Risk Estimation Method Research Articles

Dependent structure and risk analysis of S&P 500 Index's continuously rising returns and continuously falling returns

PurposeThe stock market price time series can be divided into two processes: continuously rising and continuously falling. The authors can effectively prevent the stock market from crashing by accurately estimating the risk on continuously rising returns (CRR) and continuously falling returns (CFR).Design/methodology/approachThe authors add an exogenous variable into Log-autoregressive conditional duration (Log-ACD) model, and then apply our extended Log-ACD model and Archimedean copula to estimate the marginal distribution and conditional distribution of CRR and CFR. Plus, the authors analyze the conditional value at risk (CVaR) and present back-test results of the CVaR. The back-test shows that our proposed risk estimation method has a good estimation power for the risk of the CRR and CFR, especially the downside risk. In addition, the authors detect whether the dependent structure between the CRR and CFR changes using the change point test method.FindingsThe empirical results indicate that there is no change point here, suggesting that the results on the dependent structure and risk analysis mentioned above are stable. Therefore, major financial events will not affect the dependent structure here. This is consistent with the point that the CRR and CFR can be analyzed to obtain the trend of stock returns from a more macro perspective than daily stock returns scholars usually study.Practical implicationsThe risk estimation method of this paper is of great significance in understanding stock market risk and can provide corresponding valuable information for investment advisors and public policy regulators.Originality/valueThe authors defined a new stock returns, CRR and CFR, since it is difficult to analyze and predict the trend of stock returns according to daily stock returns because of the small autocorrelation among daily stock returns.

Design of an algorithm for an adaptive Value at Risk measurement through the implementation of robust methods in relation to asset cross-correlation

This study proposes an algorithmic approach for selecting among different Value at Risk (VaR) estimation methods. The proposed metaheuristic, denominated as “Commitment Machine” (CM), has a strong focus on assets cross-correlation and allows to measure adaptively the VaR, dynamically evaluating which is the most performing method through the minimization of a loss function. The CM algorithm compares five different VaR estimation techniques: the traditional historical simulation method, the filtered historical simulation (FHS) method, the Monte Carlo method with correlated assets, the Monte Carlo method with correlated assets which uses a GARCH model to simulate asset volatility and a Bayesian Vector autoregressive model. The heterogeneity of the compared methodologies and the proposed dynamic selection criteria allow us to be confident in the goodness of the estimated risk measure. The CM approach is able to consider the correlations between portfolio assets and the non-stationarity of the analysed time-series in the different models. The paper describes the techniques adopted by the CM, the logic behind model selection and it provides a market application case of the proposed metaheuristic, by simulating an equally weighted multi-asset portfolio.

Cardiometabolic risks of SARS-CoV-2 hospitalization using Mendelian Randomization

Many cardiometabolic conditions have demonstrated associative evidence with COVID-19 hospitalization risk. However, the observational designs of the studies in which these associations are observed preclude causal inferences of hospitalization risk. Mendelian Randomization (MR) is an alternative risk estimation method more robust to these limitations that allows for causal inferences. We applied four MR methods (MRMix, IMRP, IVW, MREgger) to publicly available GWAS summary statistics from European (COVID-19 GWAS n = 2956) and multi-ethnic populations (COVID-19 GWAS n = 10,908) to better understand extant causal associations between Type II Diabetes (GWAS n = 659,316), BMI (n = 681,275), diastolic and systolic blood pressure, and pulse pressure (n = 757,601 for each) and COVID-19 hospitalization risk across populations. Although no significant causal effect evidence was observed, our data suggested a trend of increasing hospitalization risk for Type II diabetes (IMRP OR, 95% CI 1.67, 0.96–2.92) and pulse pressure (OR, 95% CI 1.27, 0.97–1.66) in the multi-ethnic sample. Type II diabetes and Pulse pressure demonstrates a potential causal association with COVID-19 hospitalization risk, the proper treatment of which may work to reduce the risk of a severe COVID-19 illness requiring hospitalization. However, GWAS of COVID-19 with large sample size is warranted to confirm the causality.

Fetal ECG signal processing and identification of hypoxic pregnancy conditions in-utero.

The fetal heart rate (fHR) variability and fetal electrocardiogram (fECG) are considered the most important sources of information about fetal wellbeing. Non-invasive fetal monitoring and analysis of fECG are paramount for clinical trials. They enable examining the fetal health status and detecting the heart rate changes associated with insufficient oxygenation to cut the likelihood of hypoxic fetal injury. Despite the fact that significant advances have been achieved in electrocardiography and adult ECG signal processing, the analysis of fECG is still in its infancy. Due to accurate fetal morphology extraction techniques have not been properly developed, many areas require particular attention on the way of fully understanding the changes in variability in the fetus and implementation of the non-invasive techniques suitable for remote home care which is increasingly in demand for high-risk pregnancy monitoring. In this paper, we introduce an integrated approach for fECG signal extraction and processing based on various methods for fetal welfare investigation and hypoxia risk estimation. To the best of our knowledge, this is the first attempt to introduce the auto-generated risk scoring in fECG to achieve early warning on fetus' safety and provide the physician with additional information about the possible fetal complications. The proposed method includes the following stages: fECG extraction, fHR and fetal heart rate variability (fHRV) calculation, hypoxia index (HI) evaluation and risk estimation. The extracted signals were examined by assessing Signal to Noise Ratio (SNR) and mean square error (MSE) values. The results obtained demonstrated great potential, but more profound research and validation, as well as a consistent clinical study, are needed before implementation into the hospital and at-home monitoring.

Mathematical Estimation Methods and Models for Industrial Companies

The collateralized debt obligations and credit default swaps applications are shown in this paper. The industry obligations secondary market risk estimation methods are considered in this work. The new methods taking into account statistically significant parameters for industrial credit derivatives portfolio are offered for single-name investment risks numerical experiments realization. The mathematical estimation of tranche were shown. The single and multiple name default obligations necessary mathematical modeling methods and formulae for the industrial materials manufacturers derivative credit tools market are shown. It is determined that the portfolio of synthetic debt tools is made of the given parameters. The task of a loss derivative tranches mathematical estimation is solved. Late defaults raise the equity tranches payment required sums with high spreads, early defaults reduce. Also the functional characteristics required for an estimation huge debts problem solving are partly considered in this paper. The problem of the default modeling for market tools and numerical simulation of the obligations influence on conditions of current bistability mode are shown here. Some credit derivatives of industrial manufacturers are demonstrated in the modeling process of default as an example. It is found that the model is an additional factor help us to estimate the default opportunity.

Integration of pre-surgical blood test results predict microvascular invasion risk in hepatocellular carcinoma

Microvascular invasion (MVI) is one of the most important factors leading to poor prognosis for hepatocellular carcinoma (HCC) patients, and detection of MVI prior to surgical operation could great benefit patient’s prognosis and survival. Since it is still lacking effective non-invasive strategy for MVI detection before surgery, novel MVI determination approaches were in urgent need. In this study, complete blood count, blood test and AFP test results are utilized to perform preoperative prediction of MVI based on a novel interpretable deep learning method to quantify the risk of MVI. The proposed method termed as “Interpretation based Risk Prediction” can estimate the MVI risk precisely and achieve better performance compared with the state-of-art MVI risk estimation methods with concordance indexes of 0.9341 and 0.9052 on the training cohort and the independent validation cohort, respectively. Moreover, further analyses of the model outputs demonstrate that the quantified risk of MVI from our model could serve as an independent preoperative risk factor for both recurrence-free survival and overall survival of HCC patients. Thus, our model showed great potential in quantification of MVI risk and prediction of prognosis for HCC patients.

Estimating Atherosclerosis Cardiovascular Disease Risk of 10-year with the Systemic Coronary Risk Estimation Method and Statin Use Recommendation

Stroke and heart disease belong to atherosclerotic cardiovascular diseases (ASCVD), which is one of the death causes in Indonesia. This study aimed to estimate the risk of atherosclerosis cardiovascular disease in the next 10 years using the Systemic Coronary Risk Estimation (SCORE) method. This research was an observational study with descriptive cross-sectional study design. Respondent data collection was taken prospectively which means directs data was collected through interviews, blood pressure measurement, and peripheral blood sampling. The results showed that respondents who were involved in this study were those with an average age of 49.70 ± 7.1 years. Respondents were more dominated by women (76.1%); BMI of respondents showed in the non-obese group 27.14 ± 4.6 kg/m2; total cholesterol, LDL, TG, and HDL levels respectively had an average of 152.17 ± 39.5 mg/dL; 91.28 ± 40.9 mg/dL; 117.24 ± 96.9 mg/dL; and 30.87 ± 8.8. The number of respondents who smoke was 17.39% and that of those who have undergone physical activity in the form of sports was 60.87%. Based on the results of risk estimation using the SCORE method, it was found that the number of people who were in the low-risk category was 54.35% and the moderate risk category was 45.65%. This study concludes that the risk of ASCVD for the next 10 years in the community is at low and moderate levels. Non-pharmacological treatment is more recommended at this level and statins can be given to individuals if there is no improvement in cholesterol levels.

A new method of temporal and spatial risk estimation for lane change considering conventional recognition defects

Risky lane change behavior of drivers normally will pose some negative impacts on traffic safety. To ensure a lane change safe and prevent potential accidents, it is important to recognize some lane-changing conditions with potential risks in advance. Despite the researches on traffic safety assessment have been developed for decades, most of the existing researches are mainly interesting in how to estimate the overall safety of the lane-changing process based on historical data. These methods tend to ignore the interactive impacts between lane-changing vehicle and its surrounding vehicles, and have the disadvantages of the long-term evaluation period and single evaluation index. To address these gaps, this study presents a temporal and spatial risk estimation (TSRE) to recognize lane-changing risk in real-time. However, this study concentrates on the instantaneous risk for a lane change event, considering temporal and spatial dimensions for the current lane change circumstance on the highway. After processing the realistic vehicle trajectory dataset, this study extracted 1444 groups of lane change samples, and then incorporates the temporal risk level (TRL) and spatial risk level (SRL) into a comprehensive risk index by applying fault tree analysis. Furthermore, SRL and comprehensive risk index are both used to determine whether the traffic condition of a lane change is safe, and it can effectively overcome the conventional recognition defects that existed in other methods. To achieve a better evaluation effect, the sensitivity tests of recognition accuracy for various risk threshold combinations were carried out. Ultimately, experimental results showed that the proposed TSRE model achieved 97.45 %, 97.79 %, 84.1 % and 85.05 % accuracy rate in terms of classifying the risky lane change samples, traffic conflicts, risky frames and safe frames, when an appropriate risk threshold combination was selected. This encouraging finding can provide the basis for algorithm design of the lane change warning system for connected vehicles.

Drought Risk Analysis, Forecasting and Assessment under Climate Change

Climate change is undoubtedly one of the world’s biggest challenges in the 21st century. Drought risk analysis, forecasting and assessment are facing rapid expansion, not only from theoretical but also practical points of view. Accurate monitoring, forecasting and comprehensive assessments are of the utmost importance for reliable drought-related decision-making. The framework of drought risk analysis provides a unified and coherent approach to solving inference and decision-making problems under uncertainty due to climate change, such as hydro-meteorological modeling, drought frequency estimation, hybrid models of forecasting and water resource management. This Special Issue will provide researchers with a summary of the latest drought research developments in order to identify and understand the profound impacts of climate change on drought risks and water resources. The ten peer-reviewed articles collected in this Special Issue present novel drought monitoring and forecasting approaches, unique methods for drought risk estimation and creative frameworks for environmental change assessment. These articles will serve as valuable references for future drought-related disaster mitigations, climate change interconnections and food productivity impacts.

Modelling and Simulation for Preclinical Cardiac Safety Assessment of Drugs with Human iPSC-Derived Cardiomyocytes

As a potentially life threatening side effect, pharmaceutical compounds may trigger cardiac arrhythmias by impeding the heart’s electrical and mechanical function. For this reason, any new compound needs to be tested since 2005 for its proarrhythmic risk both during the preclinical and the clinical phase of the drug development process. While intensive monitoring of cardiac activity during clinical tests with human volunteers constitutes a major cost factor, preclinical in vitro tests with non cardiac cells and in vivo tests with animals are currently under serious debate because of their poor extrapolation to drug cardiotoxicity in humans. For about five years now, regulatory agencies, industry and academia are working on an overhaul of the cardiac drug safety paradigm that is built a) on human heart muscle cells, that can be abundantly bioengineered from donor stem cells without ethical concerns (human induced pluripotent stem cell derived cardiomyocytes, hiPSC-CMs), and b) on computational models of human cardiac electrophysiology both at the cellular and the organ level. The combined use of such human in vitro and human in silico models during the preclinical phase is expected to improve proarrhythmia test specificity (i.e. to lower the false-positive rate), to better inform about the need of thorough heart monitoring in the clinic, and to reduce or even replace animal experiments. This review article starts by concisely informing about the electrical activity of the human heart, about its possible impairment due to drug side effects, and about hiPSC-CM assays for cardiac drug safety testing. It then summarizes the mathematical description of human cardiac electrophysiology in terms of mechanistic ODE and PDE models, and illustrates how their numerical analysis may provide insight into the genesis of drug induced arrhythmias. Finally, this paper surveys proarrhythmic risk estimation methods, that involve the simulation of human heart muscle cells, and addresses opportunities and challenges for future interdisciplinary research.

A Scalable Estimate of the Out-of-Sample Prediction Error via Approximate Leave-One-Out Cross-Validation

SummaryThe paper considers the problem of out-of-sample risk estimation under the high dimensional settings where standard techniques such as K-fold cross-validation suffer from large biases. Motivated by the low bias of the leave-one-out cross-validation method, we propose a computationally efficient closed form approximate leave-one-out formula ALO for a large class of regularized estimators. Given the regularized estimate, calculating ALO requires a minor computational overhead. With minor assumptions about the data-generating process, we obtain a finite sample upper bound for the difference between leave-one-out cross-validation and approximate leave-one-out cross-validation, |LO−ALO|. Our theoretical analysis illustrates that |LO−ALO|→0 with overwhelming probability, when n, p → ∞, where the dimension p of the feature vectors may be comparable with or even greater than the number of observations, n. Despite the high dimensionality of the problem, our theoretical results do not require any sparsity assumption on the vector of regression coefficients. Our extensive numerical experiments show that |LO−ALO| decreases as n and p increase, revealing the excellent finite sample performance of approximate leave-one-out cross-validation. We further illustrate the usefulness of our proposed out-of-sample risk estimation method by an example of real recordings from spatially sensitive neurons (grid cells) in the medial entorhinal cortex of a rat.

2019 ASCCP Risk-Based Management Consensus Guidelines: Methods for Risk Estimation, Recommended Management, and Validation.

To manage cervical screening abnormalities, the 2019 ASCCP management consensus guidelines will recommend clinical action on the basis of risk of cervical precancer and cancer. This article details the methods used to estimate risk, to determine the risk-based management, and to validate that the risk-based recommendations are of general use in different settings. Based on 1.5 million patients undergoing triennial cervical screening by cotesting at the Kaiser Permanente Northern California from 2003 to 2017, we estimated risk profiles for different clinical scenarios and combinations of past and current human papillomavirus and cytology test results. We validated the recommended management by comparing with the estimated risks in several external data sources. Risk and management tables are presented separately by Egemen et al. and Demarco et al. Risk-based management derived from the Kaiser Permanente Northern California largely agreed with the management implied from the estimated risks of the other data sources. The new risk-based guidelines present management of abnormal cervical screening results. By describing the steps used to develop these guidelines, the methods presented in this article can provide a basis for future extensions of the risk-based guidelines.

Risk management and employment of advanced methods of risk estimation

The paper studies the methods applied in risk management and estimation. The brainstorming techniques, Delphi, the method of event tree analysis (ETA) and fault tree analysis (FTA), failure modes and effect analysis (FMEA), hazard and operability study are wide used. The pros and cons of risk management methods applied in world practice by leading companies were analysed. As a technique based on the study of logical connection between the hazardous events, “Tree event” method has its advantages. At the same time, the failure modes and effect analysis differs with simplicity and accessibility. It was defined that this technique is used as one of the most important tools in risk management almost in all spheres including petrochemistry and power economy. It is recommended to use HAZOP method in the analysis of project solutions and for risk estimation in later design stages. It is pointed out that in the context of improvement of risk management system, more qualitative risk estimation, the employment of advanced methods plays a significant role for goal achievement in the company and project decision-making as well.

Probabilistic human health risk assessment of perfluorooctane sulfonate (PFOS) by integrating in vitro, in vivo toxicity, and human epidemiological studies using a Bayesian-based dose-response assessment coupled with physiologically based pharmacokinetic (PBPK) modeling approach

BackgroundEnvironmental exposure to perfluorooctane sulfonate (PFOS) is associated with various adverse outcomes in humans. However, risk assessment for PFOS with the traditional risk estimation method is faced with multiple challenges because there are high variabilities and uncertainties in its toxicokinetics and toxicity between species and among different types of studies. ObjectivesThis study aimed to develop a robust probabilistic risk assessment framework accounting for interspecies and inter-experiment variabilities and uncertainties to derive the human equivalent dose (HED) and reference dose for PFOS. MethodsA Bayesian dose-response model was developed to analyze selected 34 critical studies, including human epidemiological, animal in vivo, and ToxCast in vitro toxicity datasets. The dose-response results were incorporated into a multi-species physiologically based pharmacokinetic (PBPK) model to reduce the toxicokinetic/toxicodynamic variabilities. In addition, a population-based probabilistic risk assessment of PFOS was performed for Asian, Australian, European, and North American populations, respectively, based on reported environmental exposure levels. ResultsThe 5th percentile of HEDs derived from selected studies was estimated to be 21.5 (95% CI: 10.6–36.3) ng/kg/day. After exposure to environmental levels of PFOS, around 50% of the population in all studied populations would likely have >20% of increase in serum cholesterol, but the effects on other endpoints were estimated to be minimal (<10% changes). There was a small population (~10% of the population) that was highly sensitive to endocrine disruption and cellular response by environmental PFOS exposure. ConclusionOur results provide insights into a complete risk characterization of PFOS and may help regulatory agencies in the reevaluation of PFOS risk. Our new probabilistic approach can conduct dose-response analysis of different types of toxicity studies simultaneously and this method could be used to improve risk assessment for other perfluoroalkyl substances (PFAS).

Risk estimation and decision making in management (in selected areas of science)

Risk is a category that is inseparably connected with uncertainty and probability, which means that the nature of risk as a category of science is complex, and the concept of risk is very difficult to define by one conceptual system of modern science. Due to the above, the main research hypothesis of the work is oriented to the assumption that the complexity of the risk category is determined by the diversity (variety) of reality, as a result of which in science there is currently no uniform methodology for risk assessment and estimation. As a result, the main goal of the article is to describe the research area based on selected representative methods of risk estimation and logical decision-making schemes, as well as to systematise the knowledge about the methodology used in them. In the article, the authors illustrate risk estimation with examples developed by themselves and quoted from various fields of science, differing from one another in formal terms in quantitative and qualitative (numerical and content-verbally) dimensions. Strategic risk, risk of fraction estimation, Bayesian risk, Bayesian methods for estimation of population distribution parameters, risk of econometric model assessment, interest rate risk, banking risk, and adverse event as a measure of risk are here addressed. The article also focuses on the problem of risk estimation in terms of the theory of fractals. The work is to have not only cognitive but also practical meaning. The created source of knowledge should prove helpful for decision-makers in the area of management since effective process management requires the expertise of risk estimation in various dimensions and using various mathematical tools.

Peri-operative cardiac biomarker screening: a narrative review.

Peri-operative risk estimation has traditionally focused on assessing the likelihood of postoperative morbidity and mortality using pre-operative functional assessment. Although this strategy is currently recommended by most major society guidelines, contemporary evidence suggests that cardiac biomarker measurement has important advantages over pre-operative functional assessment. These advantages include superior predictive discrimination and inclusion of the postoperative course in risk estimation. In this review, we provide an overview of the evidence supporting the peri-operative utilisation, compare risk estimation methods and discuss which patients may benefit most from cardiac biomarker screening. We also discuss protocols for biomarker screening and management of patients with abnormal results.

Method for flood risk estimation in a tropical basin

Abstract This study proposes a method for estimating the flood risk in a tropical basin. The novelty consists of using two dynamic variables represented by the precipitation and the land use and land cover (LULC) to obtain the effective rainfall and its exceedance probability. The flood risk is determined by using deterministic methods depending on the exceedance probability of effective rainfall. Two time series of precipitation were involved. The first precipitation time series was used to prove the method corresponding to the months of the rainy season obtained from 58 gauging stations during the period 1980–2000. The second precipitation time series was applied for the validation stage of method corresponding to the period 2015–2018. In the validation stage, it has been found that there is a slight difference between the principal component N° 1 in the first time series with respect to the second time series. The method represents a contribution to determine the spatio-temporal distribution of flood risk that allows decisions to be taken to achieve preventive measures that contribute to the protection of human beings and goods.

Risk of undesired changes to significant information quality criteria

The article presents a method of estimating the risk of an undesirable change in the information quality criterion of secrecy, meaning estimating the risk of a certain class of information security incidents. The qualitative risk estimation method is adopted and the impact of a descriptive grade composition method on the results is discussed. The paper also shows considerations on the possibilities of interpreting the variables used in risk estimation and establishing the range of their actual values. It also describes how the identified range of actual variable values translates into grades used in risk estimation.

Developing Mechanism of Ensuring Enterprise Economic Security at Time of Introducing Digital Technologies

Introduction of digital technologies causes transformation of all mechanisms of enterprise management. On the basis of processing and systematization of theoretical and practical and applied works by Russian and overseas researchers and reports of official statistics the authors study specific features of the process of ensuring economic security (ES) in conditions of digitalization, analyze and systematize the lines in its development and show ways of further upgrading. The lines in developing mechanisms of ES ensuring lie in the field of available tools, methods of risk estimation and threat identification, indicators and coefficients of the economic security level, supporting subsystems, including personnel, legal, informational and managerial systems. Upgrading the system of risk estimation can be carried out in view of criteria of their identification. Within the frames of the research the authors showed specific risks of economic security arising in the process of digitalization of company management: inadequacy of digital infrastructure, insufficiency of digital tools, undeveloped digital competences. The principle internal risks of economic security are misunderstanding of digital transformation concept at the top management level and the gap in digital development of employees. The authors think that improvement of mechanisms of the enterprise ES is connected with raising the level of technical, informational and program support, the level of employees’ competences.

Methods of Estimating Operative Risks in Trade

The article deals with Russian retail trade. Today it is very topical due to our economy digitalization and sanctions introduced by some foreign countries. The authors study methodological approaches to the analysis of operative risks in trade and search for risk estimation models in operations of order shaping for the product range, payment, acquisition, transportation, damage, storage and product sale. The conceptual model of researching operative risks in trade was put forward and methodological substantiation of summarized methods and models was given. They are useful for direct application in estimating operative risks in practice, such as those connected with foreign currency, inflation, interest rate, transportation, damage and loss of products and missed profit. The authors give practical examples of using the groups of formal methods of risk estimation in wholesale operations dealing with purchase and sale of foot ware by big retailers. Practical importance of the research results is connected with the opportunity to use these models for operative risk estimation, which could cut losses and minimize risks, avoid unfavourable economic after-effects in the work of Russian retailers.