Testing co-volatility spillovers for natural gas spot, futures and ETF spot using dynamic conditional covariances

There is substantial empirical evidence that energy and financial markets are closely connected. As one of the most widely-used energy resources worldwide, natural gas has a large daily trading volume. In order to hedge the risk of natural gas spot markets, a large number of hedging strategies can be used, especially with the rapid development of natural gas derivatives markets. These hedging instruments include natural gas futures and options, as well as Exchange Traded Fund (ETF) prices that are related to natural gas stock prices. The volatility spillover effect is the delayed effect of a returns shock in one physical, biological or financial asset on the subsequent volatility or co-volatility of another physical, biological or financial asset. Investigating volatility spillovers within and across energy and financial markets is a crucial aspect of constructing optimal dynamic hedging strategies. The paper tests and calculates spillover effects among natural gas spot, futures and ETF markets using the multivariate conditional volatility diagonal BEKK model. The data used include natural gas spot and futures returns data from two major international natural gas derivatives markets, namely NYMEX (USA) and ICE (UK), as well as ETF data of natural gas companies from the stock markets in the USA and UK. The empirical results show that there are significant spillover effects in natural gas spot, futures and ETF markets for both USA and UK. Such a result suggests that both natural gas futures and ETF products within and beyond the country might be considered when constructing optimal dynamic hedging strategies for natural gas spot prices.

Development of data reduction pipelines for GTC instruments at the UCM

The UCM Instrumentation Group (GUAIX) is developing currently Data Reduction Pipelines (DRP) for four instruments of the GTC: EMIR, FRIDA, MEGARA and MIRADAS. The purpose of the DRPs is to provide astronomers scientific quality data, removing instrumental biases, calibrating the images in physical units and providing a estimation of the associated uncertainties.

Evaluación del rendimiento de un planificador de tareas sobre una plataforma heterogénea ARM+DSP de Texas Instruments

El presente trabajo estudia la viabilidad a la hora de aplicar un modelo de programación basado en la extracción de paralelismo a nivel de tareas sobre distintas arquitecturas heterogéneas basadas en un procesador multinúcleo de propósito general acelerado con uno o más aceleradores hardware. Se ha implementado una aplicación completa cuyo objetivo es la detección de bordes en una imagen (implementando el Algoritmo de Canny), y se ha evaluado en detalle su rendimiento sobre distintos tipos de arquitecturas, incluyendo CPUs multinúcleo de última generación, sistemas multi-GPU y una arquitectura objetivo basada en procesadores ARM Cortex-A15 acelerados mediante un DSP C66x de la compañía Texas Instruments. Los resultados experimentales demuestran la viabilidad de este tipo de implementación también para arquitecturas heterogéneas novedosas como esta última, e ilustran la facilidad de programación que introduce este tipo de modelos de programación sobre arquitecturas de propósito específico.