VR BioViewer - A new interactive-visual model to represent medical information

Virtual reality (VR) techniques to understand and obtain conclusions of data in an easy way are being used by the scientific community. However, these techniques are not used frequently for analyzing large amounts of data in life sciences, particularly in genomics, due to the high complexity of data (curse of dimensionality). Nevertheless, new approaches that allow to bring out the real important data characteristics, arise the possibility of constructing VR spaces to visually understand the intrinsic nature of data. It is well known the benefits of representing high dimensional data in tridimensional spaces by means of dimensionality reduction and transformation techniques, complemented with a strong component of interaction methods. Thus, a novel framework, designed for helping to visualize and interact with data about diseases, is presented. In this paper, the framework is applied to the Van't Veer breast cancer dataset is used, while oncologists from La Paz Hospital (Madrid) are interacting with the obtained results. That is to say a first attempt to generate a visually tangible model of breast cancer disease in order to support the experience of oncologists is presented.

Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities

In the current uncertain context that affects both the world economy and the energy sector, with the rapid increase in the prices of oil and gas and the very unstable political situation that affects some of the largest raw materials’ producers, there is a need for developing efficient and powerful quantitative tools that allow to model and forecast fossil fuel prices, CO2 emission allowances prices as well as electricity prices. This will improve decision making for all the agents involved in energy issues. Although there are papers focused on modelling fossil fuel prices, CO2 prices and electricity prices, the literature is scarce on attempts to consider all of them together. This paper focuses on both building a multivariate model for the aforementioned prices and comparing its results with those of univariate ones, in terms of prediction accuracy (univariate and multivariate models are compared for a large span of days, all in the first 4 months in 2011) as well as extracting common features in the volatilities of the prices of all these relevant magnitudes. The common features in volatility are extracted by means of a conditionally heteroskedastic dynamic factor model which allows to solve the curse of dimensionality problem that commonly arises when estimating multivariate GARCH models. Additionally, the common volatility factors obtained are useful for improving the forecasting intervals and have a nice economical interpretation. Besides, the results obtained and methodology proposed can be useful as a starting point for risk management or portfolio optimization under uncertainty in the current context of energy markets.

Analysis of the critical moments in the League ACB 2007-2008

The aim of this paper was to identify the variables that determine winning or losing in the critical moments of the basketball games. A total of forty one critical moments were analyzed, corresponding to 30 games of the regular season from the Spanish Professional League (ACB, season 2007-2008). Games were selected according to the definition of critical moments (described in the available literature), which corresponded to overtime and the last 5 minutes of games with score differences equal or below to six points. The results revealed better values in the winners in defensive rebounds and successful free-throws.

A Valuation of SAREB, the Spanish “Bad Bank”, Upside Using the Real Options Methodology

This paper develops a model to analyze the upside potential of profitability of the SAREB (“Asset Management Company for Assets Arising from Bank Restructuring”), the Spanish “Bad Bank”. The model is based in the Real Options methodology, that is especially adequate due to the convergence of two elements, (i) depreciated assets with a high upside potential, and (ii) a highly volatile market as it has shown to be the real estate Spanish market. Our results suggest that the SAREB has a higher than expected profitability potentialthat would be dedicated to increase the return to its shareholders, mainly private banks. Consequently we also show that after the financial crisis are emerging two types of banks in Spain, in one hand the losers who are transferring their real estate assets at a deep discount, and in the other hand the winners, capturing the upside potential of those assets as shareholders of SAREB, and consequently consolidating their strength in the Spanish Real Estate Industry. It is worth to mention that Governments should make an effort in properly redistribute the wealth generated by the real Estate industry.