An appropriate calibration and forecasting of volatility and market risk are some of the main challenges faced by companies that have to manage the uncertainty inherent to their investments or funding operations such as banks, pension funds or insurance companies. This has become even more evident after the 2007-2008 Financial Crisis, when the forecasting models assessing the market risk and volatility failed. Since then, a significant number of theoretical developments and methodologies have appeared to improve the accuracy of the volatility forecasts and market risk assessments. Following this line of thinking, this paper introduces a model based on using a set of Machine Learning techniques, such as Gradient Descent Boosting, Random Forest, Support Vector Machine and Artificial Neural Network, where those algorithms are stacked to predict S&P500 volatility. The results suggest that our construction outperforms other habitual models on the ability to forecast the level of volatility, leading to a more accurate assessment of the market risk.

Events such as the Financial Crisis of 2007–2008 or the COVID-19 pandemic caused significant losses to banks and insurance entities. They also demonstrated the importance of using accurate equity risk models and having a risk management function able to implement effective hedging strategies. Stock volatility forecasts play a key role in the estimation of equity risk and, thus, in the management actions carried out by financial institutions. Therefore, this paper has the aim of proposing more accurate stock volatility models based on novel machine and deep learning techniques. This paper introduces a neural network-based architecture, called Multi-Transformer. Multi-Transformer is a variant of Transformer models, which have already been successfully applied in the field of natural language processing. Indeed, this paper also adapts traditional Transformer layers in order to be used in volatility forecasting models. The empirical results obtained in this paper suggest that the hybrid models based on Multi-Transformer and Transformer layers are more accurate and, hence, they lead to more appropriate risk measures than other autoregressive algorithms or hybrid models based on feed forward layers or long short term memory cells.

Persistent organic pollutants (POPs) are among the most relevant and dangerous contaminants in soil, from where they can be transferred to crops. Additionally, livestock animals may inadvertently consume relatively high amounts of soil attached to the roots of the vegetables while grazing, leading to indirect exposure to humans. Therefore, periodic monitoring of soils is crucial; thus, simple, robust, and powerful methods are needed. In this study, we have tested and validated an easy QuEChERS-based method for the extraction of 49 POPs (8 PBDEs, 12 OCPs, 11 PAHs, and 18 PCBs) in soils and their analysis by GC-MS/MS. The method was validated in terms of linearity, precision, and accuracy, and a matrix effect study was performed. The limits of detection (LOD) were established between 0.048 and 3.125 ng g−1 and the limits of quantification (LOQ) were between 0.5 and 20 ng g−1, except for naphthalene (50 ng g−1). Then, to verify the applicability of the validated method, we applied it to a series of 81 soil samples from farms dedicated to mixed vegetable cultivation and vineyards in the Canary Islands, both from two modes of production (organic vs. conventional) where residues of OCPs, PCBs, and PAHs were found.

With the same personal conditions, the system used influences both the degree of severity of dependence and the possibility to become eligible to public funds. The Spanish system is the most generous and the French system is the most restrictive one, the latter also imposes limitations on age.

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