Risk Measures Research Articles

Meta-analysis of breast cancer risk for individuals with PALB2 pathogenic variants.

Multigene panel testing now allows efficient testing of many cancer susceptibility genes leading to a larger number of mutation carriers being identified. They need to be counseled about their cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Multiple studies reported risk estimates for breast cancer (BC) conferred by pathogenic variants in PALB2. Due to the diverse modalities of reported risk estimates (age-specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes, a meta-analysis combining these estimates is necessary to accurately counsel patients with this mutation. However, this is not trivial due to heterogeneity of studies in terms of study design and risk measure. We utilized a recently proposed Bayesian random-effects meta-analysis method that can synthesize estimates from such heterogeneous studies. We applied this method to combine estimates from 12 studies on BC risk for carriers of pathogenic PALB2 mutations. The estimated overall (meta-analysis-based) risk of BC is 12.80% (6.11%-22.59%) by age 50 and 48.47% (36.05%-61.74%) by age 80. Pathogenic mutations in PALB2 makes women more susceptible to BC. Our risk estimates can help clinically manage patients carrying pathogenic variants in PALB2.

Climate risk and financial stability: evidence from syndicated lending

We study the impact of unexpected climate shocks on banks' individual and systemic risks. Employing climate risk measures developed using the Billion-Dollar Weather and Climate Disasters data from the National Oceanic and Atmospheric Administration (NOAA) and Dealscan syndicated lending data, we find that climate risk exposure acquired through cross-state lending increases banks' individual and systemic risks. We also find that bank profitability helps offset some of the adverse effects of climate risk. Banks reduce lending and increase loan loss reserves after the experience of an unexpected climate shock. The loan-level analysis reveals that the effect of climate risk exposure on bank risks is more pronounced for loans granted for operating and capital expenditures. We contribute to a growing literature on the impact of climate risk on financial stability and the development of robust measures of climate risk for banks.

Risk Measurement of Investment Portfolio Using Var and Cvar from The Top 10 Traded Stocks on the IDX

Portfolio investment reflects a commitment to the allocation of funds or resources which is considered a strategic step in managing assets to achieve future profits. This research begins with a careful analysis of a portfolio consisting of the ten best stocks on the Indonesia Stock Exchange (IDX). Through in-depth processing and analysis of stock data, the dynamics of performance, risk and volatility involved in each investment commitment are revealed. The Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) methods at the 90%, 95%, and 99% confidence levels take centre stage, highlighting the potential for increased losses as confidence levels increase. In-depth analysis illustrates that CVaR, considering the extreme risks in the distribution, provides a more holistic picture than VaR. With a VaR (99%) value of IDR 84,973,959,424 and CVaR (99%) of IDR 471,795,822,064, this research provides a concrete picture of potential risks at the highest level of confidence. These results confirm that CVaR has a crucial role in identifying and measuring the potential for more significant losses, especially in the face of unexpected market uncertainty. As a guide for investment decision makers, this research forms an important basis for carefully considering the level of risk and potential return at various levels of confidence. This allows the development of smarter and more informed investment strategies.

The Roles of Housing and Household Characteristics in U.S. Residential Heat Risk

This study uses data from the 2020 nationally-representative U.S. Residential Energy Consumption Survey (RECS) to analyze the under-explored relationship between housing type, household characteristics, and two novel heat health risk measures. We use a combination of descriptive analysis and logistic regression models. The results show that renters and residents of lower-cost housing types – multifamily and manufactured housing – are much more likely to keep their homes at unhealthy temperatures and require medical attention due to hot indoor temperatures. We also find stark disparities in heat risk for households with lower incomes, seniors, and Black, Native American, multiracial, and Hispanic households.

Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market.

Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors.

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.

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

Competitiveness in the global market is getting more intense. Due to resource and budget constraints, firms need to achieve their expected goals and satisfy all investment constraints under uncertainty. Selecting the set of projects among other candidates to get the most efficient portfolio requires a lot of attention from the Decision Makers (DMs) as this consideration no longer relies purely on the financial term. This problem becomes a multi-objective problem under uncertainty where the financial return and risk from uncertainty are required into the trading off consideration. Due to the financial uncertainty, the chance-constrained programming has been employed in this study for defuzzifying and solving uncertain optimization problems at a specified confidence level that is defined by the DMs. Then, various kinds of investment or financial risk measures, Lower-Semi Variance Index (LSVI), the absolute deviation with the expected FNPV, and the absolute mean-Conditional Value at Risk (CVaR) gap are provided in the selection of such risk measures to show their differences in characteristics and performances in the obtained results. Since, such problems can consist of many project candidates and complex constraints, which may grow beyond the application of the exact optimization approach, a meta-heuristic method, Genetic Algorithm (GA), is introduced to optimize this problem through designing and constructing a decision support tool for the investment portfolio selection and optimization. The applicability of the proposed comparative approach and the constructed tool are illustrated through examples.

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.

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.