Forecasting the density of asset returns

In this paper we introduce a transformation of the Edgeworth-Sargan series expansion of the Gaussian distribution, that we call Positive Edgeworth-Sargan (PES). The main advantage of this new density is that it is well defined for all values in the parameter space, as well as it integrates up to one. We include an illustrative empirical application to compare its performance with other distributions, including the Gaussian and the Student's t, to forecast the full density of daily exchange-rate returns by using graphical procedures. Our results show that the proposed function outperforms the other two models for density forecasting, then providing more reliable value-at-risk forecasts.Density forecasting, Edgeworth-Sargan distribution, probability integral transformations, P-value plots, VaR

Effects of music therapy in depression and anxiety disorder

Mental disorders are considerate 'the evil of the century' by renowned researchers, because a large part of the population in many countries is a diagnosticated, and currently affect youth and children. Among the numerous therapeutic interventions, the music therapy is a non-invasive approach. However, there are very few people who are professionally engaged in research and studies on the subject. Therefore, this article is a literature to talk about the benefits of music therapy sessions for people with stress, depression and anxiety disorders

Long memory conditional volatility and asset allocation

Pre-print version dated May 2011 issued as Discussion paper by University of Exeter. A definitive version was subsequently published in International Journal of Forecasting Volume 29, Issue 2, April–June 2013, Pages 258–273. Available online at http://www.sciencedirect.com/In this paper, we evaluate the economic benefits that arise from allowing for long memory when forecasting the covariance matrix of returns over both short and long horizons, using the asset allocation framework of Engle and Colacito (2006) In particular, we compare the statistical and economic performances of four multivariate long memory volatility models (the long memory EWMA, long memory EWMA–DCC, FIGARCH-DCC and component GARCH-DCC models) with those of two short memory models (the short memory EWMA and GARCH-DCC models). We report two main findings. First, for longer horizon forecasts, long memory models generally produce forecasts of the covariance matrix that are statistically more accurate and informative, and economically more useful than those produced by short memory models. Second, the two parsimonious long memory EWMA models outperform the other models–both short and long memory–across most forecast horizons. These results apply to both low and high dimensional covariance matrices and both low and high correlation assets, and are robust to the choice of the estimation window

Forecasting the density of asset returns

In this paper we introduce a transformation of the Edgeworth-Sargan series expansion of the Gaussian distribution, that we call Positive Edgeworth-Sargan (PES). The main advantage of this new density is that it is well defined for all values in the parameter space, as well as it integrates up to one. We include an illustrative empirical application to compare its performance with other distributions, including the Gaussian and the Student’s t, to forecast the full density of daily exchange-rate returns by using graphical procedures. Our results show that the proposed function outperforms the other two models for density forecasting, then providing more reliable value-at-risk forecasts

Higher-order moments in the theory of diversification and portfolio composition

This paper revisits the corner solution in classical portfolio choice theory in which risk-averse agents would all be optimally plungers rather than diversifiers. We examine the effect of higher-order moments of two-, three- and four-parameter density functions on the investor's decision to diversify in an expected utility framework. The empirical analysis provides estimates of four parametric and two semi-nonparametric densities for the S&P500 and concluded that allocation of all wealth in the risky asset would not have been optimal

Analysis of timed automata with guards in dioids algebra

International audienceIn this paper, we propose a new linear representation to model the behavior of Timed Automata with Guards (TAGs) using the formalism of dioids algebra. This linear modeling is used to define the parallel composition and properties of determinism for TAGs. The contribution is illustrated with an example of a jobshop to analyze the performances of this system

Commande tolérante aux fautes des systèmes à événements discrets : comparaison de deux approches sur un cas d'étude

National audienceDans ce papier, deux approches de commande to-lérante aux fautes pour les systèmes à événements discrets sont évaluées : l'approche par masquage de fautes [9] et l'ap-proche par reconguration du contrôle [3]. Elles ont été ap-pliquées à un cas d'étude, an de pouvoir être comparées. Le détail des applications est présenté, ainsi qu'une discussion autour de ces applications et de la capacité de traitement des fautes des deux méthodes. Mots-clés Systèmes à événements discrets, commande tolé-rante aux fautes, reconguration de contrôle, masquage de faute