Risk Measures Research Articles

Worst-case distortion riskmetrics and weighted entropy with partial information

In this paper, we discuss the worst-case distortion riskmetrics for general distributions when only partial information (mean and variance) is known. This result is applicable to a general class of distortion risk measures and variability measures. Furthermore, we also consider the worst-case weighted entropy for general distributions when only partial information is available. Specifically, we provide some applications for entropies, weighted entropies and risk measures. The commonly used entropies include Gini functional, cumulative residual entropy, tail-Gini functional, cumulative Tsallis past entropy, extended Gini coefficient, among others. The risk measures contain some premium principles and shortfalls based on entropy. The shortfalls include the Gini shortfall, extended Gini shortfall, shortfall of cumulative residual entropy and shortfall of cumulative residual Tsallis entropy with order α.

Is firm-level political risk priced in the corporate bond market?

We investigate whether political risk is priced in the cross-section of corporate bond returns by using a text-based measure of firm-level political risk. We document a positive and significant political risk premium after controlling for bond and firm characteristics, conventional risk factors, and exposure to aggregate economic policy uncertainty. Bonds with higher political and credit risk, as well as smaller, more illiquid, and longer maturity corporate bonds exhibit a larger political risk premium. Time-series analysis indicates that monetary policy shocks and common shocks in the equity and bond market exhibit a statistically significant and positive association with the political risk premium. Our findings reveal the importance of idiosyncratic political risk beyond common risk factors and aggregate economic policy uncertainty.

Clinical Frailty as a Proxy Measure for Infection Risk in ANCA-Associated Glomerulonephritis

Clinical Frailty as a Proxy Measure for Infection Risk in ANCA-Associated Glomerulonephritis

Envisioning a decision support system for the planning of alternative fuel bunkering operations in Singapore

Abstract In the coming decades, the global maritime industry will face a most formidable technological and societal challenge set by climate change – achieving net zero carbon emissions by 2050. To meet this target, ships are shifting towards the use of low-carbon alternative fuels such as ammonia and methanol. While this paradigm shift to the bunkering of greener fuels is encouraging, it also introduces a new and unique type of environmental risk in the unfortunate scenario of a spill. In the past decades numerous environmental studies have been conducted and mitigation techniques for petroleum oil spills have been developed. However, ammonia and methanol spills are comparatively less understood globally and in the region. There is a significant knowledge gap regarding how the complex atmosphere, hydrodynamic and environmental conditions of the Singapore Straits may influence the dispersion and assimilation of hypothetical ammonia or methanol plumes. Together with other emerging alternative fuels such as electricity and hydrogen, ammonia and methanol have different physical and chemical properties in ambient water compared to conventional LNG or petroleum oil which makes modelling the risk they pose to the environment more challenging. In view of these challenges, the key objective of our research is to develop an operationally running decision support system (DSS) that combines physics-based atmospheric and coastal hydrodynamic models that can forecast the marine and atmospheric plume dispersion of alternative fuels. This forecasting capability will be supplemented with data-driven and rule-based optimisation algorithms that can be used to determine the optimal mitigation scenarios which minimises the potential environmental impacts. Overall, the DSS will integrate observation data, models, mitigation scenarios, and decision-making tools pertaining to efficient and safe bunkering operations within Singapore’s ports. This is essential as the use of alternative fuels in maritime industry becomes more common in Singapore and around the world. Such a system will aid relevant authorities to better calculate risk and plan mitigation measures at designated bunkering locations and periods having optimal atmospheric, hydraulics, and environmental conditions. In this paper, we describe the potential consequences of alternative fuel spills, mitigation measures and a plan for assessing their environmental impact within the context of Singapore. We will review the limited current efforts to model ammonia spills in the literature and propose for a more integrated modelling approach through the DSS.

Model risk management in stress testing: The road up to here

This paper reviews the historical evolution of the quantitative models, model usage and model risk management (MRM) in large US banks since the 1980s. It comments on the most significant model-related events through this period. Some of these events were associated with contexts of great stress to the financial system. The paper identifies the main features of modelling in finance and economics that distinguish it from its application in the natural sciences. Then it presents the different types of models used in the large banks' stress testing programmes, the specific characteristics and risks of each type of model and the best practices for the implementation of sound model risk management, and it suggests a proper way to interpret the results of stress testing and capital assessment in the presence of model risks. The paper discusses the future evolution of MRM towards the implementation of a `fully fledged risk management framework` along the lines of other risk management disciplines, including risk identification, measurement, monitoring, reporting, limiting and capitalisation. It acknowledges the enhancements and opportunities offered by advances in the use of artificial intelligence and machine learning as well as increases in computational power. The paper concludes by noting that the model risk management framework that has been enforced by the Federal Reserve's stress testing and capital planning programmes has substantially strengthened the capital position, profitability and resilience of the large banks in the US.

The Impact Of Stock Price On Investment Decision: Theory & Evidence

Purpose – The purpose of this paper is to present a behavioral explanation of excess stock price volatility relative to present value theory. Design/methodology/approach – The conceptual basis is the impact of effect on investor decisions. The empirical tests involve survey data collected from a sample of semi-professional Investors and investment advisors. Findings – It is suggested that affect causes investors to perceive an inverse ex ante relationship Between risk perceptions and expected returns. Thus, new good or bad information has an amplified effect on stock valuations. In addition, investors tend to extrapolate recent short-term market movements into the future. Practical implications – The primary implications are that ex ante perceptions of risk and return Vary inversely and that affect has a strong influence on valuation. This means that simple statistical Measures of risk are unlikely to fully capture risk perceptions and that market volatility can be Expected to be greater than a simple present value model would imply. Originality/value – This paper is unique as to the conclusion that risk and return perceptions vary Inversely ex ante and that affect can amplify stock price volatility

Evaluating The Role Of International Financial Institutions In Maintaining Financial Stability

This paper examines the functions of IFIs specifically the IMF, World Bank, and BIS in relation to balance of payments in the international financial system. Employing a cross-sectional research methodology, the study analyzes IFI interventions during the Asian Financial Crisis (1997), the Global Financial Crisis (2008), and the COVID-19 pandemic. It evaluates data related to GDP growth, capital flows, and systemic risk measures, and finds that IFIs have a highly positive impact on economic recovery and financial system stability. The study also highlights the importance of advanced technologies, such as big data, machine learning, and artificial intelligence, in improving IFI capacities for evaluating and forecasting financial stability. However, it reveals issues with data quality, methodological limitations, and policy-data gaps, particularly from developing countries where IFIs are crucial. Recommendations are provided for enhancing data acquisition, openness, and the use of big data analytics, along with directions for further research in prognostic modeling and crisis anticipation.

PDOSPERT: A New Scale to Predict Domain‐Specific Risk‐Taking Behaviors in Times of a Pandemic

ABSTRACTUnderstanding risk tolerance is crucial for predicting and changing behavior across various domains, including health and safety, finance, and ethics. This remains true during a crisis, such as the COVID‐19 pandemic, and leads to a key question: Do current risk measures reliably predict risk‐taking in the drastically different context of a pandemic? The Domain Specific Risk‐Taking (DOSPERT) scale, one of the most widely used risk‐taking measures, assesses self‐reported risk‐taking in response to 30 risky situations across five domains. With the hypothetical risks of the DOSPERT being based on prepandemic circumstances, we estimate that three out of four of its risk‐taking situations were not possible due to preventive measures or did not reflect risk‐taking in times of COVID‐19. In addition, COVID‐19 brought forth new behaviors deemed risky. With an aim to better predict risk‐taking in times of a pandemic, we introduce the Pandemic DOSPERT (PDOSPERT). We summarize three preregistered online studies with 1254 UK participants to validate the scale against the original DOSPERT and three other common risk‐taking measures. We also test its ability to predict pandemic risk‐related behaviors at three points in time over 2 years. Overall, we find that the PDOSPERT scale significantly improves predictions for pandemic‐related risk behavior as compared to the original DOSPERT. In particular, the health/safety subscale is significantly and strongly associated with pandemic‐related risk behavior. We not only validate a pandemic‐specific risk task but also introduce a template for developing context‐ and domain‐sensitive measures for risk‐taking in the future.

Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospective cohort study design

ObjectiveDespite extensive exploration of potential biomarkers of cardiovascular diseases (CVDs) derived from retinal images, it remains unclear how retinal images contribute to CVD risk profiling and how the results can...

A wee exploration of techniques for risk assessments of extreme events

AbstractAssessing the behaviour of extreme events in univariate and multivariate settings entails many challenges, from the need to capture different sources of non-stationarity to adequately extrapolate into the tail of the distribution and compute probabilities of extreme events associated with high-dimensional vectors. Motivated by these common issues, we use a combination of methods from extreme-value theory, dimensionality reduction, non-parametric statistics, copula theory, and bootstrap model averaging to provide estimates of risk measures associated with environmental extremes. The work is tailored to the four data challenges presented in the EVA (2023) Conference Data Challenge competition, and the methods introduced here represent the approach taken by the Wee Extremes group.

S844 Development and Validation of Novel Ergonomic Risk Measure (Colonoscopy Staff Strain Index) for Endoscopy Staff Supporting Colonoscopy

S844 Development and Validation of Novel Ergonomic Risk Measure (Colonoscopy Staff Strain Index) for Endoscopy Staff Supporting Colonoscopy

Bivariate Tail Conditional Co-Expectation for elliptical distributions

In this paper, we consider a random vector X=(X1,X2) following a multivariate Elliptical distribution and we provide an explicit formula for E(X|X≤X˜), i.e., the expected value of the bivariate random variable X conditioned to the event X≤X˜, with X˜∈R2. Such a conditional expectation has an intuitive interpretation in the context of risk measures. Specifically, E(X|X≤X˜) can be interpreted as the Tail Conditional Co-Expectation of X (TCoES). Our main result analytically proves that for a large number of Elliptical distributions, the TCoES can be written as a function of the probability density function of the Skew Elliptical distributions introduced in the literature by the pioneering work of Azzalini (1985). Some numerical experiments based on empirical data show the usefulness of the obtained results for real-world applications.

Extended weighted generalized cumulative residual entropy for risk measurement

In this paper, we propose an extended variant of Weighted Generalized Cumulative Residual Entropy (WGCRE), its dynamic version, and a Weighted Generalized Cumulative Kerridge Inaccuracy measure by considering a general continuous non negative weight function. Several properties, including bounds and stochastic ordering, of these measures are obtained under appropriate assumptions. Furthermore, we introduce the empirical extended WGCRE as a non-parametric estimator, demonstrating its almost sure convergence to the proposed measure. A central limit theorem is established, examples are provided for illustrative purposes, and the stability of the empirical measure is examined. Finally, we demonstrate the practicality of this measure using real financial data for risk measurement.

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.