Four essays on quantitative economics applications to volatility analysis in Emerging Markets and renewable energy projects

[ES]Las decisiones financieras se pueden dividir en decisiones de inversión y decisiones de financiación. En lo que respecta a las decisiones de inversión, la incertidumbre acerca de la dinámica futura de las variables económicas y de las financieras tiene un rol fundamental. Eso, se explica porque los retornos esperados por las empresas y por los inversionistas se pueden ver afectados por los movimientos adversos en los mercados financieros y por los altos niveles de volatilidad. Como consecuencia, resulta crucial realizar un adecuado análisis y modelación de la volatilidad para el proceso de toma de decisiones financieras, por parte de las empresas y el diseño de estrategias de inversión y cobertura por parte de los inversionistas. En este sentido, el estudio de la volatilidad se ha convertido en uno de los temas más interesantes de la investigación en finanzas. Lo anterior ha cobrado mayor relevancia en los últimos años, teniendo en cuenta el escenario de alta volatilidad e incertidumbre que afrontan los mercados a nivel global. Este documento tiene como objetivo abordar cuatro cuestiones centrales, las cuales están relacionadas con la volatilidad financiera como campo de investigación. Esas cuestiones son, la transmisión y spillovers de volatilidad en mercados emergentes, la calibración de la superficie de volatilidad para proyectos de energía renovable y el pronóstico de los rendimientos de activos energéticos y spillovers de volatilidad a través de técnicas de machine learning. En el primer capítulo del documento, se examinan los efectos de transmisión de volatilidad entre un índice de energía y un índice financiero para los Mercados Emergentes. En consecuencia, mediante el uso de un modelo DCC, se muestra que los efectos de transmisión de volatilidad entre los índices empleados para la crisis subprime y la crisis del COVID-19 fueron diferentes. Lo anteriormente dicho, considerando que la primera crisis se originó en el sector financiero y luego se extendió al resto de la economía, mientras que la segunda se originó en el sector real y posteriormente afectó al resto de la economía. Teniendo en cuenta que la relación entre la volatilidad de los mercados es cambiante en el tiempo, en el segundo capítulo se llevó a cabo un análisis dinámico de los spillovers de volatilidad entre materias primas, Bitcoin y un índice de Mercados Emergentes. Así, empleando la metodología propuesta por Diebold y Yilmaz (2012), se concluyó que los efectos de los spillovers de volatilidad entre los activos analizados no son constantes en dirección e intensidad a través del tiempo. En particular, para períodos de crisis como el de la pandemia del COVID-19, hay reversiones en la dirección de los spillovers de volatilidad debido al sector en el que se originó la crisis. Además, en este capítulo se explota la naturaleza dinámica de los spillovers de volatilidad. Por lo tanto, se planteó que el índice de spillovers de volatilidad propuesto por Diebold y Yilmaz puede ser usado como una medida para pronosticar periodos de alta turbulencia. Lo anterior se desarrolló a través de modelos econométricos tradicionales y de técnicas de machine learning. En el tercer capítulo del documento, se propone un modelo que predice los retornos de los precios del carbono y del petróleo. En este sentido, se desarrolló un modelo híbrido, el cual combina las proyecciones obtenidas a partir de diferentes técnicas de machine learning y modelos econométricos tradicionales, obteniéndose resultados los cuales muestran las ventajas de emplear modelos híbridos que incorporan técnicas de machine learning, exclusivamente, para pronosticar variables financieras. Finalmente, en el capítulo cuatro, se presenta una metodología para la estimación de la volatilidad en la valoración de proyectos de energías renovables mediante opciones reales. En esta metodología, la cual es una extensión del enfoque de volatilidad implícita empleada para las opciones financieras, la volatilidad de un proyecto es la volatilidad implícita obtenida a partir de la superficie de la volatilidad de empresas comparables, según una determinada fecha de valoración y dada la relación deuda-capital de un proyecto de energía renovable. En este análisis, se utilizó el modelo estocástico 'alfa-beta-rho' para calibrar la superficie de la volatilidad para la valoración mediante opciones reales. Por último, al final del documento se presentan las conclusiones derivadas de los capítulos mencionados, así como algunas recomendaciones para las futuras investigaciones. [EN]Financial decisions can be divided in investment and financing decisions. Concerning investment decisions, the uncertainty about the future dynamics of financial and economic variables has a central role, considering that the returns expected by firms and investors can be affected by the adverse movements in financial markets and their high volatility. In consequence, the adequate volatility analysis and modeling is crucial for the firm’s financial decision-making process and the design of investing and hedging strategies by investors. In this regard, the study of volatility has become one of the most interesting topics in finance research. The foregoing has become more relevant in recent years considering the scenario of high volatility and uncertainty faced by markets globally. This document aims to address four central issues related to financial volatility as a research area. These are, volatility transmission and spillovers in Emerging Markets, the calibration of the volatility surface for renewable energy projects and the forecast of energy assets returns and volatility spillovers through machine learning techniques. In the first chapter of the document, the volatility transmission effects between an energy index and a financial index for Emerging Markets are examined. Then, by using a DCC model, it is shown that the volatility transmission effects between the employed indices for the subprime crisis and the COVID-19 pandemic were different. This, considering that the former crisis originated in the financial sector and spread to the rest of the economy, while the second originated in the real sector and trasmitted to the rest of the economy posteriorly. Considering that the relationship between markets volatility is time-varying, in the second chapter, a dynamic analysis of volatility spillovers between commodities, Bitcoin and an Emerging Markets index is developed. Employing the methodology proposed by Diebold and Yilmaz (2012), it is concluded that the volatility spillovers effects between the analyzed assets is not constant in direction and intensity over time. In particular, for periods of crisis such as the COVID-19 pandemics, there are reversals in the direction of volatility spillovers due to the sector in which the crises originate. In addition, in this chapter the dynamic nature of volatility spillovers is exploited. Hence, the volatility spillover index proposed by Diebold and Yilmaz is forecasted to be used as a measure to anticipate high turbulence periods. This, through both traditional econometric models and machine learning techniques. In the third chapter, a model for the prediction of carbon and oil prices is proposed. In this sense, a hybrid model that ensembles the forecasts obtained from different machine learning techniques and traditional econometric models is developed, obtaining results that show the advantages of employing hybrid models which combine machine learning techniques, exclusively, to forecast financial variables. In Chapter four, a methodology for the estimation of volatility in renewable energy projects valuation through real options is presented. In this methodology, which is an extension of the implied volatility approach employed for financial options, the volatility of the project is the implied volatility obtained from the volatility surface of comparable firms for a certain valuation date and given debt-to-equity relation of a renewable energy project. In this analysis, the stochastic ‘alpha-beta-rho’ model is utilized to calibrate the volatility surface for real option valuation purposes. Finally, the conclusions derived from the mentioned chapters are presented at the end of the document as well as some recommendations for future research

Brief considerations on business valuation methods

Nowadays, determining the value of a business has gained significant importance in academic and business fields, as the understanding of the value of an organization has become a key tool for the management and marketing of a business. Accordingly, numerous methods have been developed in order to perform these kinds of practices. In this article, the most used and current methods in processes of business evaluation are revised, observing their strengths and weaknesses, with the aim of comparing them and determining that the discounted cash flow methods are the most adequate procedures to perform this type of analysis

Analysis of a sensor fusion hybrid solution for indoor/outdoor robot navigation

Proceedings of: 5th ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC 2010). Noordwij, Netherlands, 8-10 December 2010Autonomous mobile robots need robust, flexible and accurate navigation algorithms. One approach consists in fusing as many information sources as possible, integrating measures from internal sensors with data obtained from external sensing entities. This work presents a solution for combined indoor/outdoor robot navigation, and analyzes some preliminary results in an outdoor environment using a Particle Filter for GPS/INS sensor fusion. Experiments are based in predesigned trajectories which have been simulated in first place and then reproduced using a robotic platform. As a concluding remark, some considerations about the use of Particle Filters and the differences between simulated and real data are presentedThis work was supported in part by Projects ATLANTIDA, CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008- 06732-C02-02/TEC, SINPROB, CAM MADRINET S- 0505/TIC/0255 DPS2008-07029-C02-02.Publicad

Neighborhood-based Regularization of Proposal Distribution for Improving Resampling Quality in Particle Filters

Proceedings of: 14th International Conference on Information Fusion (FUSION 2011). Chicago, Illinois, USA 5-8 July 2011Particle Filter is a sequential Montecarlo algorithm extensively used for solving estimation problems with non-linear and non-Gaussian features. In spite of its relative simplicity, it is known to suffer some undesired effects that can spoil its performance. Among these problems we can account the one known as sample depletion. This paper reviews the different causes of sample depletion and the many solutions proposed in the existing literature. It also introduces a new strategy for particle resampling which relies in a local linearization of the proposal distribution. The particles drawn using the proposed method are not affected by sample impoverishment and can indirectly lead to better results thanks to a reduction in the plant noise employed, as well to increased performance because of requiring a lower number of particles to achieve same results.Publicad

Categorización de errores en la estimación de cantidades de longitud y superficie

En una investigación de diseño en curso sobre estimación de cantidades continuas (longitud y superficie), se han detectado importantes deficiencias en la capacidad estimativa de alumnos de 3º de E.S.O. Se ha elaborado una categorización de los errores cometidos en la realización de tareas estimativas que permite analizar el efecto, del proceso de enseñanza implementado en la capacidad estimativa de los alumnos, y la vinculación del tipo de error con la magnitud a estimar

Estudio sobre la estimación de cantidades continuas: longitud y superficie

En un estudio previo sobre estimación de cantidades continuas (Castillo, 2006), en el caso de las magnitudes longitud, superficie, capacidad y masa se detectaron importantes deficiencias en la capacidad estimativa de los alumnos de secundaria. Estos nos han conducido a realizar una investigación de diseño dirigida a analizar cómo un grupo de alumnos de 3º de E.S.O. desarrolla su capacidad de estimación de las magnitudes longitud y superficie, a lo largo de un proceso de enseñanza en el que atendemos a las diferentes componentes de la estimación. En esta comunicación describimos la estructura de dicho estudio y presentamos los primeros resultados

Comparación de marcos de trabajo de Aprendizaje Profundo para la detección de objetos

Muchas aplicaciones en visión por computador necesitan de sistemas de detección precisos y eficientes. Esta demanda coincide con el auge de la aplicación de técnicas de aprendizaje profundo en casi todos las áreas del aprendizaje máquina y la visión artificial. Este trabajo presenta un estudio que engloba diferentes sistemas de detección basados en aprendizaje profundo proporcionando una comparativa unificada entre distintos marcos de trabajo con el objetivo de realizar una comparación técnica de las medidas de rendimiento de los métodos estudiados.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Mobile Application as a legal and informative support tool for the active population of Ecuador

The purpose of this study was to design a mobile application as legal and informative resource aimed at an Active Population—employed and in search of employment—in Ecuador. A quantitative research method was used with the Feasible Project, which complement with the “Development of Mobile Apps. First, the model was obtained through the National Statistics and Census Department (INEC, 2018) and by the Employment, Unemployment and Underemployment National Survey— (ENEMDU, 2019). Second, data was gathered through a documental technique—a method consisting of empirical levels. In addition, data sheets were used as tools, while the Content Analysis enabled data interpretation. The technical diagnosis revealed that the Active Population faces unemployment and underemployment serious issues affecting the country’s productive stability in addition to decreasing the likeliness of promotion in the latter of opportunities in society. A mobile app has been designed as a tool containing legal and informative content to enable access to rules and regulations governing labor and information of productive processes. In order to accomplish the making of this app, agile methods, SCRUM work frame and a CMMI model. Furthermore, analyses requirement, conceptualization and assessment prototypes were established

Teaching Using Collaborative Research Projects: Experiences with Adult Learners in Distance Education

[EN] This research studies the acquisition and improvement of specific cognitive, functional, and social competencies of the students enrolled in a university module in which we applied Collaborative Research Project (CRP) strategy. The module was Research Methodology for a master’s degree in research in electrical engineering, electronics and industrial control given at the National Distance Education University (UNED) in Spain. This practice was applied to a research project in which the private sector was interested in. We have been aiming at increasing academia–industry interaction while promoting active learning; both are principles advocated by the European Higher Education Area (EHEA). Having applied this strategy, the module learning outcomes were evaluated following the guideline standards set by the National Agency for Quality Assessment of Universities (ANECA) of the Spanish Government. The results from this evaluation indicated that CRP, even when carried out by using distance learning, has encouraged the students’ interest in both research and the module. It has also fostered collaboration between students and lecturers while increasing their degree of satisfaction. We highlight the difficulties in merging all the outcomes from the students’ research as the main drawback