Central limit theorems for multiple stochastic integrals and Malliavin calculus

We give a new characterization for the convergence in distribution to a standard normal law of a sequence of multiple stochastic integrals of a fixed order with variance one, in terms of the Malliavin derivatives of the sequence. We extend our result to the multidimensional case and prove a weak convergence result for a sequence of square integrable random variables.Comment: 16 page

A pricing measure to explain the risk premium in power markets

In electricity markets, it is sensible to use a two-factor model with mean reversion for spot prices. One of the factors is an Ornstein-Uhlenbeck (OU) process driven by a Brownian motion and accounts for the small variations. The other factor is an OU process driven by a pure jump L\'evy process and models the characteristic spikes observed in such markets. When it comes to pricing, a popular choice of pricing measure is given by the Esscher transform that preserves the probabilistic structure of the driving L\'evy processes, while changing the levels of mean reversion. Using this choice one can generate stochastic risk premiums (in geometric spot models) but with (deterministically) changing sign. In this paper we introduce a pricing change of measure, which is an extension of the Esscher transform. With this new change of measure we also can slow down the speed of mean reversion and generate stochastic risk premiums with stochastic non constant sign, even in arithmetic spot models. In particular, we can generate risk profiles with positive values in the short end of the forward curve and negative values in the long end. Finally, our pricing measure allows us to have a stationary spot dynamics while still having randomly fluctuating forward prices for contracts far from maturity.Comment: 37 pages, 7 figure

Data based predictive control: Application to water distribution networks

In this thesis, the main goal is to propose novel data based predictive controllers to cope with complex industrial infrastructures such as water distribution networks. This sort of systems have several inputs and out- puts, complicate nonlinear dynamics, binary actuators and they are usually perturbed by disturbances and noise and require real-time control implemen- tation. The proposed controllers have to deal successfully with these issues while using the available information, such as past operation data of the process, or system properties as fading dynamics. To this end, the control strategies presented in this work follow a predic- tive control approach. The control action computed by the proposed data- driven strategies are obtained as the solution of an optimization problem that is similar in essence to those used in model predictive control (MPC) based on a cost function that determines the performance to be optimized. In the proposed approach however, the prediction model is substituted by an inference data based strategy, either to identify a model, an unknown control law or estimate the future cost of a given decision. As in MPC, the proposed strategies are based on a receding horizon implementation, which implies that the optimization problems considered have to be solved online. In order to obtain problems that can be solved e ciently, most of the strategies proposed in this thesis are based on direct weight optimization for ease of implementation and computational complexity reasons. Linear convex combination is a simple and strong tool in continuous domain and computational load associated with the constrained optimization problems generated by linear convex combination are relatively soft. This fact makes the proposed data based predictive approaches suitable to be used in real time applications. The proposed approaches selects the most adequate information (similar to the current situation according to output, state, input, disturbances,etc.), in particular, data which is close to the current state or situation of the system. Using local data can be interpreted as an implicit local linearisation of the system every time we solve the model-free data driven optimization problem. This implies that even though, model free data driven approaches presented in this thesis are based on linear theory, they can successfully deal with nonlinear systems because of the implicit information available in the database. Finally, a learning-based approach for robust predictive control design for multi-input multi-output (MIMO) linear systems is also presented, in which the effect of the estimation and measuring errors or the effect of unknown perturbations in large scale complex system is considered

Estilo de inversión en los fondos internacionales del mercado español

Los artículos publicados quedan en propiedad del Instituto Español de Analistas Financieros (IEAF), que administra los derechos de reproducción y copia de los mismo

Student workload estimation to pass a statistics course in Economics and Business Administration

The convergence process in Higher Education in Europe implies a radical change in the teacher’s practice. One of the fundamental questions in the harmonization process stems from the way credits are allocated by the ECTS (European Credit Transfer System). This article analyzes diverse methodologies for the estimation of student workload, with results regarding the time students need to successfully complete the Descriptive Statistics course in Economics and Business Administration. The obtained estimated time is slightly below the 25-30 hours of credit that are usually established as a reference

Severe poverty determinants in Spain using both a monetary and a deprivation approach

Los cambios experimentados por la sociedad española en las últimas décadas pueden haber modificado la distribución de la renta y el perfil de pobreza en nuestro país. El presente trabajo tiene como principal objetivo retomar el análisis de los factores socioeconómicos que determinan las situaciones de pobreza extrema en el nuevo milenio. Utilizando algunos indicadores monetarios tradicionales para la medición de la pobreza, los resultados demuestran que existen ciertos factores que perduran en el tiempo como determinantes de la pobreza extrema, mientras que otras variables han modificado su impacto. Esta visión monetaria de la pobreza se completa con un estudio de la misma desde un punto de vista de privación, comprobándose que, cuando se adopta un enfoque multidimensional, algunos de los factores que determinan la pobreza extrema cambian su efecto.Changes in Spanish society in last decades may have modified the income distribution and the poverty profile in our country. The main aim of this paper is to bring up to date the analysis of socioeconomic factors determining situations of severe poverty in the new millennium. Using some of the traditional monetary indexes for poverty measuring, results show the prevalence in time of several factors as determinants of severe poverty, whereas some other variables have modified their impact. This monetary vision of poverty is completed using a deprivation poverty approach; we may conclude that when a multidimensional approach is adopted, some of the factors determining severe poverty have a different effect