Design and development of a technological system for grey water reuse

This work has as purpose "contribute to the decrease of the consumption of water drinking to purposes that not so require". Its objectives are to design, develop and transfer a system not conventional for such purpose; to improve the health and conditions of habitability of sanitary spaces with sustainability. To such end, it proposes are: an strategy of "Participatory action research" as "a social practice of knowledge production that seeks social change seen as a totality, occurs in the very action and contributes to it?; a system that allows to replace the traditional toilet tank and reuse and store water used in toilets to be downloaded in nuclei sanitary toilets. With regard to the results achieved, the work has 2 stages that includes: 1. Developing the theoretical framework; the study of history and analysis of geographical areas of application; the generation of possible solutions for responding to the system; the selection of surpassing proposal; 2. adjustment of the surpassing proposal; preparation of technical documentation; design of your building process; adjustment of its operation, use and maintenance; its materialization; experimentation and evaluation. The conclusions was that this system is "Adaptive" and "affordable" that presents facility construction and installation work; features that make it an "adoptable product" in different types of architectural objects and a "sustainable product" because that makes it possible to the care of the environment.Indizada en: Agricultural & Environmental Science Database, CAB Abstracts, Pollution Abstracts, Veterinary Science DatabaseFil: Garzon, Beatriz Silvia. Universidad Nacional de Tucumán. Facultad de Arquitectura y Urbanismo; Argentina. Universidad Nacional de Tucuman. Secretaria de Ciencia, Arte E Innovación Tecnologica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Paterlini, Leonardo. Universidad Nacional de Tucuman. Secretaria de Ciencia, Arte E Innovación Tecnologica; Argentina. Universidad Nacional de Tucumán. Facultad de Arquitectura y Urbanismo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; Argentin

Efficient and robust estimation for financial returns: an approach based on q-entropy

We consider a new robust parametric estimation procedure, which minimizes an empirical version of the Havrda-Charvàt-Tsallis entropy. The resulting estimator adapts according to the discrepancy between the data and the assumed model by tuning a single constant q, which controls the trade-off between robustness and effciency. The method is applied to expected return and volatility estimation of financial asset returns under multivariate normality. Theoretical properties, ease of implementability and empirical results on simulated and financial data make it a valid alternative to classic robust estimators and semi-parametric minimum divergence methods based on kernel smoothing.q-entropy; robust estimation; power-divergence; financial returns

The Maximum Lq-Likelihood Method: an Application to Extreme Quantile Estimation in Finance

Estimating financial risk is a critical issue for banks and insurance companies. Recently, quantile estimation based on Extreme Value Theory (EVT) has found a successful domain of application in such a context, outperforming other approaches. Given a parametric model provided by EVT, a natural approach is Maximum Likelihood estimation. Although the resulting estimator is asymptotically efficient, often the number of observations available to estimate the parameters of the EVT models is too small in order to make the large sample property trustworthy. In this paper, we study a new estimator of the parameters, the Maximum Lq-Likelihood estimator (MLqE), introduced by Ferrari and Yang (2007). We show that the MLqE can outperform the standard MLE, when estimating tail probabilities and quantiles of the Generalized Extreme Value (GEV) and the Generalized Pareto (GP) distributions. First, we assess the relative efficiency between the the MLqE and the MLE for various sample sizes, using Monte Carlo simulations. Second, we analyze the performance of the MLqE for extreme quantile estimation using real-world financial data. The MLqE is characterized by a distortion parameter q and extends the traditional log-likelihood maximization procedure. When q?1, the new estimator approaches the traditionalMaximum Likelihood Estimator (MLE), recovering its desirable asymptotic properties; when q 6=1 and the sample size is moderate or small, the MLqE successfully trades bias for variance, resulting in an overall gain in terms of accuracy (Mean Squared Error).Maximum Likelihood, Extreme Value Theory, q-Entropy, Tail-related Risk Measures

Efficient and robust estimation for financial returns: an approach based on q-entropy

We consider a new robust parametric estimation procedure, which minimizes an empirical version of the Havrda-Charv_at-Tsallis entropy. The resulting estimator adapts according to the discrepancy between the data and the assumed model by tuning a single constant q, which controls the trade-o_ between robustness and e_ciency. The method is applied to expected re- turn and volatility estimation of _nancial asset returns under multivariate normality. Theoretical properties, ease of implementability and empirical re- sults on simulated and _nancial data make it a valid alternative to classic robust estimators and semi-parametric minimum divergence methods based on kernel smoothingq-entropy, robust estimation, power-divergence, _nancial returns

The Maximum Lq-Likelihood Method: an Application to Extreme Quantile Estimation in Finance

Estimating financial risk is a critical issue for banks and insurance companies. Recently, quantile estimation based on Extreme Value Theory (EVT) has found a successful domain of application in such a context, outperforming other approaches. Given a parametric model provided by EVT, a natural approach is Maximum Likelihood estimation. Although the resulting estimator is asymptotically efficient, often the number of observations available to estimate the parameters of the EVT models is too small in order to make the large sample property trustworthy. In this paper, we study a new estimator of the parameters, the Maximum Lq-Likelihood estimator (MLqE), introduced by Ferrari and Yang (2007). We show that the MLqE can outperform the standard MLE, when estimating tail probabilities and quantiles of the Generalized Extreme Value (GEV) and the Generalized Pareto (GP) distributions. First, we assess the relative efficiency between the the MLqE and the MLE for various sample sizes, using Monte Carlo simulations. Second, we analyze the performance of the MLqE for extreme quantile estimation using real-world financial data. The MLqE is characterized by a distortion parameter q and extends the traditional log-likelihood maximization procedure. When q→1, the new estimator approaches the traditionalMaximum Likelihood Estimator (MLE), recovering its desirable asymptotic properties; when q 6=1 and the sample size is moderate or small, the MLqE successfully trades bias for variance, resulting in an overall gain in terms of accuracy (Mean Squared Error).Maximum Likelihood, Extreme Value Theory, q-Entropy, Tail-related Risk Measures

Differential Evolution for Multiobjective Portfolio Optimization

Financial portfolio optimization is a challenging problem. First, the problem is multiobjective (i.e.: minimize risk and maximize profit) and the objective functions are often multimodal and non smooth (e.g.: value at risk). Second, managers have often to face real-world constraints, which are typically non-linear. Hence, conventional optimization techniques, such as quadratic programming, cannot be used. Stochastic search heuristic can be an attractive alternative. In this paper, we propose a new multiobjective algorithm for portfolio optimization: DEMPO - Differential Evolution for Multiobjective Portfolio Optimization. The main advantage of this new algorithm is its generality, i.e., the ability to tackle a portfolio optimization task as it is, without simplifications. Our empirical results show the capability of our approach of obtaining highly accurate results in very reasonable runtime, in comparison with quadratic programming and another state-of-art search heuristic, the so-called NSGA II.Portfolio Optimization, Multiobjective, Real-world Constraints, Value at Risk, Expected Shortfall, Differential Evolution

Operational–risk Dependencies and the Determination of Risk Capital

With the advent of Basel II, risk–capital provisions need to also account for operational risk. The specification of dependence structures and the assessment of their effects on aggregate risk–capital are still open issues in modeling operational risk. In this paper, we investigate the potential consequences of adopting the restrictive Basel’s Loss Distribution Approach (LDA), as compared to strategies that take dependencies explicitly into account. Drawing on a real–world database, we fit alternative dependence structures, using parametric copulas and nonparametric tail–dependence coefficients, and discuss the implications on the estimation of aggregate risk capital. We find that risk–capital estimates may increase relative to that derived for the LDA when accounting explicitly for the presence of dependencies. This phenomenon is not only be due to the (fitted) characteristics of the data, but also arise from the specific Monte Carlo setup in simulation–based risk–capital analysis.Copula, Nonparametric Tail Dependence, Basel II, Loss Distribution Approach, Value–at–Risk, Subadditivity