Risk Measures Research Articles

Investigating racial disparities in violence risk assessment using the Spousal Assault Risk Assessment Guide-Version 3 (SARA-V3): Structured professional judgment ratings and recidivism among Indigenous and non-Indigenous individuals.

Racial disparities in criminal justice outcomes are widely observed. In Canada, such disparities are particularly evident between Indigenous and non-Indigenous persons. The role of formal risk assessment in contributing to such disparities remains a topic of interest to many, but critical analysis has almost exclusively focused on actuarial or statistical risk measures. Recent research suggests that ratings from other common tools, based on the structured professional judgment model, can also demonstrate racial disparities. This study examined risk assessments produced using a widely used structured professional judgment tool, the Spousal Assault Risk Assessment Guide-Version 3, among a sample of 190 individuals with histories of intimate partner violence. We examined the relationships among race, risk factors, summary risk ratings, and recidivism while also investigating whether participants' racial identity influenced the likelihood of incurring formal sanctions for reported violence. Spousal Assault Risk Assessment Guide-Version 3 risk factor totals and summary risk ratings were associated with new violent charges. Indigenous individuals were assessed as demonstrating more risk factors and were more likely to be rated as high risk, even after controlling for summed risk factor totals and prior convictions. They were also more likely to recidivate and to have a history of at least one reported act of violence that did not result in formal sanctions. The results suggest that structured professional judgment guidelines can produce disparate results across racial groups. The disparities observed may reflect genuine differences in the likelihood of recidivism, driven by psychologically meaningful risk factors which have origins in deep-rooted systemic and contextual factors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Research on Tail Risk Hedging in the Digital Asset Market

In recent years, the digital asset market has experienced rapid growth, but at the same time, its implicit tail risks have gradually raised concerns, especially amidst the global economic downturn caused by the pandemic. This paper aims to study tail risks and propose hedging strategies. Based on the GARCH and VaR models, this paper uses weekly return data from July 2017 to December 2021 for Bitcoin digital cryptocurrency and traditional non-digital asset market indices such as stocks, bonds, gold, and commodities. Utilizing EViews and Excel, the study explores tail risk measurement and portfolio diversification effects in the digital asset market. The main findings are as follows: (1) During the sample period, cryptocurrency returns exhibited significant volatility, with weak correlations with returns from traditional non-digital assets. Further analysis revealed strong hedging effects of digital assets against the bond market but weaker effects against the gold and stock markets, as well as commodity markets; (2) Cryptocurrency in traditional investment portfolios can enhance the risk-return ratio by adding it to the asset mix.

A Divestment Model: Migration to Green Energy Investment Portfolio Concept

In a targeted terminal wealth generated by bond and risky assets, where the proportion of a risky asset is gradually being phased down, we propose a divestment model in a risky asset compensated by growth in a bond (insurance). The model includes the phase-down rate of the risky asset, c(t), the variable proportion, π(t), in a risky asset and the interest rate, r, of the bond. To guide the growth of the total wealth in this study, we compared it to the Øksendal and Sulem (Backward Stochastic Differential Equations and Risk Measures (2019)) total wealth for which c(t)=0, and π(t) is a constant. We employed the Fokker–Planck equation to find the variable moment, π(t), and the associated variance. We proved the existence and uniqueness of the first moment by Feller’s criteria. We have found a pair (c*(t),r*) for each π(t), which guarantees a growing total wealth. We have addressed the question whether this pair can reasonably be achieved to ensure an acceptable phase-down rate at a financially achievable interest rate, r*.

Pulpotomy versus pulpectomy in carious vital pulp exposure in primary incisors: a randomized controlled trial

BackgroundPulpotomy as a minimally invasive pulp therapy technique is the treatment of choice for carious pulp exposures, however many pediatric dentists perform pulpectomies in vital primary incisors. The aim of this split mouth randomized controlled study was to compare formocresol pulpotomy and zinc-oxide and eugenol pulpectomy in the treatment of vital pulp exposure in primary incisors.MethodsContralateral pairs of incisors were randomly assigned to receive pulpotomy or pulpectomy in children aging from 18 to 66 months old and were followed up for 12 months.Results39 pairs of incisors were included. Clinical and radiographical success rates showed no statistical significant difference (p = 1, p = 0.8 respectively). Relative risk measures for clinical success rates (RR = 1.03, 95%CI 0.87 to 1.23) and for radiographic success rates (RR = 1.03, 95%CI 0.83 to 1.29) with CIs including number one showing no difference between the two groups. The Survival rate using Kaplan-Meier survival analysis score showed 82% for pulpotomy and 74% for pulpectomy at 12 months (P = 0.2).ConclusionsBoth pulpotomy and pulpectomy techniques can be used successfully in the treatment of carious vital pulp exposure in primary incisors.Trial registrationThe trial was retrospectively registered in Clinicaltrials .gov with this identifier NCT05589025 on 21/10/2022.

Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index

Systemic risk refers to the potential for a disruption in one part of a financial system to trigger a cascade of adverse effects, impacting the functioning of the system. Despite the progress on novel systemic risk measures, research on dynamics of systemic risk network structure and its community effect is still in its initial state. In this study, we utilize price data from 107 representative Chinese stocks spanning the period from 2017 to 2022. A systemic risk network is derived from the Risk Transmission Index based on TENET and the QR–Lasso model. By utilizing DBSCAN, HITS and community detection algorithms on the network, we aim to propose a more suitable definition of systemically important companies, explore the interrelationships between companies, and discuss its plausible reasons for dynamics structural changes. The empirical findings demonstrate a substantial involvement of insurance companies in both contributing to and receiving systemic risk within the analyzed context. We identify prominent risk output and input centers, and emphasize the profound impact of the COVID-19 pandemic on the dynamics of systemic risk.

Worst-case risk with unspecified risk preferences

In this paper, we study the worst-case distortion risk measure for a given risk when information about distortion functions is partially available. We obtain the explicit forms of the worst-case distortion functions for several different sets of plausible distortion functions. When there is no concavity constraint on distortion functions, the worst-case distortion function is independent of the risk to be measured and the corresponding worst-case distortion risk measure is the weighted average of the VaR's of the risk for all decision makers. When the concavity constraint is imposed on distortion functions and the set of concave distortion functions is defined by the riskiness of one single risk, the explicit form of the worst-case distortion function is obtained, which depends the risk to be measured. When the set of concave distortion functions is defined by the riskiness of multiple risks, we reduce the infinite-dimensional optimization problem to a finite-dimensional optimization problem which can be solved numerically. Finally, we apply the worst-case risk measure to optimal decision making in reinsurance.

Disparities in mortality risk after diagnosis of hematological malignancies in 185 countries: A global data analysis

This study was to report proxy measures for mortality risk in patients with hematological malignancies across 185 countries globally and explore its association with their socioeconomic status and treatment. The incidence, mortality, and 5-year prevalence data were extracted from the GLOBOCAN database. The data regarding the human development index (HDI), gross national income (GNI), vulnerability index, and concordance with cancer Essential Medicines List (EML) were obtained from open-source reports. The ratio of mortality to 5-year-prevalence (MPR) and that of mortality to incidence (MIR) were calculated and age-standardized using Segi's world standard population. Finally, the possible associations were assessed using Pearson correlation analyses. In 2020, the global incidence, mortality, and 5-year prevalence of HMs were 1,278,362, 711,840, and 3,616,685, respectively. Global age-standardized MPR and MIR were 0.15 and 0.44, respectively; they varied significantly among 6 regions, 185 countries, 4 HM types, and 4 HDI groups worldwide. Older populations always had higher ratios. The correlation of MPRs and MIRs with HDI, GNI, and concordance with cancer EML was negative, whereas it was positive with the vulnerability index (lower was better). Increasing access to cancer drugs in resource-limited regions with a focus on vulnerable children may aid in reducing HM-related mortality risk.

Backwards walking speed reserve in persons with multiple sclerosis

BackgroundDecreased gait speed is common in persons with Multiple Sclerosis (PwMS) and has been associated with elevated fall risk. The walking speed reserve (WSR) indicates the ability to increase gait speed on demand and has previously been examined in PwMS. Backward walking is a sensitive measure of fall risk in PwMS; however, no studies have reported on the utility of backward walking speed reserve (BW-WSR) as a clinical assessment tool of functional mobility or fall risk in PwMS, nor have they associated this measure with cognition. Methods23 PwMS completed walking trials at their preferred walking speed (PWS) and maximal walking speed (MWS). Participants performed these walking trials in both the forward (FW) and backward direction (BW). The forward walking speed reserve (FW-WSR) was calculated as the difference between MWS and PWS in the forward direction, while the backward walking speed reserve (BW-WSR) was calculated as the difference between MWS and PWS in the back backward direction. Correlation analyses examined the relationship between the FW- and BW-WSR with clinical assessments of functional mobility (the timed up-and-go) as well as cognitive functioning (the Symbol Digit Modalities Test, the Brief Visuospatial Memory Test-Revised, the California Verbal Learning Test, and the Trail Making Test A and B). Correlations also examined the relationship between FW- and BW-WSR with prospective falls. ResultsA lower BW-WSR was associated with disease severity and poorer performance on clinical walking and balance assessment, as well as with decreased information processing speed and attentional performance. Interestingly, FW-WSR showed similar relations. Neither FW- or BW-WSR were associated with prospective risk in this small sample of PwMS. ConclusionThe BW-WSR did not offer a distinct advantage over other measures, such as the FW-WSR, PWS, or MWS, in the forward or backward direction. The selection of the most sensitive clinical measures of functional mobility and fall risk is crucial; our study holds valuable clinical implications for PwMS by providing novel insights into functional mobility assessments in PwMS.

Financial contagion among the GSIBs and regulatory interventions

This paper compares three methods for assessing the contagion of risk among ten Globally Significant International Banks, known as GSIBs, listed on the New York Stock Exchange with daily and weekly data sets from 2007 to 2020, based on Machine Learning and Network Analysis. In particular we identify the banks which are the largest net sources or transmitters of risk, and net receptors of risk. We also examine the response of regulatory actions, in the form of fines and BIS Bin Classification for capital adequacy.Under alternative risk measures, of Range Volatility (RV) of share prices, Credit Default Swap (CDS) premia, and Conditional Value at Risk (ΔCoVar), there is a stronger and significant connection between Contagion and the BIS Bin classifications relative to the connections between Contagion and banking fines, either in the amount or frequency of the fines. These results show that BIS bin classifications respond positively to underlying signals of increased contagion in the form of Range Volatility (RV) and ΔCoVar measures but not to CDS risk premia.

Statistical Inference and Application of Asymmetrical Generalized Pareto Distribution Based on Peaks-Over-Threshold Model

Generalized Pareto distribution (GPD), an asymmetrical distribution, primarily models exceedances over a high threshold in many applications. Within the peaks-over-threshold (POT) framework, we consider a new GPD parameter estimation method to estimate a common tail risk measure, the value at risk (VaR). The proposed method is more suitable for the POT framework and makes full use of data information. Specifically, our estimation method builds upon the generalized probability weighted moments method and integrates it with the nonlinear weighted least squares method. We use exceedances for the GPD, minimizing the sum of squared differences between the sample and population moments of a function of GPD random variables. At the same time, the proposed estimator uses three iterations and assigns weight to further improving the estimated performance. Under Monte Carlo simulations and with a real heavy-tailed dataset, the simulation results show the advantage of the newly proposed estimator, particularly when VaRs are at high confidence levels. In addition, by simulating other heavy-tailed distributions, our method still exhibits good performance in estimating misjudgment distributions.

Simulating Credit Loss Distributions: Empirical Versus the Vasicek Model

Because credit losses can be substantial, managing credit risk is a focus area of risk measurement and management. It is important for financial institutions to select credit risk models that accurately forecast losses. The Basel Committee on Banking Supervision (BCBS) chose the closed-form single risk factor Vasicek model for regulatory capital calculations. In this article, its forecast accuracy is compared with empirical loss distributions using simulated probabilities of default and losses given default. The effect of altering probabilities of default on asset correlations was analysed and how this affects credit portfolio loss distributions. The robustness of the Vasicek model against five different portfolios with unique compositions was explored: results highlight two key findings. Firstly, the Vasicek model is a good approximation of credit losses for a portfolio that does not contain dominating loans (it is, after all, based on the assumption of large-scale homogeneity). Secondly, the Vasicek model is a good approximation for expected loss (ELs) but lacks accuracy when determining extreme unexpected losses (ULs). Finally, credit capital requirements as a function of two variables are presented which reveals novel ways of viewing these values.

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.

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.

Worst-case risk measures of stop-loss and limited loss random variables under distribution uncertainty with applications to robust reinsurance

Stop-loss and limited loss random variables are two important transforms of a loss random variable and appear in many modeling problems in insurance, finance, and other fields. Risk levels of a loss variable and its transforms are often measured by risk measures. When only partial information on a loss variable is available, risk measures of the loss variable and its transforms cannot be evaluated effectively. To deal with the situation of distribution uncertainty, the worst-case values of risk measures of a loss variable over an uncertainty set, describing all the possible distributions of the loss variable, have been extensively used in robust risk management for many fields. However, most of these existing results on the worst-case values of risk measures of a loss variable cannot be applied directly to the worst-case values of risk measures of its transforms. In this paper, we derive the expressions of the worst-case values of distortion risk measures of stop-loss and limited loss random variables over an uncertainty set introduced in Bernard et al. (2023). This set represents a decision maker’s belief in the distribution of a loss variable. We find the distributions under which the worst-case values are attainable. These results have potential applications in a variety of fields. To illustrate their applications, we discuss how to model optimal stop-loss reinsurance problems and how to determine optimal stop-loss retentions under distribution uncertainty. Explicit and closed-form expressions for the worst-case TVaRs of stop-loss and limited loss random variables and optimal stop-loss retentions are given under special forms of the uncertainty set. Numerical results are presented under more general forms of the uncertainty set.

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.