Risk Measures Research Articles

Multimodal exercise training to reduce frailty in people with multiple sclerosis: study protocol for a pilot randomized controlled trial

BackgroundFrailty, a syndrome characterized by decreased reserve and resistance to stressors across multiple physiologic systems, is highly prevalent in people living with multiple sclerosis (pwMS), independent of age or disability level. Frailty in MS is strongly associated with adverse clinical outcomes, such as falls, and may aggravate MS-related symptoms. Consequently, there is a pressing necessity to explore and evaluate strategies to reduce frailty levels in pwMS. The purpose of this pilot randomized controlled trial (RCT) will be to examine the feasibility and preliminary efficacy of a multimodal exercise training program to reduce frailty in pwMS.MethodsA total of 24 participants will be randomly assigned to 6 weeks of multimodal exercise or to a waitlist control group with a 1:1 allocation. PwMS aged 40–65 years and living with frailty will be eligible. The multimodal exercise program will consist of cognitive-motor rehabilitation (i.e., virtual reality treadmill training) combined with progressive, evidence-based resistance training. At baseline and post-intervention, participants will complete the Evaluative Frailty Index for Physical Activity (EFIP), measures of fall risk, and quality of life. Frailty-related biomarkers will also be assessed. In addition, the feasibility of the multimodal exercise program will be systematically and multidimensionally evaluated.DiscussionTo date, no RCT has yet been conducted to evaluate whether targeted exercise interventions can minimize frailty in MS. The current study will provide novel data on the feasibility and preliminary efficacy of multimodal exercise training as a strategy for counteracting frailty in pwMS.Trial registrationClinicalTrials.gov, NCT06042244 (registered in September 2023).

Modeling the risk of single-vehicle run-off-road crashes on horizontal curves using connected vehicle data

Crash risk measures (CRMs) are widely used in safety analysis to complement crash reports. However, none of the existing CRMs are specifically developed for modeling the risk of single-vehicle run-off-road (SVROR) crashes, especially those on horizontal curves. This paper proposes a novel crash risk measure for modeling SVROR crash risk using connected vehicle data. The proposed SVROR crash risk measure (SVROR-CRM) is based on the concept of tetraquark in particle physics. It utilizes the adjusted position deviation risk force (Fposirisk) and adjusted attitude deviation risk moment (Γattirisk) to quantify SVROR crash risk. The SVROR crash risk is then estimated by the joint probability of Fposirisk and Γattirisk using a peak-over threshold approach. The risk threshold is automatically determined via a mean absolute error function. The SVROR-CRM is validated using connected vehicle and crash data from sixteen curves on Interstate 80 in Wyoming. The results suggest that the estimated SVROR crash risks well match historical crash records. Also, it is found that attitude deviation poses a higher risk of SVROR crash than position deviation on horizontal curves. Therefore, it is critical for drivers to steer properly on curves to minimize SVROR crash risks. The proposed approach bridges an important gap in crash risk measure research and can be used to estimate SVROR crash risk and identify unsafe trajectories and high-crash locations and/or periods on highway horizontal curves.

Scalar BSDEs of iterated-logarithmically sub-linear generators with integrable terminal values

We establish a general existence and uniqueness result of integrable adapted solutions to scalar backward stochastic differential equations with integrable parameters, where the generator g has an iterated-logarithmically uniform continuity in the second unknown variable z. The result improves our previous one in Fan et al. (2023), and can be applied to the (conditional) g-expectation and the star-shaped risk measure.

Risk-driven optimal scheduling of renewable-oriented energy hub under demand response program and energy storages: A novel Entropic value-at-risk modeling

Energy hubs have revolutionized the field of energy management, emerging as state-of-the-art solutions for achieving efficient and sustainable energy systems. These hubs act as integrated platforms that seamlessly combine a variety of energy technologies, facilitating the synergistic interaction of multi-carrier energy systems with diverse energy sources. This paper outlines a framework for an energy hub that integrates a wide range of energy technologies, encompassing electrical, thermal, and cooling systems, alongside renewable resources and storage units. The proposed model incorporates demand response programs (DRPs) to effectively manage energy demand, optimize load distribution, enhance grid stability, and achieve substantial cost reductions. The effectiveness of the proposed framework is demonstrated through careful analysis of four distinct case studies, giving its potential in diverse energy management scenarios. To tackle uncertainties associated with power, heating, and cooling demands, as well as power and gas market prices, wind speed, and solar irradiation, this study utilizes EVaR as a stochastic modeling approach. The EVaR is a novel coherent risk measure incorporated to enhance risk management practices and surpass the limitations of traditional value-at-risk measures. According to the obtained results, due to the conservativeness of the operator, the income has decreased by 23 % while the system operation cost has increased by 36 % in the presence of the EVaR optimization approach.

Porphyromonas gingivalis Infection is Associated with Increased Vascular Inflammation in Patients with and Without Coronary Artery Disease

Vascular inflammation is a critical measure of risk for major adverse cardiac events in patients with and without known coronary artery disease. It is recognized that patients with poor oral hygiene tend to have an increased prevalence of coronary artery disease and myocardial infarction. Porphyromonas gingivalis (PG) is the keystone pathogen leading to periodontal disease. Despite evidence suggesting a link between active PG infection and coronary artery disease, there is limited data investigating whether PG can contribute to increased markers of vascular inflammation in patients with coronary artery disease. In this study, we studied 112 consecutive patients who presented at a community preventive cardiology clinic who consented to be assessed for the presence of PG. Subjects were enrolled in a mouth along assessment with measures of vascular inflammation, which was part of their routine clinical care. The average age of patients was 70.4 ± 8.5 years with 48.6% of the patients positive for PG and 20.5% with a history of coronary artery disease (CAD). The distribution of PG positivity was 48.3% and 34.8% in patients without and with a history of CAD, respectively. Patients with CAD have significantly greater levels of high sensitivity C-reactive protein (hsCRP_ (1.3 ± 0.3 vs. 3.2 ± 1.1 mg/dL, p = 0.04) and MPO (245.1 ± 19.4 vs. 371.4 ± 39.1 pmol/L, p = 0.003). Patients without a history of CAD, only Myeloperoxidase (MPO) was significantly increased. Statin use was significantly greater in those patients with a history of CAD. Interestingly, statin use tended to be greater in PG+ patients (71% vs. 62%) regardless of CAD status. The data from this early study demonstrate a link between active PG infection and increase in markers of vascular inflammation, especially in patients with established CAD. The findings suggest PG as a marker of cardiovascular risk, especially in patients with residual inflammatory risk.

Measurement and Forecasting of Systemic Risk: A Vine Copula Grouped-CoES Approach

Measurement and Forecasting of Systemic Risk: A Vine Copula Grouped-CoES Approach

Landscape ecological risk evaluation and prediction under a wetland conservation scenario in the Sanjiang Plain based on land use/cover change

Landscape ecological risk is the term used to describe potential negative effects of the relationship between environmental processes and landscape structure as a result of both natural and human interactions. The scientific assessment of past and prediction future ecological risks to landscapes is the basis for achieving sustainable regional development. In this study, we built an ecological risk measurement system characterized by variability in landscape composition to reflect the components of landscape ecological risk. In particular, the CA-Markov model was used to simulate the spatial pattern of land use in the Sanjiang Plain, China, under a wetland protection scenario. The evolution of the spatiotemporal land use trend in the study area was then analyzed for past (2011–2021) and future (2021–2036) periods. The results identified that construction land, farmland, water, and grassland areas exhibited an increasing trend from 2011 to 2021, while forest and wetland areas decreased each year. From 2021 to 2036, the grassland areas exhibited a downward trend, while the other land use types maintained the same previous trend, and all land use types experienced a slowdown in the degree of change. The major landscape ecological risk levels in the research region changed from medium-risk and low-risk levels (2011–2021) to low-risk and lower-risk levels (2021–2036). An evident decrease in risk is observed for farmland and construction land in the mid-region of the research district and forests in the south. In contrast, high-risk and higher-risk levels regions were smaller and distributed in the Songhua River basin in the northeast of the research region, wetland nature reserves, and the Xingkai Lake area, with an overall reduction of 59%. Based on the results, we propose several risk prevention and control management strategies. This research acts as a basis for determining Sanjiang Plain's future rational distribution of land resources and provides an example for the reduction of ecological risks in similar regions.

Distortion risk measures for tail-risk multicriteria decision-making based on a probabilistic linguistic environment

Probabilistic linguistic term sets (PLTSs) provide a flexible tool to express linguistic preferences, and several multi-criteria decision models based on PLTSs have been recently developed. In this framework, distortion risk measures are extensively used in finance and insurance applications, but are rarely applied in fuzzy systems. In this paper, distortion risk measures are applied to fuzzy tail decisions. In particular, three tail risk measurement methods are put forward, referred to as probabilistic linguistic VaR (PLVaR), expected probability linguistic VaR (EPLVaR), and Wang tail risk measure and extensively study their properties. Our novel methods help to clarify the connections between distortion risk measure and fuzzy tail decision-making. In particular, the Wang tail risk measure is characterized by consistency and stability of decision results. The criteria and expert weights are unknown or only partially known during the decision making process, and the maximising PLTSs deviations are showed how to determine them. The theoretical results are showcased on an optimal stock fund selection problem, where the three tail risk measures are compared and analyzed.

RETRACTED: A comparative study of fuzzy multi-objective investment project portfolio selection and optimization based on financial return and different risk measurements

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Is there an optimal level of leverage? The case of banks and non-bank institutions in Europe

In this paper, we evaluate whether banks and non-banks size and systemic risk are affected by their level of leverage. We implement a threshold analysis to a sample of European traditional banks and non-banks (Finance services and Real Estate Finance Services) over 2006:1-2019:4. We find that Finance Services show positive co-movements between leverage and size, independently of the level of leverage, while for traditional banks the level of leverage matters. Both banks and non-banks show positive correlation between leverage and systemic risk. During periods of crisis, SRISK measure of systemic risk allows to identify an optimal level of leverage.

GLOBAL PATTERNS AND EXTREME EVENTS IN SOVEREIGN RISK PREMIA: A FUZZY S DEEP LEARNING COMPARATIVE

Investment in foreign countries has become more common nowadays and this implies that there may be risks inherent to these investments, being the sovereign risk premium the measure of such risk. Many studies have examined the behaviour of the sovereign risk premium, nevertheless, there are limitations to the current models and the literature calls for further investigation of the issue as behavioural factors are necessary to analyse the investor’s risk perception. In addition, the methodology widely used in previous research is the regression model, and the literature shows it as scarce yet. This study provides a model for a new of the drivers of the government risk premia in developing countries and developed countries, comparing Fuzzy methods such as Fuzzy Decision Trees, Fuzzy Rough Nearest Neighbour, Neuro-Fuzzy Approach, with Deep Learning procedures such as Deep Recurrent Convolution Neural Network, Deep Neural Decision Trees, Deep Learning Linear Support Vector Machines. Our models have a large effect on the suitability of macroeconomic policy in the face of foreign investment risks by delivering instruments that contribute to bringing about financial stability at the global level.

Toward green central banking: Proposing an augmented Taylor rule

This study explores the greening of the Federal Reserve monetary policy from January 2000 to August 2023. We discuss the challenges inherent in the US central bank's pursuit of an alternative monetary policy to deal with its main mission of price stability while also taking climate change into account, and propose an augmented green Taylor rule. In particular, we reconsider the Taylor rule model while allowing the Fed to conduct its monetary policy, taking both physical and transition risks into account. To our knowledge, this is the first study on the sensitivity of monetary instruments and monetary policy with respect to physical and transition risks. Our analysis yields two interesting findings. First, Fed's greening of US monetary policy appears feasible, as taking the climate risk factors into account in formulating the policy is not out of step with the main price stability mandate and could yield a climate risk premium. Accordingly, the climate risk measure related to transition and physical risk drives the federal funds rate, and an augmented green Taylor rule would fit the data better than the basic Taylor rule. Second, we find that the Fed's reaction to news related to transition and physical risks in particular and climate risk in general has been more pronounced since the post COVID-19 subperiod than over the period as a whole.

The Diversification Benefits of Foreign Real Estate: Evidence from 40 Years of Data

We investigate the potential of global real estate to improve the long-term performance of a US equity portfolio, utilizing a recent dataset of 40 years’ worth of US stocks, US real estate, 13 foreign stock markets, and 13 foreign real estate markets across diverse regions. Despite a modest performance in terms of risk and return, foreign real estate has consistently lower correlations with US stocks compared to foreign equities. Rolling correlation analysis indicates that foreign real estate markets remain relatively segmented compared to foreign equity, despite increasing financial market correlations over time. Efficient frontier analysis demonstrates that portfolios including foreign real estate consistently outperform those limited to US stocks and US real estate or those including foreign stocks, indicating the importance of foreign real estate in optimizing portfolio performance. Subperiod analysis reveals that foreign real estate retains its diversification benefits even in the latter, more integrated period. Our results are robust when using Conditional Value-at-Risk as a measure of risk. Overall, our findings highlight the persistent diversification benefits and superior risk-adjusted returns from incorporating foreign real estate into US equity portfolios.

Grace Under Pressure: a mixed methods impact assessment of a verbatim theatre intervention to improve healthcare workplace culture

BackgroundHealthcare workplace mistreatment has been documented globally. Poor workplace behaviour, ranging from incivility to bullying and harassment, is common in healthcare, and contributes significantly to adverse events in healthcare, poor mental health among healthcare workers, and to attrition in the healthcare workforce, particularly in junior years. Poor workplace behaviour is often normalised, and is difficult to address. Verbatim theatre, a form of research informed theatre in which plays are created from informants’ exact words only, is particularly suited to facilitating workplace culture change by raising awareness about issues that are difficult to discuss. The objective of this study was to assess the impact of the verbatim theatre play ‘Grace Under Pressure’ on workplace culture in NSW hospitals.MethodsThe intervention was conducted in 13 hospitals from 8 Local Health Districts (LHDs) in NSW, Australia, in October and November 2019, with aggregated impact across all sites measured by a bespoke survey (‘Pam McLean Centre (PMC) survey’) at the conclusion of the intervention. This study was conducted in 3 Local Health Districts (one urban, one regional, one remote), with data collection conducted in November–December 2019 and December 2020. The study design was a mixed methods assessment of the play’s impact using (1) validated baseline measures of psychosocial risk, analysed descriptively, (2) overall findings from the PMC survey above, analysed descriptively, (3) interviews conducted within a month of the intervention, analysed thematically and (4) interviews conducted one year later, analysed thematically.ResultsHalf (51.5%) of the respondents (n = 149) to the baseline survey had scores indicating high risk of job strain and depressive symptoms. Of 478 respondents to the PMC survey (response rate 57%), 93% found the play important, 92% recommended others see the play, 89% considered that it stimulated thinking about workplace behaviour, and 85% that it made discussing these issues easier. Thematic analysis of interviews within one month (n = 21) showed that the play raised awareness about poor workplace behaviour and motivated behaviour change. Interviews conducted one year later (n = 6) attributed improved workplace culture to the intervention due to improved awareness, discussion and capacity to respond to challenging issues.ConclusionsVerbatim theatre is effective in raising awareness about difficult workplace behaviour in ways that motivate behaviour change, and hence can be effective in catalysing real improvements in healthcare workplace culture. Creative approaches are recommended for addressing similarly complex challenges in healthcare workforce retention.

News event memory in amnestic and non-amnestic MCI, heritable risk for dementia, and subjective memory complaints

Robust and sensitive clinical measures are needed for more accurate and earlier detection of Alzheimer's disease (AD), for staging preclinical AD, and for gauging the efficacy of treatments. Mild impairment on episodic memory tests is thought to indicate a cognitive risk of developing AD and mild cognitive impairment (MCI), considered to be a transitional stage between normal aging and AD. Novel tests of semantic memory, such as memory for news events, are also impaired early on but have received little clinical attention even though they may provide a novel way to assess cognitive risk for AD. We examined memory for news events in older adults with normal cognition (NC, N = 34), amnestic MCI (aMCI, N = 27), or non-aMCI (N = 10) using the Retrograde Memory News Events Test (RM-NET). We asked if news event memory was sensitive to 1) aMCI and also non-aMCI, which has rarely been examined, 2) genetic risk for dementia (positive family history of any type of dementia, presence of an APOE-4 allele, or polygenic risk for AD), and 3) subjective memory functioning judgments about the past. We found that both MCI subgroups exhibited impaired RM-NET Lifespan accuracy scores together with temporally-limited retrograde amnesia. For the aMCI group amnesia extended back 45 years prior to testing, but not beyond that time frame. The extent of retrograde amnesia could not be reliably estimated in the small non-aMCI group. The effect sizes of having MCI on the RM-NET were medium for the non-aMCI group and large for the aMCI group, whereas the effect sizes of participant characteristics on RM-NET accuracy scores were small. For the combined MCI group (N = 37), news event memory was significantly related to positive family history of dementia but was not related to the more specific genetic markers of AD risk. For the NC group, news event memory was not related to any measure of genetic risk. Objective measures of past memory from the RM-NET were not related to subjective memory judgements about the present or the recent past in either group. By contrast, when individuals subjectively compared their present versus past memory abilities, there was a significant association between this judgment and objective measures of the past from the RM-NET (direct association for the NC group and inverse for the MCI group). The RM-NET holds significant promise for early identification of those with cognitive and genetic risk factors for AD and non-AD dementias.

Systemic Financial Risk Forecasting with Decomposition–Clustering-Ensemble Learning Approach: Evidence from China

Establishing a scientifically effective systemic financial risk early warning model is of great significance for prudently mitigating systemic financial risks and enhancing the efficiency of financial supervision. Based on the measurement of systemic financial risk and the network sentiment index of 47 financial institutions, this study adopted the “decomposition–reconstruction–integration” approach, utilizing techniques such as extreme-point symmetric empirical mode decomposition (ESMD), empirical mode decomposition (EMD), variational mode decomposition (VMD), hierarchical clustering, fast independent component analysis (FastICA), attention mechanism, bidirectional long short-term memory neural network (BiLSTM), support vector regression (SVR), and their combination, to construct a systemic financial risk prediction model. The empirical results demonstrate that decomposing and reconstructing relevant indicators before predicting systemic financial risks can enhance prediction accuracy. Among the proposed models, the ESMD-HFastICA-BiLSTM-Attention model exhibits superior performance in systemic financial risk early warning.

Risk Measures’ Duality on Ordered Linear Spaces

The aim of this paper is to provide a dual representation of convex and coherent risk measures in partially ordered linear spaces with respect to the algebraic dual space. An algebraic robust representation is deduced by weak separation of convex sets by functionals, which are assumed to be only linear; thus, our framework does not require any topological structure of the underlying spaces, and our robust representations are found without any continuity requirement for the risk measures. We also use such extensions to the representation of acceptability indices.

Non–High-Density Lipoprotein Cholesterol Levels From Childhood to Adulthood and Cardiovascular Disease Events

Elevated non-high-density lipoprotein cholesterol (non-HDL-C; a recommended measure of lipid-related cardiovascular risk) is common in children and increases risk of adult cardiovascular disease (CVD). Whether resolution of elevated childhood non-HDL-C levels by adulthood is associated with reduced risk of clinical CVD events is unknown. To examine the associations of non-HDL-C status between childhood and adulthood with incident CVD events. Individual participant data from 6 prospective cohorts of children (mean age at baseline, 10.7 years) in the US and Finland. Recruitment took place between 1970 and 1996, with a final follow-up in 2019. Child (age 3-19 years) and adult (age 20-40 years) non-HDL-C age- and sex-specific z scores and categories according to clinical guideline-recommended cutoffs for dyslipidemia. Incident fatal and nonfatal CVD events adjudicated by medical records. Over a mean length of follow-up of 8.9 years after age 40 years, 147 CVD events occurred among 5121 participants (60% women; 15% Black). Both childhood and adult non-HDL-C levels were associated with increased risk of CVD events (hazard ratio [HR], 1.42 [95% CI, 1.18-1.70] and HR, 1.50 [95% CI, 1.26-1.78] for a 1-unit increase in z score, respectively), but the association for childhood non-HDL-C was reduced when adjusted for adult levels (HR, 1.12 [95% CI, 0.89-1.41]). A complementary analysis showed that both childhood non-HDL-C levels and the change between childhood and adulthood were independently associated with the outcome, suggesting that from a preventive perspective, both childhood non-HDL-C levels and the change into adulthood are informative. Compared with those whose non-HDL-C levels remained within the guideline-recommended range in childhood and adulthood, participants who had incident non-HDL-C dyslipidemia from childhood to adulthood and those with persistent dyslipidemia had increased risks of CVD events (HR, 2.17 [95% CI, 1.00-4.69] and HR, 5.17 [95% CI, 2.80-9.56], respectively). Individuals who had dyslipidemic non-HDL-C in childhood but whose non-HDL-C levels were within the guideline-recommended range in adulthood did not have a significantly increased risk (HR, 1.13 [95% CI, 0.50-2.56]). Individuals with persistent non-HDL-C dyslipidemia from childhood to adulthood had an increased risk of CVD events, but those in whom dyslipidemic non-HDL-C levels resolve by adulthood have similar risk to individuals who were never dyslipidemic. These findings suggest that interventions to prevent and reduce elevated childhood non-HDL-C levels may help prevent premature CVD.

A Systemic Review of Adolescent Supracondylar Fractures: What Is the Surgical Treatment, Open Reduction With Internal Fixation (ORIF) or Closed Reduction With Percutaneous Pinning (CRPP)?

Supracondylar humerus (SCH) fractures represent the most frequent elbow injury in young children. These fractures can be treated through either closed reduction with percutaneous pinning (CRPP) or open reduction with internal fixation (ORIF). Yet, the optimal treatment option for adolescents remains unclear. This research contrasts the results of CRPP and ORIF treatments for distal humerus fractures in adolescents. In June 2023, we conducted a comprehensive search of PUBMED, OVID MEDLINE, Web of Science, Cochrane Central Register of Controlled Trials, and various trial registries without any time restrictions. We evaluated the quality of qualifying studies using the Methodological Index for Non-randomized Studies (MINORS) and Cochrane risk measures for bias. We extracted data particularly related to patient demographics, fracture details, medical procedures followed, complications encountered, and the resulting outcomes. Out of the 488 studies identified, only four satisfied the inclusion criteria. Both methods illustrated comparable outcomes in terms of range of motion, averaging approximately 118 degrees in the ORIF group versus a span of 114 to 128 degrees in the CRPP group. The immobilization period varied, spanning 10 to 13 days for ORIF versus 24 to 29 days for CRPP. Despite this, CRPP displayed a decreased necessity for additional surgery. Notably, one study indicated a higher frequency of heterotopic ossification within the ORIF group. This review indicates that both CRPP and ORIF are effective for treating supracondylar fractures in adolescents, yielding similar results. However, CRPP has a lower need for follow-up surgery. Future studies with larger sample sizes are needed to solidify these findings, providing stronger guidance for treatment.

The mediating effect of bank risk on the relationship between diversification strategies and discretionary loan loss provisions

Purpose This study aims to investigate the effect of income and asset diversification on earnings management using discretionary loan loss provisions (LLP) in banks, and the role of risk level in mediating this effect. Design/methodology/approach A sample of banks operating in Middle East and North Africa countries was used to test the mediation model of Baron and Kenny (1986) with different measures of diversification and risk. Findings The results show that bank income and asset diversification have unique and combined effects on earnings management. The results also support the idea that a risk-mediating effect contributes to explaining this relationship among banks. Specifically, bank diversification strategies positively affect LLP-based earnings management by increasing bank risk. This result is relevant for conventional banks. However, only a direct and positive effect of diversification strategies on LLP-based earnings management can be observed in Islamic banks, and the indirect effect is not supported. Originality/value This study extends previous research by examining the unique and combined effects of income and asset diversification strategies on earnings management in the banking sector. Specifically, it provides new evidence that diversification strategies increase LLP-based earnings management, both directly and indirectly, through bank risk.