Inhomogeneous Dependency Modelling with Time Varying Copulae

Measuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of applications, though, requires a modelling framework different from the multivariate normal. In risk management the non-normal behaviour of most financial time series calls for nonlinear (i.e. non-gaussian) dependency. The correct modelling of non-gaussian dependencies is therefore a key issue in the analysis of multivariate time series. In this paper we use copulae functions with adaptively estimated time varying parameters for modelling the distribution of returns, free from the usual normality assumptions. Further, we apply copulae to estimation of Value-at-Risk (VaR) of a portfolio and show its better performance over the RiskMetrics approach, a widely used methodology for VaR estimation.Value-at-Risk, time varying copula, adaptive estimation, nonparametric estimation.

Risk Assessment of Transitional Economies by Multivariate and Multicriteria Approaches

This article assesses country-risk of sixteen Central, Baltic and South-East European transition countries, for 2005 and 2007, using multivariate cluster analysis. It was aided by the appropriate ANOVA (analysis of variance) testing and the multicriteria PROMETHEE method. The combination of methods makes for more accurate and efficient country-risk assessment.Country risk classifications and ratings involve evaluating the performance of countries while considering their economic and socio-political characteristics. The purpose of the article is to classify, and then find the comparative position of each individual country in the group of analyzed countries, in order to find out to which extent development of market economy and democratic society has been achieved.Country-risk, Transition countries, Multivariate cluster analysis, PROMETHEE method.

Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange

Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version

Investors\u27 Asset Allocations versus Life-Cycle Funds

Life-cycle funds, among the newest asset allocation fund offerings, are managed according to investors\u27 time horizons and risk tolerances. Partly in response to the appearance of these funds, we examined the relationships among the risk in individual investors\u27 portfolios, their financial-planning time horizons, and their risk tolerances. Generally, we found that portfolio risk increases as time horizon and willingness to take risk increase. This relationship held when we used willingness to take risk increase. This relationship held when we used multivariate analysis. Additional factors related to portfolio risk were found to be the investors\u27 expectations of a future economic downtown, age, education, and marital status

Hepatic resection for metastatic colorectal adenocarcinoma: A proposal of a prognostic scoring system

Background: Hepatic resection for metastatic colorectal cancer provides excellent longterm results in a substantial proportion of patients. Although various prognostic risk factors have been identified, there has been no dependable staging or prognostic scoring system for metastatic hepatic tumors. Study Design: Various clinical and pathologic risk factors were examined in 305 consecutive patients who underwent primary hepatic resections for metastatic colorectal cancer. Survival rates were estimated by the Cox proportional hazards model using the equation: S(t) = [S(o)(t)](exp(R - R(o))), where S(o)(t) is the survival rate of patients with none of the identified risk factors and R(o) = 0. Results: Preliminary multivariate analysis revealed that independently significant negative prognosticators were: (1) positive surgical margins, (2) extrahepatic tumor involvement including the lymph node(s), (3) tumor number of three or more, (4) bilobar tumors, and (5) time from treatment of the primary tumor to hepatic recurrence of 30 months or less. Because the survival rates of the 62 patients with positive margins or extrahepatic tumor were uniformly very poor, multivariate analysis was repeated in the remaining 243 patients who did not have these lethal risk factors. The reanalysis revealed that independently significant poor prognosticators were: (1) tumor number of three or more, (2) tumor size greater than 8 cm, (3) time to hepatic recurrence of 30 months or less, and (4) bilobar tumors. Risk scores (R) for tumor recurrence of the culled cohort (n = 243) were calculated by summation of coefficients from the multivariate analysis and were divided into five groups: grade 1, no risk factors (R = 0); grade 2, one risk factor (R = 0.3 to 0.7); grade 3, two risk factors (R = 0.7 to 1.1); grade 4, three risk factors (R = 1.2 to 1.6); and grade 5, four risk factors (R > 1.6). Grade 6 consisted of the 62 culled patients with positive margins or extrahepatic tumor. Kaplan-Meier and Cox proportional hazards estimated 5-year survival rates of grade 1 to 6 patients were 48.3% and 48.3%, 36.6% and 33.7%, 19.9% and 17.9%, 11.9% and 6.4%, 0% and 1.1%, and 0% and 0%, respectively (p < 0.0001). Conclusions: The proposed risk-score grading predicted the survival differences extremely well. Estimated survival as determined by the Cox proportional hazards model was similar to that determined by the Kaplan-Meier method. Verification and further improvements of the proposed system are awaited by other centers or international collaborative studies

Sepsis caused by bloodstream infection in patients in the intensive care unit: the impact of inactive empiric antimicrobial therapy on outcome

Background: Sepsis is one of the leading causes of death in the UK. Aims: The aims of this study were to identify the rate of inactive antimicrobial therapy (AMT) in the ICU and whether inactive AMT had an effect on in hospital mortality, ICU mortality, 90-day mortality and length of hospital stay. Additionally, we wanted to identify risk factors for receiving inactive AMT. Methods: This was a retrospective observational study conducted at Glasgow Royal Infirmary ICU between January 2010 and December 2013, with 12,000 blood cultures taken over this time period, of which n=127 were deemed clinically significant. Multivariate logistic regression was used to identify risk factors independently associated with mortality. To identify risk factors for receiving inactive AMT a univariable and a subsequent multivariate analysis was constructed. Results: The rate of inactive AMT was 47% (n =60). Our multivariate analysis showed that receiving antibiotics within the first 24 hours of ICU admission led to a reduced mortality (RR 1.70; 95% CI 1.19-2.44.) Furthermore, it showed that severity of illness (as defined by SIRS criteria sepsis vs septic shock) increased mortality (OR 9.87; 95% CI 1.73-55.5). However, inactive AMT did not increase mortality (OR 1.07; 95% CI 0.47-2.41) or length of hospital stay (53.2 vs 69.1 days p=0.348.) We identified fungal bloodstream infection as a risk factor for receiving inactive AMT (OR 5.10;95% CI 1.29-20.14. Conclusion: Mortality from sepsis is influenced by multiple factors. We were unable to demonstrates that inactive AMT had an effect on mortality in sepsis

The Correlation of Dyslipidemia with the Extent of Coronary Artery Disease in the Multiethnic Study of Atherosclerosis.

BackgroundThe extent of coronary artery calcium (CAC) improves cardiovascular disease (CVD) risk prediction. The association between common dyslipidemias (combined hyperlipidemia, simple hypercholesterolemia, metabolic Syndrome (MetS), isolated low high-density lipoprotein cholesterol, and isolated hypertriglyceridemia) compared with normolipidemia and the risk of multivessel CAC is underinvestigated.ObjectivesTo determine whether there is an association between common dyslipidemias compared with normolipidemia, and the extent of coronary artery involvement among MESA participants who were free of clinical cardiovascular disease at baseline.MethodsIn a cross-sectional analysis, 4,917 MESA participants were classified into six groups defined by specific LDL-c, HDL-c, or triglyceride cutoff points. Multivessel CAC was defined as involvement of at least 2 coronary arteries. Multivariate Poisson regression analysis evaluated the association of each group with multivessel CAC after adjusting for CVD risk factors.ResultsUnadjusted analysis showed that all groups except hypertriglyceridemia had statistically significant prevalence ratios of having multivessel CAC as compared to the normolipidemia group. The same groups maintained statistical significance prevalence ratios with multivariate analysis adjusting for other risk factors including Agatston CAC score [combined hyperlipidemia 1.41 (1.06-1.87), hypercholesterolemia 1.55 (1.26-1.92), MetS 1.28 (1.09-1.51), and low HDL-c 1.20 (1.02-1.40)].ConclusionCombined hyperlipidemia, simple hypercholesterolemia, MetS, and low HDL-c were associated with multivessel coronary artery disease independent of CVD risk factors and CAC score. These findings may lay the groundwork for further analysis of the underlying mechanisms in the observed relationship, as well as for the development of clinical strategies for primary prevention

PARAMETRIC MODELING AND SIMULATION OF JOINT PRICE-PRODUCTION DISTRIBUTIONS UNDER NON-NORMALITY, AUTOCORRELATION AND HETEROSCEDASTICITY: A TOOL FOR ASSESSING RISK IN AGRICULTURE

This study presents a way to parametrically model and simulate multivariate distributions under potential non-normality, autocorrelation and heteroscedasticity and illustrates its application to agricultural risk analysis. Specifically, the joint probability distribution (pdf) for West Texas irrigated cotton, corn, sorghum, and wheat production and prices is estimated and applied to evaluate the changes in the risk and returns of agricultural production in the region resulting from observed and predicted price and production trends. The estimated pdf allows for time trends on the mean and the variance and varying degrees of autocorrelation and non-normality (kurtosis and right- or left-skewness) in each of the price and production variables. It also allows for any possible price-price, production-production, or price-production correlation.agricultural risk analysis, autocorrelation, heteroscedasticity, multivariate non-normal simulation, West Texas agriculture, Research Methods/ Statistical Methods, Risk and Uncertainty,

Bayesian Value-at-Risk for a Portfolio: Multi- and Univariate Approaches Using MSF-SBEKK Models

The s-period ahead Value-at-Risk (VaR) for a portfolio of dimension n is considered and its Bayesian analysis is discussed. The VaR assessment can be based either on the n-variate predictive distribution of future returns on individual assets, or on the univariate Bayesian model for the portfolio value (or the return on portfolio). In both cases Bayesian VaR takes into account parameter uncertainty and non-linear relationship between ordinary and logarithmic returns. In the case of a large portfolio, the applicability of the n-variate approach to Bayesian VaR depends on the form of the statistical model for asset prices. We use the n-variate type I MSF-SBEKK(1,1) volatility model proposed specially to cope with large n. We compare empirical results obtained using this multivariate approach and the much simpler univariate approach based on modelling volatility of the value of a given portfolio.Bayesian econometrics, risk analysis, multivariate GARCH processes, multivariate SV processes, hybrid SV-GARCH models