Gross proteinuria is a strong risk predictor for cardiovascular mortality in Brazilian type 2 diabetic patients

Increased proteinuria is recognized as a risk predictor for all-cause and cardiovascular mortality in diabetic patients; however, no study has evaluated these relationships in Brazilian patients. The aim of this study was to investigate the prognostic value of gross proteinuria for all-cause and cardiovascular mortalities and for cardiovascular morbidity in a cohort study of 471 type 2 diabetic individuals followed for up to 7 years. Several clinical, laboratory and electrocardiographic variables were obtained at baseline. The relative risks for all-cause, cardiovascular and cardiac mortalities and for cardiovascular and cardiac events associated with the presence of overt proteinuria (>0.5 g/24 h) were assessed by Kaplan-Meier survival curves and by multivariate Cox regression model. During a median follow-up of 57 months (range 2-84 months), 121 patients (25.7%) died, 44 from cardiovascular and 30 from cardiac causes, and 106 fatal or non-fatal cardiovascular events occurred. Gross proteinuria was an independent risk predictor of all-cause, cardiovascular and cardiac mortalities and of cardiovascular morbidity with adjusted relative risks ranging from 1.96 to 4.38 for the different endpoints. This increased risk remained significant after exclusion of patients with prior cardiovascular disease at baseline from the multivariate analysis. In conclusion, gross proteinuria was a strong predictor of all-cause, cardiovascular and cardiac mortalities and also of cardiovascular morbidity in a Brazilian cohort of type 2 diabetic patients. Intervention studies are necessary to determine whether the reduction of proteinuria can decrease morbidity and mortality of type 2 diabetes in Brazil.

Utility of a novel risk score for prediction of ventricular tachycardia and cardiac death in chronic Chagas disease - the SEARCH-RIO study

The SEARCH-RIO study prospectively investigated electrocardiogram (ECG)-derived variables in chronic Chagas disease (CCD) as predictors of cardiac death and new onset ventricular tachycardia (VT). Cardiac arrhythmia is a major cause of death in CCD, and electrical markers may play a significant role in risk stratification. One hundred clinically stable outpatients with CCD were enrolled in this study. They initially underwent a 12-lead resting ECG, signal-averaged ECG, and 24-h ambulatory ECG. Abnormal Q-waves, filtered QRS duration, intraventricular electrical transients (IVET), 24-h standard deviation of normal RR intervals (SDNN), and VT were assessed. Echocardiograms assessed left ventricular ejection fraction. Predictors of cardiac death and new onset VT were identified in a Cox proportional hazard model. During a mean follow-up of 95.3 months, 36 patients had adverse events: 22 new onset VT (mean±SD, 18.4±4‰/year) and 20 deaths (26.4±1.8‰/year). In multivariate analysis, only Q-wave (hazard ratio, HR=6.7; P<0.001), VT (HR=5.3; P<0.001), SDNN<100 ms (HR=4.0; P=0.006), and IVET+ (HR=3.0; P=0.04) were independent predictors of the composite endpoint of cardiac death and new onset VT. A prognostic score was developed by weighting points proportional to beta coefficients and summing-up: Q-wave=2; VT=2; SDNN<100 ms=1; IVET+=1. Receiver operating characteristic curve analysis optimized the cutoff value at >1. In 10,000 bootstraps, the C-statistic of this novel score was non-inferior to a previously validated (Rassi) score (0.89±0.03 and 0.80±0.05, respectively; test for non-inferiority: P<0.001). In CCD, surface ECG-derived variables are predictors of cardiac death and new onset VT.

How Is Credit Risk Estimated? Discussion and Evidence based on Cross-Industry Sample Utilizing the KMV Model

The purpose of this thesis is to focus on credit risk estimation. Different credit risk estimation methods and characteristics of credit risk are discussed. The study is twofold, including an interview of a credit risk specialist and a quantitative section. Quantitative section applies the KMV model to estimate credit risk of 12 sample companies from three different industries: automobile, banking and financial sector and technology. Timeframe of the estimation is one year. On the basis of the KMV model and the interview, implications for analysis of credit risk are discussed. The KMV model yields consistent results with the existing credit ratings. However, banking and financial sector requires calibration of the model due to high leverage of the industry. Credit risk is considerably driven by leverage, value and volatility of assets. Credit risk models produce useful information on credit worthiness of a business. Yet, quantitative models often require qualitative support in the decision-making situation.

Liquidity Risk in the Finnish Stock Market

This study explores the pricing of liquidity risk and its effect on stock returns in the Finnish stock market. In addition to that, it investigates whether there is a trend in liquidity risk. Finally, it analyzes whether the two chosen liquidity measures provide different results. The data consists of all the common shares listed in the Finnish stock market during the period of 1/1997–7/2015. To examine whether liquidity risk affects stock returns in the Finnish stock market, this study utilizes a conditional version of liquidity-adjusted capital asset pricing model (LCAPM) by Acharya and Pedersen (2005). Two recently proposed illiquidity measures – PQS and AdjILLIQ – are used in the empirical estimation to see whether there are differences in the results between the measures. The time-varying conditional liquidity risks are estimated by using a multivariate DCC-GARCH model, while the pricing of the liquidity risk is conducted by applying fixed effect panel regression. The results imply that investors in the Finnish stock market are willing to pay a premium to hedge from wealth shocks and having liquid assets during the declined market liquidity. However, investors are not willing to pay a premium for stocks with higher returns during illiquid markets. The total annualized illiquidity premiums found in the Finnish stock market are 1.77% and 1.04%, based on the PQS and AdjILLIQ measures, respectively. The study also shows that liquidity risk does not exhibit decreasing trend, and investors should consider liquidity risk in their portfolio diversification in the Finnish stock market.

The Shape of the Risk Premium: Evidence from a Semiparametric Garch Model.

We examine the relationship between the risk premium on the S&P 500 index return and its conditional variance. We use the SMEGARCH - Semiparametric-Mean EGARCH - model in which the conditional variance process is EGARCH while the conditional mean is an arbitrary function of the conditional variance. For monthly S&P 500 excess returns, the relationship between the two moments that we uncover is nonlinear and nonmonotonic. Moreover, we find considerable persistence in the conditional variance as well as a leverage effect, as documented by others. Moreover, the shape of these relationships seems to be relatively stable over time.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Investigating the role of potential genetic markers that can predict risk for steroid refractory inflammatory bowel disease

Contexte - La variation interindividuelle de la réponse aux corticostéroïdes (CS) est un problème important chez les patients atteints de maladies inflammatoires d’intestin. Ce problème est bien plus accentué chez les enfants avec la prévalence de la corticodépendance extrêmement (~40 %) élevée. La maladie réfractaire au CS a des répercussions sur le développement et le bien-être physique et psychologique des patients et impose des coûts médicaux élevés, particulièrement avec la maladie active comparativement à la maladie en rémission, le coût étant 2-3 fois plus élevé en ambulatoire et 20 fois plus élevé en hôpital. Il est ainsi primordial de déterminer les marqueurs prédictifs de la réponse aux CS. Les efforts précédents de découvrir les marqueurs cliniques et démographiques ont été équivoques, ce qui souligne davantage le besoin de marqueurs moléculaires. L'action des CS se base sur des processus complexes déterminés génétiquement. Deux gènes, le ABCB1, appartenant à la famille des transporteurs transmembraneaux, et le NR3C1, encodant le récepteur glucocorticoïde, sont des éléments importants des voies métaboliques. Nous avons postulé que les variations dans ces gènes ont un rôle dans la variabilité observée de la réponse aux CS et pourraient servir en tant que les marqueurs prédictifs. Objectifs - Nous avons visé à: (1) examiner le fardeau de la maladie réfractaire aux CS chez les enfants avec la maladie de Crohn (MC) et le rôle des caractéristiques cliniques et démographiques potentiellement liés à la réponse; (2) étudier l'association entre les variantes d'ADN de gène ABCB1 et la réponse aux CS; (3) étudier les associations entre les variantes d'ADN de gène NR3C1 et la réponse aux CS. Méthodes - Afin d’atteindre ces objectifs, nous avons mené une étude de cohorte des patients recrutés dans deux cliniques pédiatriques tertiaires de gastroentérologie à l’Ottawa (CHEO) et à Montréal (HSJ). Les patients avec la MC ont été diagnostiqués avant l'âge de 18 ans selon les critères standard radiologiques, endoscopiques et histopathologiques. La corticorésistance et la corticodépendance ont été définies en adaptant les critères reconnus. L’ADN, acquise soit du sang ou de la salive, était génotypée pour des variations à travers de gènes ABCB1 et NR3C1 sélectionnées à l’aide de la méthodologie de tag-SNP. La fréquence de la corticorésistance et la corticodépendance a été estimée assumant une distribution binomiale. Les associations entre les variables cliniques/démographiques et la réponse aux CS ont été examinées en utilisant la régression logistique en ajustant pour des variables potentielles de confusion. Les associations entre variantes génétiques de ABCB1 et NR3C1 et la réponse aux CS ont été examinées en utilisant la régression logistique assumant différents modèles de la transmission. Les associations multimarqueurs ont été examinées en utilisant l'analyse de haplotypes. Les variantes nongénotypées ont été imputées en utilisant les données de HAPMAP et les associations avec SNPs imputés ont été examinées en utilisant des méthodes standard. Résultats - Parmi 645 patients avec la MC, 364 (56.2%) ont reçu CS. La majorité de patients étaient des hommes (54.9 %); présentaient la maladie de l’iléocôlon (51.7%) ou la maladie inflammatoire (84.6%) au diagnostic et étaient les Caucasiens (95.6 %). Huit pourcents de patients étaient corticorésistants et 40.9% - corticodépendants. Le plus bas âge au diagnostic (OR=1.34, 95% CI: 1.03-3.01, p=0.040), la maladie cœxistante de la région digestive supérieure (OR=1.35, 95% CI: 95% CI: 1.06-3.07, p=0.031) et l’usage simultané des immunomodulateurs (OR=0.35, 95% CI: 0.16-0.75, p=0.007) ont été associés avec la corticodépendance. Un total de 27 marqueurs génotypés à travers de ABCB1 (n=14) et NR3C1 (n=13) ont été en l'Équilibre de Hardy-Weinberg, à l’exception d’un dans le gène NR3C1 (rs258751, exclu). Dans ABCB1, l'allèle rare de rs2032583 (OR=0.56, 95% CI: 0.34-0.95, p=0.029) et génotype hétérozygote (OR=0.52, 95% CI: 0.28-0.95 p=0.035) ont été négativement associes avec la dépendance de CS. Un haplotype à 3 marqueurs, comprenant le SNP fonctionnel rs1045642 a été associé avec la dépendance de CS (p empirique=0.004). 24 SNPs imputés introniques et six haplotypes ont été significativement associés avec la dépendance de CS. Aucune de ces associations n'a cependant maintenu la signification après des corrections pour des comparaisons multiples. Dans NR3C1, trois SNPs: rs10482682 (OR=1.43, 95% CI: 0.99-2.08, p=0.047), rs6196 (OR=0.55, 95% CI: 0.31-0.95, p=0.024), et rs2963155 (OR=0.64, 95% CI: 0.42-0.98, p=0.039), ont été associés sous un modèle additif, tandis que rs4912911 (OR=0.37, 95% CI: 0.13-1.00, p=0.03) et rs2963156 (OR=0.32, 95% CI: 0.07-1.12, p=0.047) - sous un modèle récessif. Deux haplotypes incluant ces 5 SNPs (AAACA et GGGCG) ont été significativement (p=0.006 et 0.01 empiriques) associés avec la corticodépendance. 19 SNPs imputés ont été associés avec la dépendance de CS. Deux haplotypes multimarqueurs (p=0.001), incluant les SNPs génotypés et imputés, ont été associés avec la dépendance de CS. Conclusion - Nos études suggèrent que le fardeau de la corticodépendance est élevé parmi les enfants avec le CD. Les enfants plus jeunes au diagnostic et ceux avec la maladie coexistante de la région supérieure ainsi que ceux avec des variations dans les gènes ABCB1 et NR3C1 étaient plus susceptibles de devenir corticodépendants.

Actuarial applications of multivariate phase-type distributions : model calibration and credibility

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

A Network model of systemic risk : identifying the sources of dependence across institutions

We design a financial network model that explicitly incorporates linkages across institutions through a direct contagion channel, as well as an indirect common exposure channel. In particular, common exposure is setup so as to link the financial to the real sector. The model is calibrated to balance sheet data on the colombian financial sector. Results indicate that commercial banks are the most systemically important financial institutions in the system. Whereas government owned institutions are the most vulnerable institutions in the system.

The price of risk in construction projects: contingency approximation model (CAM)

Little attention has been focussed on a precise definition and evaluation mechanism for project management risk specifically related to contractors. When bidding, contractors traditionally price risks using unsystematic approaches. The high business failure rate our industry records may indicate that the current unsystematic mechanisms contractors use for building up contingencies may be inadequate. The reluctance of some contractors to include a price for risk in their tenders when bidding for work competitively may also not be a useful approach. Here, instead, we first define the meaning of contractor contingency, and then we develop a facile quantitative technique that contractors can use to estimate a price for project risk. This model will help contractors analyse their exposure to project risks; and help them express the risk in monetary terms for management action. When bidding for work, they can decide how to allocate contingencies strategically in a way that balances risk and reward.

Results of an aqueous source term model for a radiological risk assessment of the Drigg LLW Site, U.K.

A radionuclide source term model has been developed which simulates the biogeochemical evolution of the Drigg low level waste (LLW) disposal site. The DRINK (DRIgg Near field Kinetic) model provides data regarding radionuclide concentrations in groundwater over a period of 100,000 years, which are used as input to assessment calculations for a groundwater pathway. The DRINK model also provides input to human intrusion and gaseous assessment calculations through simulation of the solid radionuclide inventory. These calculations are being used to support the Drigg post closure safety case. The DRINK model considers the coupled interaction of the effects of fluid flow, microbiology, corrosion, chemical reaction, sorption and radioactive decay. It represents the first direct use of a mechanistic reaction-transport model in risk assessment calculations.