scholarly journals A Framework for Analysis and Prediction of Operational Risk Stress

2021 ◽  
Vol 26 (1) ◽  
pp. 19
Author(s):  
Peter Mitic
Keyword(s):  
Risk Factors ◽  
Financial Risk ◽  
Time Window ◽  
Operational Risk ◽  
Specific Reference ◽  
Mild Stress ◽  
Regulatory Capital ◽  
Regulatory Requirements ◽  
Extreme Stress ◽  
Loss Data

A model for financial stress testing and stability analysis is presented. Given operational risk loss data within a time window, short-term projections are made using Loess fits to sequences of lognormal parameters. The projections can be scaled by a sequence of risk factors, derived from economic data in response to international regulatory requirements. Historic and projected loss data are combined using a lengthy nonlinear algorithm to calculate a capital reserve for the upcoming year. The model is embedded in a general framework, in which arrays of risk factors can be swapped in and out to assess their effect on the projected losses. Risk factor scaling is varied to assess the resilience and stability of financial institutions to economic shock. Symbolic analysis of projected losses shows that they are well-conditioned with respect to risk factors. Specific reference is made to the effect of the 2020 COVID-19 pandemic. For a 1-year projection, the framework indicates a requirement for an increase in regulatory capital of approximately 3% for mild stress, 8% for moderate stress, and 32% for extreme stress. The proposed framework is significant because it is the first formal methodology to link financial risk with economic factors in an objective way without recourse to correlations.

scholarly journals Numerical estimates of risk factors contingent on credit ratings

2021 ◽  
Author(s):  
T. Gärtner ◽  
S. Kaniovski ◽  
Y. Kaniovski
Keyword(s):  
Risk Factors ◽  
Financial Risk ◽  
Credit Rating ◽  
Likelihood Function ◽  
Adverse Outcome ◽  
Credit Ratings ◽  
Industry Sector ◽  
Random Events ◽  
Historical Transition ◽  
Industry Sectors

AbstractAssuming a favorable or an adverse outcome for every combination of a credit class and an industry sector, a binary string, termed as a macroeconomic scenario, is considered. Given historical transition counts and a model for dependence among credit-rating migrations, a probability is assigned to each of the scenarios by maximizing a likelihood function. Applications of this distribution in financial risk analysis are suggested. Two classifications are considered: 7 non-default credit classes with 6 industry sectors and 2 non-default credit classes with 12 industry sectors. We propose a heuristic algorithm for solving the corresponding maximization problems of combinatorial complexity. Probabilities and correlations characterizing riskiness of random events involving several industry sectors and credit classes are reported.

scholarly journals A Family of Loss Distributions with an Application to the Vehicle Insurance Loss Data

2019 ◽  
pp. 731-744 ◽  
Author(s):  
Zubair Ahmad Ahmad ◽  
Eisa Mahmoudi Mahmoudi ◽  
G. G. Hamedani
Keyword(s):  
Maximum Likelihood ◽  
Financial Risk ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Data Set ◽  
Loss Model ◽  
New Class ◽  
Proposed Model ◽  
Loss Distributions ◽  
Loss Data

Actuaries are often in search of nding an adequate loss model in the scenario of actuarial and financial risk management problems. In this work, we propose a new approach to obtain a new class of loss distributions. A special sub-model of the proposed family, called the Weibull-loss model isconsidered in detail. Some mathematical properties are derived and maximum likelihood estimates of the model parameters are obtained. Certain characterizations of the proposed family are also provided. A simulation study is done to evaluate the performance of the maximum likelihood estimators. Finally, an application of the proposed model to the vehicle insurance loss data set is presented.

scholarly journals Impact of age, body weight and metabolic risk factors on steroid reference intervals in men

2020 ◽  
Vol 182 (5) ◽  
pp. 459-471
Author(s):  
Marco Mezzullo ◽  
Guido Di Dalmazi ◽  
Alessia Fazzini ◽  
Margherita Baccini ◽  
Andrea Repaci ◽  
...  
Keyword(s):  
Risk Factors ◽  
Reference Intervals ◽  
Metabolic Risk Factors ◽  
Specific Reference ◽  
Metabolic Risk ◽  
Cross Sectional ◽  
Cholesterol Ratio ◽  
Age Related ◽  
Reference Limits ◽  
17 Hydroxyprogesterone

Objective To evaluate the independent impact of age, obesity and metabolic risk factors on 13 circulating steroid levels; to generate reference intervals for adult men. Design Cross-sectional study. Methods Three hundred and fifteen adults, drug-free and apparently healthy men underwent clinical and biochemical evaluation. Thirteen steroids were measured by LC-MS/MS and compared among men with increasing BMI. Moreover, the independent impact of age, BMI and metabolic parameters on steroid levels was estimated. Upper and lower reference limits were generated in steroid-specific reference sub-cohorts and compared with dysmetabolic sub-cohorts. Results We observed lower steroid precursors and testosterone and increase in estrone levels in men with higher BMI ranges. By multivariate analysis, 17-hydroxyprogesterone and dihydrotestosterone decreased with BMI, while cortisol decreased with waist circumference. Estrone increased with BMI and systolic blood pressure. Testosterone decreased with worsening insulin resistance. 17-hydroxypregnenolone and corticosterone decreased with increasing total/HDL-cholesterol ratio. Age-related reference intervals were estimated for 17-hydroxypregnenolone, DHEA, 17-hydroxyprogesterone, corticosterone, 11-deoxycortisol, cortisol and androstenedione, while age-independent reference intervals were estimated for progesterone, 11-deoxycorticosterone, testosterone, dihydrotestosterone, estrone and estradiol. Testosterone lower limit was 2.29 nmol/L lower (P = 0.007) in insulin resistant vs insulin sensitive men. Furthermore, the upper limits for dihydrotestosterone (−0.34 nmol/L, P = 0.045), cortisol (−87 nmol/L, P = 0.045–0.002) and corticosterone (−10.1 nmol/L, P = 0.048–0.016) were lower in overweight/obese, in abdominal obese and in dyslipidaemic subjects compared to reference sub-cohorts, respectively. Conclusions Obesity and mild unmedicated metabolic risk factors alter the circulating steroid profile and bias the estimation of reference limits for testosterone, dihydrotestosterone, cortisol and corticosterone. Applying age-dependent reference intervals is mandatory for steroid precursors and corticosteroids.

scholarly journals Geological- and Chemical-Based Environmental Risk Factor Sustainability Model

2020 ◽  
Vol 9 (4) ◽  
pp. 303
Author(s):  
Daniel T. Rogers
Keyword(s):  
Risk Factors ◽  
Environmental Sustainability ◽  
Anthropogenic Factors ◽  
Regulatory Requirements ◽  
Leadership And Management ◽  
Industrial Manufacturing ◽  
Innovative Leadership ◽  
Sustainability Model ◽  
Natural And Anthropogenic Factors ◽  
Operational Aspects

An environmental sustainability model that integrates natural and anthropogenic factors was developed and tested for 10 years. The model is composed of calculated geological risk factors, chemical risk factors, and operational aspects of environmental regulatory requirements which are integrated into a comprehensive environmental sustainability model. The model was tested at 67 operating industrial manufacturing facilities in 12 countries over a period of 10 years. The results achieved included measured reductions of environmental impacts to air, land, and water from 5% to more than 95% of operational aspects compared to pre-model values. A significant catalyst for model success was identifying and applying innovative leadership and management principles that were required to modify business objectives and culture from purely capitalist incentives and objectives to sustainability-oriented goals. This was accomplished through a clear and understandable model, stated objectives, incentives, rewards and penalties, measuring results, data analysis, identifying and communicating areas where improvement was needed, model adaptations, transparent communication and feedback, and flexible timelines. The results indicate that the model can be scaled from the parcel to global level, assuming management and leadership principles are in place and properly supported.  Keywords: Sustainability model, contaminant risk, geologic vulnerability

scholarly journals Time-Varying Associations Between an Exposure History and a Subsequent Health Outcome: A landmark Approach to Identify Critical Windows.

2021 ◽  
Author(s):  
Maude Wagner ◽  
Francine Grodstein ◽  
Karen Leffondre ◽  
Cécilia Samieri ◽  
Cécile Proust-Lima
Keyword(s):  
Risk Factors ◽  
Health Outcome ◽  
Measurement Errors ◽  
Mixed Model ◽  
Time Window ◽  
Time Windows ◽  
Critical Time ◽  
Cumulative Exposure ◽  
Exposure Measurement ◽  
Exposure History

Abstract Background: Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology.Methods: We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model.Results: A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses’ Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease).Conclusions: This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

Impact of Ownership and Size on Operational Risk Management Practices: A Study of Banks in India

2017 ◽  
Vol 18 (3) ◽  
pp. 795-810 ◽  
Author(s):  
Deepak Tandon ◽  
Yogieta S. Mehra
Keyword(s):  
Risk Management ◽  
Factor Analysis ◽  
Data Collection ◽  
Management Practices ◽  
Operational Risk ◽  
Test Reliability ◽  
Operational Risk Management ◽  
Data Collection And Analysis ◽  
Loss Data ◽  
The Impact

The financial crisis and resulting failure of large banks worldwide has shaken the entire world. Improper management of operational risk has been touted as one of the reasons for this failure. In light of the rising importance of operational risk management (ORM) in banks, the study explores the range of ORM practices followed by a cross section of Indian banks and compares them with the banks worldwide. The study also analyses the impact of size and ownership of banks on these practices. Reliability analysis using Cronbach alpha model, Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity was used to test reliability of questionnaire and justifies the use of factor analysis. Factor analysis was performed to extract the most important variables in ORM. The small size of bank was observed to be a deterrent to deep involvement of operational risk functionaries, collection and usage of external loss data and data collection and analysis. Further, the performance/preparedness of public sector and old private sector banks lagged behind peers in usage of key reporting components, such as risk and control self-assessment (RCSA), key risk indicators (KRI), scenarios, collection and usage of external loss data, data collection and analysis and quantification and modelling of operational risk.

Store brand and perceived risk on private label brand attitude

2020 ◽  
Vol 12 (1) ◽  
pp. 133-150 ◽  
Author(s):  
M.K.M. Manikandan
Keyword(s):  
Risk Factors ◽  
Perceived Risk ◽  
Tamil Nadu ◽  
Financial Risk ◽  
Perceived Quality ◽  
Negative Influence ◽  
Private Label ◽  
Social Risk ◽  
Content Type ◽  
The Social

Purpose The purpose of this paper is to find the influence of retailer equity and perceived risk on attitudes toward private label brand (PLB) grocery products. Design/methodology/approach Retailer equity includes four variables: retailer awareness, retailer association, retailer perceived quality and retailer loyalty. The perceived risk factors include functional risk, financial risk and social risk. The attitude toward PLBs was taken as the dependent variable. The study was carried out by using a standardized questionnaire for all three constructs. The convenience sampling method was adopted to carry out data collection from customers of organized retail stores in the city of Coimbatore, in the state of Tamil Nadu, India. The relationship between the three variables was studied with structural equation modeling using IBM SPSS Amos software. Findings The study revealed that excluding the Financial Risk and the Social Risk, functional risk alone has significant influence over the PLB Attitude. The Retailer Equity variables, retailer perceived quality and retailer loyalty have positive influence on the PLB Attitude, while the other two variables do not show any influence. Retailer Awareness shows a negative influence over the social risk. Retailer Association does not show any influence on any of the three risk factors. Retailer perceived quality shows negative influence over the functional risk while retailer loyalty negatively influences social risk. Research limitations/implications The research study was carried out in cities that are populous in the Indian state of Tamil Nadu. All the respondents came from three cities in Tamil Nadu, namely, Coimbatore, Tiruppur and Madurai. Hence, extending the findings of the study to other countries where organized retail penetration is deeper may be attempted with caution. Practical implications The study will offer managers in the retail industry some understanding of the risk-relieving factors in operation when buying grocery goods. Originality/value The research paper contributes to the literature concerning the role played by retailer equity and perceived risk factors on attitudes toward PLBs.

Family History, Stress, Less Physical Activity, Obesity and Excessive Salty Food Consumption as Risk Factors of Hypertension

2016 ◽  
pp. 186
Author(s):  
I.M. Jaya Widyartha ◽  
W. G. Artawan Eka Putra ◽  
Luh Seri Ani
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Family History ◽  
Food Consumption ◽  
Coffee Consumption ◽  
Public Health Problem ◽  
Abdominal Circumference ◽  
Mild Stress ◽  
Severe Stress ◽  
Salty Food

Background and purpose: Hypertension was a significant public health problem. This study aims to determine risk factors of hypertension.Methods: The study used a case-control design, involving 77 cases and 77 controls aged 18-65 years old, who were selected consecutively. Cases and controls were matched on age and sex. Cases were defined as patients who were diagnosed with hypertension by clinician at the primary health center (PHC), and controls were patients at PHC who were not diagnosed as hypertension. Data were collected through interview regarding sosiodemographic status and risk factors of hipertension. Direct measurement was performed for weight, height and abdominal circumference. Multivariate analysis was conducted using logistic regression.Results: Cases and controls were comparable in term of sex, age and education level. Variables that associated with hypertension were family history (AOR=9.20; 95%CI: 3.47-24.41), moderate stress (AOR=13.01; 95%CI: 3.70-45.79), severe stress (AOR=16,75; 95%CI: 3,32-84,38), less physical activity (AOR=3.53 (95%CI: 1.38-9.01), obesity (AOR=5.72; 95%CI: 2.09-15.68) dan excessive salty food consumption (AOR=3.08; 95%CI: 1.17-8.09). Eating fatty foods may also indirectly cause hypertension. Income, mild stress, smoking habits, being passive smokers, coffee consumption habits, frequency of fruits and vegetables consumption were not found to be risk factors.Conclusion: Family history, moderate and severe stress, less physical activity, obesity and excessive salty food consumption were risk factors of hypertension.

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