955 resultados para Multivariate risk model
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It is well known that cointegration between the level of two variables (e.g. prices and dividends) is a necessary condition to assess the empirical validity of a present-value model (PVM) linking them. The work on cointegration,namelyon long-run co-movements, has been so prevalent that it is often over-looked that another necessary condition for the PVM to hold is that the forecast error entailed by the model is orthogonal to the past. This amounts to investigate whether short-run co-movememts steming from common cyclical feature restrictions are also present in such a system. In this paper we test for the presence of such co-movement on long- and short-term interest rates and on price and dividend for the U.S. economy. We focuss on the potential improvement in forecasting accuracies when imposing those two types of restrictions coming from economic theory.
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This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.
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O objetivo deste estudo é propor a implementação de um modelo estatístico para cálculo da volatilidade, não difundido na literatura brasileira, o modelo de escala local (LSM), apresentando suas vantagens e desvantagens em relação aos modelos habitualmente utilizados para mensuração de risco. Para estimação dos parâmetros serão usadas as cotações diárias do Ibovespa, no período de janeiro de 2009 a dezembro de 2014, e para a aferição da acurácia empírica dos modelos serão realizados testes fora da amostra, comparando os VaR obtidos para o período de janeiro a dezembro de 2014. Foram introduzidas variáveis explicativas na tentativa de aprimorar os modelos e optou-se pelo correspondente americano do Ibovespa, o índice Dow Jones, por ter apresentado propriedades como: alta correlação, causalidade no sentido de Granger, e razão de log-verossimilhança significativa. Uma das inovações do modelo de escala local é não utilizar diretamente a variância, mas sim a sua recíproca, chamada de “precisão” da série, que segue uma espécie de passeio aleatório multiplicativo. O LSM captou todos os fatos estilizados das séries financeiras, e os resultados foram favoráveis a sua utilização, logo, o modelo torna-se uma alternativa de especificação eficiente e parcimoniosa para estimar e prever volatilidade, na medida em que possui apenas um parâmetro a ser estimado, o que representa uma mudança de paradigma em relação aos modelos de heterocedasticidade condicional.
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Using a sequence of nested multivariate models that are VAR-based, we discuss different layers of restrictions imposed by present-value models (PVM hereafter) on the VAR in levels for series that are subject to present-value restrictions. Our focus is novel - we are interested in the short-run restrictions entailed by PVMs (Vahid and Engle, 1993, 1997) and their implications for forecasting. Using a well-known database, kept by Robert Shiller, we implement a forecasting competition that imposes different layers of PVM restrictions. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to the unrestricted VAR. Moreover, imposing short-run restrictions produces forecast winners 70% of the time for the target variables of PVMs and 63.33% of the time when all variables in the system are considered.
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We develop an affine jump diffusion (AJD) model with the jump-risk premium being determined by both idiosyncratic and systematic sources of risk. While we maintain the classical affine setting of the model, we add a finite set of new state variables that affect the paths of the primitive, under both the actual and the risk-neutral measure, by being related to the primitive's jump process. Those new variables are assumed to be commom to all the primitives. We present simulations to ensure that the model generates the volatility smile and compute the "discounted conditional characteristic function'' transform that permits the pricing of a wide range of derivatives.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Objective To evaluate the prevalence of metabolic syndrome (MetS) and its associated risk factors in Brazilian postmenopausal women.Methods In this cross-sectional study, a total of 368 postmenopausal women, aged 40-75 years, seeking health care at a public outpatient center in Southeastern Brazil, were included. According to the US National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) guidelines, MetS was diagnosed in subjects with three or more of the following: waist circumference >= 88 cm, blood pressure >= 130/85 mHg, triglycerides >= 150 mg/dl, high density lipoprotein cholesterol <50 mg/dl and glucose >= 110 mg/dl. Data on past medical history, tobacco use, anthropometric indicators, and values of C-reactive protein (CRP) were collected. Multivariate analysis, using a logistic regression model (odds ratio, OR) was used to evaluate the influence of various simultaneous MetS risk factors.Results The prevalence of having at least three, four and five MetS diagnostic criteria were met in 39.6%, 16.8% and 3.8% of the cases, respectively. The most prevalent risk factor was abdominal obesity, affecting 62.5% of women. The risk of MetS increased with a personal history of diabetes (OR 5.95, 95% confidence interval (CI) 2.82-12.54), hypertension (OR 4.52, 95% CI 2.89-7.08), cardiovascular disease (OR 2.16, 95% CI 1.18-3.94) and high CRP (>1 mg/dl) (OR 3.35, 95% CI 1.65-6.79). Plasma CRP levels increased with the number of MetS components present. Age, time since menopause and smoking had no influence, while hormone therapy reduced MetS risk (OR 0.64, 95% CI 0.42-0.97).Conclusion Metabolic syndrome was highly prevalent among Brazilian postmenopausal women seeking gynecologic health care. Abdominal obesity, diabetes, hypertension and high CRP were strong MetS predictors and hormone therapy appeared to play a protective role for this condition.
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O objetivo do artigo foi avaliar o uso da lógica fuzzy para estimar possibilidade de óbito neonatal. Desenvolveu-se um modelo computacional com base na teoria dos conjuntos fuzzy, tendo como variáveis peso ao nascer, idade gestacional, escore de Apgar e relato de natimorto. Empregou-se o método de inferência de Mamdani, e a variável de saída foi o risco de morte neonatal. Criaram-se 24 regras de acordo com as variáveis de entrada, e a validação do modelo utilizou um banco de dados real de uma cidade brasileira. A acurácia foi estimada pela curva ROC; os riscos foram comparados pelo teste t de Student. O programa MATLAB 6.5 foi usado para construir o modelo. Os riscos médios foram menores para os que sobreviveram (p < 0,001). A acurácia do modelo foi 0,90. A maior acurácia foi com possibilidade de risco igual ou menor que 25% (sensibilidade = 0,70, especificidade = 0,98, valor preditivo negativo = 0,99 e valor preditivo positivo = 0,22). O modelo mostrou acurácia e valor preditivo negativo bons, podendo ser utilizado em hospitais gerais.
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Background Post-transplant anemia is multifactorial and highly prevalent. Some studies have associated anemia with mortality and graft failure. The purpose of this study was to assess whether the presence of anemia at 1 year is an independent risk factor of mortality and graft survival. Methods All patients transplanted at a single center who survived at least 1 year after transplantation and showed no graft loss (n = 214) were included. Demographic and clinical data were collected at baseline and at 1 year. Patients were divided into two groups (anemic and nonanemic) based on the presence of anemia (hemoglobin<130 g/l in men and 120 g/l in women). Results Baseline characteristics such as age, gender, type of donor, CKD etiology, rejection, andmismatches were similar in both groups. Creatinine clearance was similar in both anemic and nonanemic groups (69.32 ± 29.8 × 75.69 ± 30.5 ml/mim; P = 0.17). A Kaplan- Meier plot showed significantly poorer death-censored graft survival in the anemic group, P = 0.003. Multivariate analysis revealed that anemic patients had a hazard ratio for the graft loss of 3.85 (95% CI: 1.49-9.96; P = 0.005). Conclusions In this study, anemia at 1 year was independently associated with death-censored graft survival and anemic patients were 3.8-fold more likely to lose the graft. © 2010 Springer Science+Business Media, B.V.
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Background: We aimed to verify the association of risk behavior aggregation in different categories of physical activity (PA) with the presence of cardiovascular risk factors (RF) employees at a public university. Method. We analyzed data of 376 employees, which were visited in their workplace for measurement of weight, height and questionnaires to identify the risk behaviors and risk factors. Chi-square test was used to analyze the association between the dependent and independent variables and binary logistic regression was used to construct a multivariate model for the observed associations. Results: Associations were found between the aggregation of following risk behaviors: smoking, alcohol consumption and physical inactivity, considered in different categories of PA, and the increase in RF, except for the presence of hypertriglyceridemia. Individuals with two or more risk behaviors in occupational PA category are more likely to be hypertensive (3.04 times) and diabetes (3.44 times). For the free time PA category, these individuals were 3.18 times more likely to have hypercholesterolemia and for locomotion PA, more likely to be hypertensive (2.42 times) and obese (2.51 times). Conclusion: There are association between the aggregation of two or more risk behaviors and the presence of cardiovascular RF. © 2013 Bernardo et al.; licensee BioMed Central Ltd.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Background: Polycystic ovary syndrome (PCOS) is an endocrine disorder associated with metabolic dysfunction and changes in cardiovascular risk markers, and using oral contraceptives (OCs) may exert a further negative effect on these alterations in patients with PCOS. Thus, the primary objective of this study was to assess the effects on arterial function and structure of an OC containing chlormadinone acetate (2 mg) and ethinylestradiol (30 mcg), alone or combined with spironolactone (OC+SPL), in patients with PCOS. Study Design: This was a randomized, controlled clinical trial. Fifty women with PCOS between 18 and 35 years of age were randomized by a computer program to use OC or OC+SPL. Brachial artery flow-mediated vasodilation, carotid intima-media thickness and the carotid artery stiffness index were evaluated at baseline and after 6 and 12 months. Serum markers for cardiovascular disease were also analyzed. The intragroup data were analyzed using analysis of variance with Tukey's post hoc test. A multivariate linear regression model was used to analyze the intergroup data. Results: At 12 months, the increase in mean total cholesterol levels was greater in the OC+SPL group than in the OC group (27% vs. 13%, respectively; p=.02). The increase in mean sex hormone-binding globulin levels was greater in the OC group than in the OC+SPL group (424% vs. 364%, respectively; p=.01). No statistically significant differences between the groups were found for any of the other variables. Conclusion: The addition of spironolactone to an OC containing chlormadinone acetate and ethinylestradiol conferred no cardiovascular risk-marker advantages in young women with PCOS. (C) 2012 Elsevier Inc. All rights reserved.
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The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.
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Background: The complex natural history of human papillomavirus (HPV) infections following a single HPV test can be modeled as competing-risks events (i.e., no-, transient- or persistent infection) in a longitudinal setting. The covariates associated with these compet ng events have not been previously assessed using competing-risks regression models. Objectives: To gain further insights in the outcomes of cervical HPV infections, we used univariate- and multivariate competing-risks regression models to assess the covariaies associated with these competing events. Study Design and Methods: Covariates associated with three competing outcomes (no-, transient- or persistent HR-HPV infection) were analysed in a sub-cohort of 1,865 women prospectively followed-up in the NIS (n = 3,187) and LAMS Study (n = 12,114). Results: In multivariate competing-risks models (with two other outcomes as competing events), permanently HR-HPV negative outcome was significantly predicted only by the clearance of ASCUS+Pap during FU, while three independent covariates predicted transient HR-HPV infections: i) number of recent (< 12 months) sexual partners (risk increased), ii) previous Pap screening history (protective), and history of previous CIN (increased risk). The two most powerful predictors of persistent HR-HPV infections were persistent ASCUS+Pap (risk increased), and previous Pap screening history (protective). In pair-wise comparisons, number of recent sexual partners and previous CIN history increase the probability of transient HR-HPV infection against the HR-HPV negative competing event, while previous Pap screening history is protective. Persistent ASCUS+Pap during FU and no previous Pap screening history are significantly associated with the persistent HR-HPV outcome (compared both with i) always negative, and ii) transient events), whereas multiparity is protective. Conclusions: Different covariates are associated with the three main outcomes of cervical HPV infections. The most significant covariates of each competing events are probably distinct enough to enable constructing of a risk-profile for each main outcome.