples (pre-PSR) on visual acuity recovery after Descemet membrane endothelial keratoplasty (DMEK). This is a comparative case series retrospectively analyzing patients who underwent DMEK. Electronic records and imaging of DMEK patients were reviewed. The last preoperative and first postoperative available anterior segment optical coherence tomography scans for each eye were analyzed for the presence of pre-PSR. The difference in longitudinal trends of visual acuity recovery after DMEK was assessed in eyes with and without pre-PSR. The frequency of rebubbling and measures of proportional relative risk of rebubbling were analyzed according to the presence of preoperative and postoperative PSR. A total of 66 patients (71 eyes) were included. Pre-PSR were associated with lower preoperative visual acuity [0.6 (0.5) vs. 0.9 (0.6) LogMAR, P = 0.02] and higher central corneal thickness [613 (73.8) vs. 715.7 (129.6) micron, P < 0.001]. Eyes with pre-PSR had a slower visual recovery up to 3.5 months after surgery compared to eyes without pre-PSR and achieved lower final visual acuity [0.1 (0.2) vs. 0.3 (0.3) LogMAR, P = 0.02]. Cox proportional hazard ratios showed that postoperative PSR were associated with a greater risk of rebubbling [hazard ratio (95% CI), 7.1 (1.3, 39.5), P = 0.02] while pre-PSR were not. The presence of pre-PSR is associated with slower visual recovery and lower final visual acuity after DMEK while postoperative PSR confer a higher risk of rebubbling. PSR represent a valuable prognostic biomarker both before and after DMEK.

The Mediating Roles of Lung Function Traits and Inflammatory Factors on the Associations between Measures of Obesity and Risk of Lower Respiratory Tract Infections: A Mendelian Randomization Study.

Identifying mediators between obesity-related traits and lower respiratory tract infections (LRTIs) would inform preventive and therapeutic strategies to reduce the burden of LRITs. We aimed to recognize whether lung function and inflammatory factors mediate their associations. We conducted a two-step, two-sample Mendelian randomization (MR) analysis. Two-sample MR was performed on (1) obesity-related traits (i.e., body mass index [BMI], waist circumference [WC], and waist-to-hip ratio [WHR]) and LRTIs (i.e., acute bronchitis, acute bronchiolitis, bronchiectasis, influenza, and pneumonia), (2) obesity-related traits and potential mediators, and (3) potential mediators and LRTIs. Next, two-step MR was applied to infer whether the mediation effects exist. We found that C-reactive protein (CRP), interleukin-6 (IL-6), and forced expiratory volume in the first second (FEV1) mediated 32.59% (95% CI: 17.90%, 47.27%), 7.96% (95% CI: 1.79%, 14.14%), and 4.04% (95% CI: 0.34%, 7.74%) of the effect of BMI on pneumonia, and they mediated 26.90% (95% CI: 13.98%, 39.83%), 10.23% (95% CI: 2.72%, 17.73%), and 4.67% (95% CI: 0.25%, 9.09%) of the effect of WC on pneumonia, respectively. Additionally, CRP, forced vital capacity (FVC), and FEV1 mediated 18.66% (95% CI: 8.70%, 28.62%), 8.72% (95% CI: 1.86%, 15.58%), and 8.41% (95% CI: 2.77%, 14.06%) of the effect of BMI on acute bronchitis, and they mediated 19.96% (95% CI: 7.44%, 32.48%), 12.19% (95% CI: 2.00%, 22.39%), and 12.61% (95% CI: 2.94%, 22.29%) of the effect of WC on acute bronchitis, respectively. Health interventions linked to reducing inflammation and maintaining normal lung function could help mitigate the risk of obesity-related LRTIs.

Hitting the High-dimensional notes: an ODE for SGD learning dynamics on GLMs and multi-index models

Abstract We analyze the dynamics of streaming stochastic gradient descent (SGD) in the high-dimensional limit when applied to generalized linear models and multi-index models (e.g. logistic regression, phase retrieval) with general data-covariance. In particular, we demonstrate a deterministic equivalent of SGD in the form of a system of ordinary differential equations that describes a wide class of statistics, such as the risk and other measures of sub-optimality. This equivalence holds with overwhelming probability when the model parameter count grows proportionally to the number of data. This framework allows us to obtain learning rate thresholds for the stability of SGD as well as convergence guarantees. In addition to the deterministic equivalent, we introduce an SDE with a simplified diffusion coefficient (homogenized SGD), which allows us to analyze the dynamics of general statistics of SGD iterates. Finally, we illustrate this theory on some standard examples and show numerical simulations, which give an excellent match to the theory.