Latent Risk Factors Research Articles

Analysis of the Risk Factors Affecting the Size of Fatal Accidents Involving Trucks Based on the Structural Equation Model

For decreasing the casualties and property losses in truck-involved fatal accidents, efforts to identify risk factors and their impacts on the fatal accidents are urgently needed. First, for scaling damage extent in fatal accident, truck occupant injury factor and accident size were selected from occupant- and crash- levels respectively. Then, a general structural model was built to explore the relationships between environmental, roadway, vehicle, driver factors and fatal accident severity. Afterwards, by analyzing 2010 data from the Trucks Involved in Fatal Accidents (TIFA) database, a weighted least squares (WLS) based structural equation model (SEM) was adopted to estimate the parameters of the general structural model. Significant differences between various risk factors determining truck occupant injury factors and accident severity were identified. The results indicate that environmental, roadway, vehicle, and driver factors all have statistically significant effects on the accident size and truck occupant injury factors. However, the extent to which these risk factors impact these two latent endogenous variables differ significantly. Measurement models including different latent risk factors and the corresponding observed variables are then analyzed. The results show that main indicators affecting accident severity are: environmental and roadway factors such as weather, surface conditions; vehicle factors such as truck weight and body type; and driver factors such as age, driving experience, and history of crashes or convictions. Finally, several countermeasures are suggested for truck manufactures and safety planners with the goal of reducing the number and severity of fatal accidents involving trucks.

High-dimensional multivariate realized volatility estimation

We provide a new factor-based estimator of the realized covolatility matrix, applicable in situations when the number of assets is large and the high-frequency data are contaminated with microstructure noises. Our estimator relies on the assumption of a factor structure for the noise component, separate from the latent systematic risk factors that characterize the cross-sectional variation in the frictionless returns. The new estimator provides theoretically more efficient and finite-sample more accurate estimates of large-scale integrated covolatility and correlation matrices than other recently developed realized estimation procedures. These theoretical and simulation-based findings are further corroborated by an empirical application related to portfolio allocation and risk minimization involving several hundred individual stocks.

Prevalence of malaria parasitemia among pregnant women in a general hospital in Abraka Delta State

Malaria during the period of pregnancy is a key public health concern, with noteworthy risks for mothers, fetus and even newborn babies. This cross-sectional study was carried out to ascertain the prevalence of malaria and the latent risk factors for the malaria infections among pregnant women in a Government General Hospital in Abraka, Delta State Nigeria. One hundred pregnant women were included in the study after informed verbal consent was obtained. Peripheral blood samples were collected and then thick blood smears prepared and stained with field stains A and B to verify presence of malaria parasitaemia. The relationship between education level, age and trimester of pregnancy, with occurrence of malaria infection during pregnancy were analyzed using the chi-square test. In this study, a total of ninety five percent (95%) of the pregnant women were infected with malaria parasites, with a mean parasite 33.33. The prevalence and the parasite density progressed with age. Further analysis of the data showed that lack of education and environmental factors were greatly associated with malaria infection. Malaria is still the main public health hazard among pregnant women mostly due to lack or inadequate education and limited awareness about malaria prevention measures among this population.Keywords: Malaria, Pregnant women, parasitaemia, blood, Abraka

A Statistical Factor Asset Pricing Model Versus the 4-Factor Model

This research aims to compare the performance of a statistical factor asset pricing model with the Fama-French-Carhart 4-factor model. We perform a Principal Component Analysis (PCA) to extract latent risk factors using data of stocks listed on B3 from 2001 to 2015. We test the abilities of the two models to explain assets' returns both in the time-series and in the cross-section dimension. We found that the statistical factor models generates statistically significant abnormal returns in the time-series analysis while the 4-factor model does not. In the cross section dimension, neither model generates significant abnormal returns but they also are not able to generate positive risk premia. Similar results are found if we consider different sets of time and assets. Therefore, although the 4-factor model performs slightly better in the set of tests, neither of the models can be considered fully adequate to explain expected returns of assets in the Brazilian stock market.

Factor Models for Asset Pricing

This paper develops a methodology for inference on asset pricing models linear in latent risk factors, valid when the number of assets diverges but the time series dimension is fixed, possibly very small. We cast the factor model within the Arbitrage Pricing Theory of Ross (1976) and show that the no-arbitrage condition permits to identify the risk premia as the expectation of the latent risk factors. This result paves the way to an inferential procedure for the factors’ risk premia and for the stochastic discount factor, spanned by the latent risk factors. The strength of our set up is that it naturally handles time-varying factor models, where every feature is allowed to be time-varying including loadings, idiosyncratic risk and the number of risk factors. Several Monte Carlo experiments corroborate our theoretical findings. An empirical application based on individual asset returns data demonstrates the power of the methodology, allowing to tease out the empirical content of the time-variation stemming from asset pricing theory.

Scenario Analysis for Derivatives Portfolios via Dynamic Factor Models

A classic approach to financial risk management is the use of scenario analysis to stress test portfolios. In the case of an S&P 500 options portfolio, for example, a scenario analysis might report a P&L of −$1m in the event the S&P 500 falls 5% and its implied volatility surface increases by 3 percentage points. But how accurate is this reported value of −$1m? Such a number is typically computed under the (implicit) assumption that all other risk factors are set to zero. But this assumption is generally not justified as it ignores the often substantial statistical dependence among the risk factors. In particular, the expected values of the non-stressed factors conditional on the values of the stressed factors are generally non-zero. Moreover, even if the non-stressed factors were set to their conditional expected values rather than zero, the reported P&L might still be inaccurate due to convexity effects, particularly in the case of derivatives portfolios. A further weakness of this standard approach to scenario analysis is that the reported P&L numbers are generally not back-tested so their accuracy is not subjected to any statistical tests. There are many reasons for this but perhaps the main one is that scenario analysis for derivatives portfolios is typically conducted without having a probabilistic model for the underlying dynamics of the risk factors under the physical measure P. In this paper we address these weaknesses by embedding the scenario analysis within a dynamic factor model for the underlying risk factors. Such an approach typically requires multivariate state-space models that can model the real-world behavior of financial markets where risk factors are often latent, and that are sufficiently tractable so that we can compute (or simulate from) the conditional distribution of unstressed risk factors. We demonstrate how this can be done for observable as well as latent risk factors in examples drawn from options and fixed income markets. We show how the two forms of scenario analysis can lead to dramatically different results particularly in the case of portfolios that have been designed to be neutral to a subset of the risk factors.

Risk-Neutral Cumulants, Expected Risk Premia, and Future Stock Returns

This paper builds an empirical model to connect option-implied cumulants with expected risk premia through latent risk factors. Expected risk premia on individual stocks are estimated by applying a new partial least squares-based method on risk-neutral cumulants at different orders and various maturities. The filtered expected risk premia based on the second and third order risk-neutral cumulants exhibit a considerably large dispersion across stocks, which further generates a wide cross-sectional variation in future realized risk premia. I find a positive relationship between the ex-ante filtered expected risk premium and future realized risk premium during the period of 1996-2017. A strategy that goes long the decile portfolio with the largest filtered expected risk premium and short the decile portfolio with the smallest filtered expected risk premium yields a Fama-French-Carhart alpha of 1.06% per month (t-stat 3.75). The filtered expected risk premium of the above trading strategy is correlated (Corr: 51.5%) with the equal-weighted stock's expected excess return in Martin and Wagner (2018). Moreover, I show that the predictability of the filtered expected risk premium can be potentially explained by informed trading driven by short-selling constraints.

A Novel Framework for Sustainable Traffic Safety Programs Using the Public as Sensors of Hazardous Road Information

Traditionally, traffic safety improvement programs (TSIPs) have been based on the number of crashes at a specific location or their severity. However, the crash datasets used for such programs are obtained from the police and include two limitations: not all crashes are collected by the police (most minor and near-miss crashes are not reported), and the traditional process uses crash data recorded for the past two or three years (meaning most data inevitably include a time lag). To overcome these limitations, this study proposes a new approach for a TSIP based on citizen participation through an online survey that is broadcasted through social media. The method uses the public as sensors of hazardous road information, which means that information can be collected on individual experiences of minor crashes and latent risk factors, such as near misses and traffic conflicts. To demonstrate this approach, a case study was carried out in a small district in the city of Goyang, Korea, which has one of the highest usage rates of social media technologies. The proposed method and a traditional method were both assessed.

Analysis of risk factors of metabolic syndrome using a structural equation model: a cohort study.

We aimed to use a structural equation model (SEM) to determine the interrelations between various risk factors, including latent variables, involved in the development of metabolic syndrome(MetS). This study used data derived from the MJ Longitudinal Health Check-up Population Database for participants aged 20 to 70 years, who were asymptomatic for MetS at enrollment and were followed up for 5 years. A SEM was applied to investigate the attributions of MetS and the interrelations between different risk factors. Socioeconomic status (SES), living habits, components of metabolic syndrome (COMetS), and blood pressure had a diverse impact on the onset of MetS, directly and (or) indirectly. When investigating the latent risk factors and the interrelations between different risk factors. The standardized total effect (the sum of the direct and indirect effects, βt) of SES, living habits, blood pressure and COMetS on the onset of MetS was 0.084, -0.179, 0.154, and 0.353, respectively. SES, as a distal risk factor, directly influenced living habits, blood pressure, and COMetS with standardized regression coefficients (βr) of -0.079 (P < 0.001), 0.200 (P < 0.001), and -0.163 (P < 0.001) respectively. Unfavorable living habits exerted an inverse effect on blood pressure and COMetS (βr = -0.101, P < 0.001; βr = -0.463, P < 0.001), which was an important path way for developing MetS. These results demonstrate that individuals with a higher level of SES are susceptible to high blood pressure and are at increased risk for MetS. Additionally, there is a decrease in exercise and an increase in smoking and consumption of alcohol corresponded to an increase in metabolic risk factors.

Multi-Sensor InSAR Analysis of Progressive Land Subsidence over the Coastal City of Urayasu, Japan

In earthquake-prone areas, identifying patterns of ground deformation is important before they become latent risk factors. As one of the severely damaged areas due to the 2011 Tohoku earthquake in Japan, Urayasu City in Chiba Prefecture has been suffering from land subsidence as a part of its land was built by a massive land-fill project. To investigate the long-term land deformation patterns in Urayasu City, three sets of synthetic aperture radar (SAR) data acquired during 1993–2006 from European Remote Sensing satellites (ERS-1/-2 (C-band)), during 2006–2010 from the Phased Array L-band Synthetic Aperture Radar onboard the Advanced Land Observation Satellite (ALOS PALSAR (L-band)) and from 2014–2017 from the ALOS-2 PALSAR-2 (L-band) were processed by using multitemporal interferometric SAR (InSAR) techniques. Leveling survey data were also used to verify the accuracy of the InSAR-derived results. The results from the ERS-1/-2, ALOS PALSAR and ALOS-2 PALSAR-2 data processing showed continuing subsidence in several reclaimed areas of Urayasu City due to the integrated effects of numerous natural and anthropogenic processes. The maximum subsidence rate of the period from 1993 to 2006 was approximately 27 mm/year, while the periods from 2006 to 2010 and from 2014 to 2017 were approximately 30 and 18 mm/year, respectively. The quantitative validation results of the InSAR-derived deformation trend during the three observation periods are consistent with the leveling survey data measured from 1993 to 2017. Our results further demonstrate the advantages of InSAR measurements as an alternative to ground-based measurements for land subsidence monitoring in coastal reclaimed areas.

Bayesian semiparametric failure time models for multivariate censored data with latent variables.

In this paper, we propose a semiparametric failure time model to analyze multivariate censored data with latent variables. The proposed model generalizes the conventional accelerated failure time model to accommodate latent risk factors that could be measured by multiple observed variables through a factor analysis and to incorporate additive nonparametric functions of observed and latent risk factors to examine their functional effects on multivariate failure times of interest. A Bayesian approach, along with Bayesian P-splines and Markov chain Monte Carlo techniques, is developed to estimate the unknown parameters and functions. The empirical performance of the proposed methodology is evaluated by a simulation study. An application to a study on the risk factors of two diabetes complications is presented.

Private sector tuberculosis prevention in the US: Characteristics associated with interferon-gamma release assay or tuberculin skin testing.

ObjectiveTo determine whether latent tuberculosis infection risk factors are associated with an increased likelihood of latent tuberculosis infection testing in the US private healthcare sector.Data sourceA national sample of medical and pharmacy claims representing services rendered January 2011 through December 2013 for 3,997,986 commercially insured individuals in the US who were 0 to 64 years of age.Study designWe used multivariable logistic regression models to determine whether TB/LTBI risk factors were associated with an increased likelihood of Interferon-Gamma Release Assay (IGRA) or Tuberculin Skin Test (TST) testing in the private sector.Principal findings4.31% (4.27–4.34%) received at least one TST/IGRA test between 2011 and 2013 while 1.69% (1.67–1.72%) received a TST/IGRA test in 2013. Clinical risk factors associated with a significantly increased likelihood of testing included HIV, immunosuppressive therapy, exposure to tuberculosis, a history of tuberculosis, diabetes, tobacco use, end stage renal disease, and alcohol use disorder. Other significant variables included gender, age, asthma, the state tuberculosis rate, population density, and percent of foreign-born persons in a county.ConclusionsPrivate sector TST/IGRA testing is not uncommon and testing varies with clinical risk indicators. Thus, the private sector can be a powerful resource in the fight against tuberculosis. Analyses of administrative data can inform how best to leverage private sector healthcare toward tuberculosis prevention activities.

Bayesian analysis of semi-parametric Cox models with latent variables.

Respiratory cancer is one of the most commonly diagnosed cancers as well as the leading cause of cancer death. Numerous efforts have been devoted to reducing the death rate of respiratory cancer. In this article, we propose a semi-parametric Cox model with latent variables to assess the effects of observed and latent risk factors on survival time of respiratory cancer. The characteristics of latent risk factors are characterized via multiple observed indicators by a confirmatory factor analysis model. We develop a Bayesian estimation procedure to obtain the estimates of parameters. Simulation shows that the performance of the proposed methodology is satisfactory. The proposed method is applied to analyze the Surveillance, Epidemiology, and End Results Program data set.

Application of SCM with Bayesian B-Spline to Spatio-Temporal Analysis of Hypertension in China.

Most previous research on the disparities of hypertension risk has neither simultaneously explored the spatio-temporal disparities nor considered the spatial information contained in the samples, thus the estimated results may be unreliable. Our study was based on the China Health and Nutrition Survey (CHNS), including residents over 12 years old in seven provinces from 1991 to 2011. Bayesian B-spline was used in the extended shared component model (SCM) for fitting temporal-related variation to explore spatio-temporal distribution in the odds ratio (OR) of hypertension, reveal gender variation, and explore latent risk factors. Our results revealed that the prevalence of hypertension increased from 14.09% in 1991 to 32.37% in 2011, with men experiencing a more obvious change than women. From a spatial perspective, a standardized prevalence ratio (SPR) remaining at a high level was found in Henan and Shandong for both men and women. Meanwhile, before 1997, the temporal distribution of hypertension risk for both men and women remained low. After that, notably since 2004, the OR of hypertension in each province increased to a relatively high level, especially in Northern China. Notably, the OR of hypertension in Shandong and Jiangsu, which was over 1.2, continuously stood out after 2004 for males, while that in Shandong and Guangxi was relatively high for females. The findings suggested that obvious spatial–temporal patterns for hypertension exist in the regions under research and this pattern was quite different between men and women.

The Perception and Views of Leisure-Time Runners on Marathon Running and Related Health Services in Turkey

Introduction: The frequency and numbers of activities of marathon running in Turkey are overwhelming, but a documentation on participants is missing. We, therefore, aimed to know the perception and view of leisure-time runners (LTR) on the marathon and related health services. Materials and Methods: Sixty-three LTR agreed and gave verbal consent to participate. Participants were purposefully selected (purposive sample) from the community of LTR. The survey included 22 questions. The survey was piloted with ten LTR and the results of the pilot questionnaire were not included in the final dataset. Results: The mean age of participants was 37.2 (range 20-61); 11 (31%) were female and 36 (69%) were male; 20 (32%) were single, 36 (56%) were married, and 5 (8%) divorced/widowed. The reason for running was as follows: to stay healthy (49; 36%), to keep fit (34; 26%), to minimize risk factors of disease (25; 19%), to lose weight (15; 11%), and to get socialized (10; %8). Forty-six (%72) practiced sports in their youth. Ten (17%) had a chronic condition and 6 (11%) were on a drug treatment for their condition. Conclusion: Since these athletes were formerly sedentary and might harbor latent risk factors, which could be provoked during after strenuous activities like marathons, the provision of a rigorous screening for this athletes should be provided.

Bayesian proportional hazards model with latent variables.

We consider a joint modeling approach that incorporates latent variables into a proportional hazards model to examine the observed and latent risk factors of the failure time of interest. An exploratory factor analysis model is used to characterize the latent risk factors through multiple observed variables. In commonly used confirmatory factor analysis, the number of latent variables and their observed indicators are specified prior to analysis. By contrast, the exploratory factor analysis model allows such information to be fully determined by the data. A Bayesian approach coupled with efficient sampling methods is developed to conduct statistical inference, and the performance of the proposed methodology is confirmed through simulations. The model is applied to a study on the risk factors of chronic kidney disease for patients with type 2 diabetes.

남자 중학교 태권도 겨루기 선수들의 지속적 통증 유병률과 잠재위험요인

The objectives of present study are to calculate the prevalence of persistent overuse pain (PP) and to find latent risk factors related to PP among male middle school Taekwondo sparring athletes. A cross sectional study was utilized for the study. A total of 327 competitors participated voluntarily in this research (mean age = 13.9 years). All study participants must meet the criteria of subject selection such as a middle school student athlete and a current competition participant. As a result, ovreall prevalence rates of PP and past injury were 27.8% (95% CI: 23.0-32.7) and 49.2% (95% CI: 43.8-54.7) respecitvely. The past injury history, grade, age and conditioning training of other potential risk factors showed significant differences with respect to PP rates via χ2-test or independent t-test (p＜.05). The final binary logistic regression analysis showed that there were significant differences between PP and the two variables (past injury history and conditioning training) (p＜.05). In other words, competitors who didn’t have a past injury histroy were less likely to develop PP(OR: 0.15). Also, the competitors who did not train additional conditioning training were less likely to complain PP (OR: 0.46). The ankle joint (19.4%) was the most dominant complain of PP among other body part. In conclusions, overall prevalence rate of PP and past injury history are high among male middle school Taekwondo sparring competitors. More research is continuously needed relate to this topic.

Some Characteristics Are Risk Exposures, and the Rest Are Irrelevant

We propose a new modeling approach for the cross section of returns. Our method, Instrumented Principal Components Analysis (IPCA), allows for latent factors and time-varying loadings by introducing observable characteristics that instrument for the unobservable dynamic loadings. If the characteristics/expected return relationship is driven by compensation for exposure to latent risk factors, IPCA will identify the corresponding latent factors. If no such factors exist, IPCA infers that the characteristic effect is compensation without risk and allocates it to an intercept. Studying returns and characteristics at the stock-level, we find that four IPCA factors explain the cross section of average returns significantly more accurately than existing factor models and produce characteristic-associated anomaly intercepts that are small and statistically insignificant. Furthermore, among a large collection of characteristics explored in the literature, only eight are statistically significant in the IPCA specification and are responsible for nearly 100% of the model's accuracy. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Bayesian nonparametric areal wombling for small-scale maps with an application to urinary bladder cancer data from Connecticut.

With increasingly abundant spatial data in the form of case counts or rates combined over areal regions (eg, ZIP codes, census tracts, or counties), interest turns to formal identification of difference "boundaries," or barriers on the map, in addition to the estimated statistical map itself. "Boundary" refers to a border that describes vastly disparate outcomes in the adjacent areal units, perhaps caused by latent risk factors. This article focuses on developing a model-based statistical tool, equipped to identify difference boundaries in maps with a small number of areal units, also referred to as small-scale maps. This article proposes a novel and robust nonparametric boundary detection rule based on nonparametric Dirichlet processes, later referred to as Dirichlet process wombling (DPW) rule, by employing Dirichlet process-based mixture models for small-scale maps. Unlike the recently proposed nonparametric boundary detection rules based on false discovery rates, the DPW rule is free of ad hoc parameters, computationally simple, and readily implementable in freely available software for public health practitioners such as JAGS and OpenBUGS and yet provides statistically interpretable boundary detection in small-scale wombling. We offer a detailed simulation study and an application of our proposed approach to a urinary bladder cancer incidence rates dataset between 1990 and 2012 in the 8 counties in Connecticut.

Determining the Rank of the Beta Matrix in a Factor Model with Factor-Candidate Regressors

Many variables have been proposed as common risk factors driving asset returns, which we refer to as “empirical factors” to distinguish them from true latent factors. We examine how many true latent factors are correlated with the empirical factors by estimating the rank of the beta matrix corresponding to the empirical factors. To obtain more comprehensive empirical results, we analyze data with large numbers of portfolios and individual stock returns. While many rank estimators are available in the literature, most of them are designed for data with a relatively small number of asset returns. Thus, we develop and use a new rank estimator that is consistent regardless of the number of response variables. Analyzing twenty-six empirical factors, we find that they capture at most five different latent risk factors. Additionally, most of the multifactor asset pricing models we consider are found to have very limited power to identify risk premiums.