Distribución hiperbólica generalizada: una aplicación en la selección de portafolios y en la cuantificación de medidas de riesgo de mercado

En este trabajo se implementa una metodología para incluir momentos de orden superior en la selección de portafolios, haciendo uso de la Distribución Hiperbólica Generalizada, para posteriormente hacer un análisis comparativo frente al modelo de Markowitz.

Measuring and testing for the systemically important financial institutions

This paper analyzes the measure of systemic importance ∆CoV aR proposed by Adrian and Brunnermeier (2009, 2010) within the context of a similar class of risk measures used in the risk management literature. In addition, we develop a series of testing procedures, based on ∆CoV aR, to identify and rank the systemically important institutions. We stress the importance of statistical testing in interpreting the measure of systemic importance. An empirical application illustrates the testing procedures, using equity data for three European banks.

Composição de fundo de fundos multimercado : otimização de carteira pelo método de média-cvar

O objetivo do trabalho é demonstrar que a otimização de uma carteira composta por fundos multimercados brasileiros gera melhores resultados quando a medida de risco utilizada é o Conditional Value-at-Risk. Modelos de otimização de carteira têm como objetivo selecionar ativos que maximizem o retorno do investidor para um determinado nível de risco. Assim, a definição de uma medida apropriada de risco é de fundamental importância para o processo de alocação. A metodologia tradicional de otimização de carteiras, desenvolvida por Markowitz, utiliza como medida de risco a variância dos retornos. Entretanto, a variância é uma medida apenas apropriada para casos em que os retornos são normalmente distribuídos ou em que os investidores possuem funções de utilidade quadrática. Porém, o trabalho mostra que os retornos dos fundos multimercados brasileiros tendem a não apresentar distribuição normal. Logo, para efetuar a otimização de uma carteira composta por fundos multimercados brasileiros é necessário utilizar uma medida de risco alternativa.

Carteiras de renda fixa: imunização, risco de imunização e risco idiossincrático

O termo imunização significa construir uma carteira de títulos de forma a torná-la imune a variações nas taxas de juros. O presente trabalho tem o intuito de avaliar a eficácia das diferentes estratégias de imunização de um portfólio e avaliar seus impactos sobre a rentabilidade dos mesmos. O trabalho aborda medidas de Duração, Convexidade e Dispersão, além de medidas do VaR (value-at-risk) e o CVaR (conditional value-at-risk). O objetivo é analisar de que forma essas medidas de risco podem ser usadas como parâmetros indicadores da carteira ótima e como elas se relacionam entre si. Além disto, este trabalho inova ao introduzir uma análise sobre o risco idiossincrático a que uma carteira pode ficar exposta ao tentar mitigar o risco de imunização. Para avaliar o desempenho das diferentes estratégias de imunização e a existência de um trade off entre risco idiossincrático e risco de imunização foram realizados estudos empíricos de otimização de carteiras com títulos prefixados da dívida interna brasileira no período de 2006 a 2010.

Aplicação da moderna teoria do Portfólio em ativos não financeiros

The Markowitz's objective functions, Value-at-Risk and Conditional Value-at-Risk, are largely used tools in the financial Market for portfolio optimization. This paper tries to analyze these functions having as a target to adapt them for application in non-financial assets portfolios. The paper uses as an example the Electricity Market to analyze and optimize a fictitious investment portfolio of a possible electric power utility. Showing that, besides being possible, which considerations must be taken and which analysis must be made to apply the Modern Portfolio Theory in the non-financial universe

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.

Portfolio-optimization models for small investors

Since 2010, the client base of online-trading service providers has grown significantly. Such companies enable small investors to access the stock market at advantageous rates. Because small investors buy and sell stocks in moderate amounts, they should consider fixed transaction costs, integral transaction units, and dividends when selecting their portfolio. In this paper, we consider the small investor’s problem of investing capital in stocks in a way that maximizes the expected portfolio return and guarantees that the portfolio risk does not exceed a prescribed risk level. Portfolio-optimization models known from the literature are in general designed for institutional investors and do not consider the specific constraints of small investors. We therefore extend four well-known portfolio-optimization models to make them applicable for small investors. We consider one nonlinear model that uses variance as a risk measure and three linear models that use the mean absolute deviation from the portfolio return, the maximum loss, and the conditional value-at-risk as risk measures. We extend all models to consider piecewise-constant transaction costs, integral transaction units, and dividends. In an out-of-sample experiment based on Swiss stock-market data and the cost structure of the online-trading service provider Swissquote, we apply both the basic models and the extended models; the former represent the perspective of an institutional investor, and the latter the perspective of a small investor. The basic models compute portfolios that yield on average a slightly higher return than the portfolios computed with the extended models. However, all generated portfolios yield on average a higher return than the Swiss performance index. There are considerable differences between the four risk measures with respect to the mean realized portfolio return and the standard deviation of the realized portfolio return.

Aplicação de métodos computacionais multidisciplinares de engenharia para otimização de carteiras de investimentos.

Este trabalho apresenta uma nova metodologia para otimizar carteiras de ativos financeiros. A metodologia proposta, baseada em interpoladores universais tais quais as Redes Neurais Artificiais e a Krigagem, permite aproximar a superfície de risco e consequentemente a solução do problema de otimização associado a ela de forma generalizada e aplicável a qualquer medida de risco disponível na literatura. Além disto, a metodologia sugerida permite que sejam relaxadas hipóteses restritivas inerentes às metodologias existentes, simplificando o problema de otimização e permitindo que sejam estimados os erros na aproximação da superfície de risco. Ilustrativamente, aplica-se a metodologia proposta ao problema de composição de carteiras com a Variância (controle), o Valor-em-Risco (VaR) e o Valor-em-Risco Condicional (CVaR) como funções objetivo. Os resultados são comparados àqueles obtidos pelos modelos de Markowitz e Rockafellar, respectivamente.

Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.

External assessment of the Early Mortality Risk Score in patients with adenocarcinoma undergoing pancreaticoduodenectomy.

BACKGROUND: Pancreaticoduodenectomies (PD) still have a substantial mortality rate. Recently, different scores have been published to predict the mortality risk pre-operatively after PD. This retrospective study was designed to perform an external assessment of an Early Mortality Risk Score (EMRS). METHODS: From 2000 to 2012, all PD cases performed at our institution were documented. Only patients treated for pancreatic head adenocarcinomas were included. Survival time and EMRS (based on age, tumour size, tumour differentiation and comorbidities) were calculated for every patient. Relative risks (RR) of early death 9 and 12 months after PD were then calculated. RESULTS: Of 270 PD for various aetiologies, 120 PD for adenocarcinomas were included. The median follow-up was 37 months, and the overall median survival was 19 months. EMRS of 4 showed a mortality RR of 5.1 at 9 months (P = 0.048) and of 4.5 at 12 months (P = 0.020). CONCLUSIONS: EMRS of 4 is a predictor of tumour-related mortality at 9 and 12 months after PD for adenocarcinoma. The EMRS was externally assessed in our patient cohort and can be implemented in clinical practice. Clinical implications of this score still need to be studied.

A value chain approach to animal diseases risk management: technical foundations and practical framework for field application

Classical risk assessment approaches for animal diseases are influenced by the probability of release, exposure and consequences of a hazard affecting a livestock population. Once a pathogen enters into domestic livestock, potential risks of exposure and infection both to animals and people extend through a chain of economic activities related to producing, buying and selling of animals and products. Therefore, in order to understand economic drivers of animal diseases in different ecosystems and to come up with effective and efficient measures to manage disease risks from a country or region, the entire value chain and related markets for animal and product needs to be analysed to come out with practical and cost effective risk management options agreed by actors and players on those value chains. Value chain analysis enriches disease risk assessment providing a framework for interdisciplinary collaboration, which seems to be in increasing demand for problems concerning infectious livestock diseases. The best way to achieve this is to ensure that veterinary epidemiologists and social scientists work together throughout the process at all levels.

The role of myocardial scintigraphy in the assessment of cardiovascular risk in patients with end-stage chronic kidney disease on the waiting list for renal transplantation

The usefulness of stress myocardial perfusion scintigraphy for cardiovascular (CV) risk stratification in chronic kidney disease remains controversial. We tested the hypothesis that different clinical risk profiles influence the test. We assessed the prognostic value of myocardial scintigraphy in 892 consecutive renal transplant candidates classified into four risk groups: very high (aged epsilon 50 years, diabetes and CV disease), high (two factors), intermediate (one factor) and low (no factor). The incidence of CV events and death was 20 and 18, respectively (median follow-up 22 months). Altered stress testing was associated with an increased probability of cardiovascular events only in intermediate-risk (one risk factor) patients [30.3 versus 10, hazard ratio (HR) 2.37, confidence interval (CI) 1.693.33, P 0.0001]. Low-risk patients did well regardless of scan results. In patients with two or three risk factors, an altered stress test did not add to the already increased CV risk. Myocardial scintigraphy was related to overall mortality only in intermediate-risk patients (HR 2.8, CI 1.55.1, P 0.007). CV risk stratification based on myocardial stress testing is useful only in patients with just one risk factor. Screening may avoid unnecessary testing in 60 of patients, help stratifying for risk of events and provide an explanation for the inconsistent performance of myocardial scintigraphy.

Improving risk stratification in non-ST-segment elevation myocardial infarction with combined assessment of GRACE and CRUSADE risk scores

BACKGROUND: Risk assessment is fundamental in the management of acute coronary syndromes (ACS), enabling estimation of prognosis. AIMS: To evaluate whether the combined use of GRACE and CRUSADE risk stratification schemes in patients with myocardial infarction outperforms each of the scores individually in terms of mortality and haemorrhagic risk prediction. METHODS: Observational retrospective single-centre cohort study including 566 consecutive patients admitted for non-ST-segment elevation myocardial infarction. The CRUSADE model increased GRACE discriminatory performance in predicting all-cause mortality, ascertained by Cox regression, demonstrating CRUSADE independent and additive predictive value, which was sustained throughout follow-up. The cohort was divided into four different subgroups: G1 (GRACE<141; CRUSADE<41); G2 (GRACE<141; CRUSADE≥41); G3 (GRACE≥141; CRUSADE<41); G4 (GRACE≥141; CRUSADE≥41). RESULTS: Outcomes and variables estimating clinical severity, such as admission Killip-Kimbal class and left ventricular systolic dysfunction, deteriorated progressively throughout the subgroups (G1 to G4). Survival analysis differentiated three risk strata (G1, lowest risk; G2 and G3, intermediate risk; G4, highest risk). The GRACE+CRUSADE model revealed higher prognostic performance (area under the curve [AUC] 0.76) than GRACE alone (AUC 0.70) for mortality prediction, further confirmed by the integrated discrimination improvement index. Moreover, GRACE+CRUSADE combined risk assessment seemed to be valuable in delineating bleeding risk in this setting, identifying G4 as a very high-risk subgroup (hazard ratio 3.5; P<0.001). CONCLUSIONS: Combined risk stratification with GRACE and CRUSADE scores can improve the individual discriminatory power of GRACE and CRUSADE models in the prediction of all-cause mortality and bleeding. This combined assessment is a practical approach that is potentially advantageous in treatment decision-making.

The Value Of The 2005 International Society Of Urological Pathology (isup) Modified Gleason Grading System As A Predictor Of Biochemical Recurrence After Radical Prostatectomy.

To compare time and risk to biochemical recurrence (BR) after radical prostatectomy of two chronologically different groups of patients using the standard and the modified Gleason system (MGS). Cohort 1 comprised biopsies of 197 patients graded according to the standard Gleason system (SGS) in the period 1997/2004, and cohort 2, 176 biopsies graded according to the modified system in the period 2005/2011. Time to BR was analyzed with the Kaplan-Meier product-limit analysis and prediction of shorter time to recurrence using univariate and multivariate Cox proportional hazards model. Patients in cohort 2 reflected time-related changes: striking increase in clinical stage T1c, systematic use of extended biopsies, and lower percentage of total length of cancer in millimeter in all cores. The MGS used in cohort 2 showed fewer biopsies with Gleason score ≤ 6 and more biopsies of the intermediate Gleason score 7. Time to BR using the Kaplan-Meier curves showed statistical significance using the MGS in cohort 2, but not the SGS in cohort 1. Only the MGS predicted shorter time to BR on univariate analysis and on multivariate analysis was an independent predictor. The results favor that the 2005 International Society of Urological Pathology modified system is a refinement of the Gleason grading and valuable for contemporary clinical practice.

Validation of a strategy to diagnose coronary artery disease and predict cardiac events in high-risk renal transplant candidates

Background We validated a strategy for diagnosis of coronary artery disease ( CAD) and prediction of cardiac events in high-risk renal transplant candidates ( at least one of the following: age >= 50 years, diabetes, cardiovascular disease). Methods A diagnosis and risk assessment strategy was used in 228 renal transplant candidates to validate an algorithm. Patients underwent dipyridamole myocardial stress testing and coronary angiography and were followed up until death, renal transplantation, or cardiac events. Results The prevalence of CAD was 47%. Stress testing did not detect significant CAD in 1/3 of patients. The sensitivity, specificity, and positive and negative predictive values of the stress test for detecting CAD were 70, 74, 69, and 71%, respectively. CAD, defined by angiography, was associated with increased probability of cardiac events [log-rank: 0.001; hazard ratio: 1.90, 95% confidence interval (CI): 1.29-2.92]. Diabetes (P=0.03; hazard ratio: 1.58, 95% CI: 1.06-2.45) and angiographically defined CAD (P=0.03; hazard ratio: 1.69, 95% CI: 1.08-2.78) were the independent predictors of events. Conclusion The results validate our observations in a smaller number of high-risk transplant candidates and indicate that stress testing is not appropriate for the diagnosis of CAD or prediction of cardiac events in this group of patients. Coronary angiography was correlated with events but, because less than 50% of patients had significant disease, it seems premature to recommend the test to all high-risk renal transplant candidates. The results suggest that angiography is necessary in many high-risk renal transplant candidates and that better noninvasive methods are still lacking to identify with precision patients who will benefit from invasive procedures. Coron Artery Dis 21: 164-167 (C) 2010 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkins.