Clustering of exchange rates and their dynamics under different dependence measures

This paper proposes an improvement to the method for clustering exchange rates given by D. J. Fenn et al, in Quantitative Finance, 12 (10) 2012, pp.1493-1520. To deal with the potentially non linear nature of currency time series dependence, we propose two alternative similarity metrics to use instead of the one used in the aforementioned paper based on Pearson correlation. Our proposed similarity metrics are based upon Kendall and distance correlations. We observe how each of the newly adapted clustering methods respond over several years of currency exchange data and find significant differences in the resulting clusters.Peer ReviewedPostprint (published version

Power to invest

In this post recession time it is important to measure the possibilities offered by a society in relation to investments. To do that, we consider an investment schema I= (R;R_1,...,R_n) where R is a lower bound on the desired return and the R_i's are the return of the assets (to invest in). We introducePeer ReviewedPostprint (author's final draft

Detecting clusters and their dynamics in the Forex Market

This project studies and implements the clustering methods introduced by Fenn et al. to detect correlations in the foreign exchange market. To deal with the potentially non linear nature of currency time series dependance, we propose two alternative similarity metrics to use instead of the Pearson linear correlation. We observe how each of them responds over several years of currency exchange data and find significant differences in the resulting clusters

Uncertainty in basic short-term macroeconomic models with angel-daemon games

We propose the use of an angel-daemon framework to perform an uncertainty analysis of short-term macroeconomic models. The angel-daemon framework defines a strategic game where two agents, the angel and the daemon, act selfishly. These games are defined over an uncertainty profile which presents a short and macroscopic description of a perturbed situation. The Nash equilibria on these games provide stable strategies in perturbed situations, giving a natural estimation of uncertainty. We apply the framework to the uncertainty analysis of linear versions of the IS-LM and the IS-MP models.Peer ReviewedPostprint (author's final draft

An excursion into differential machine learning and applications to finance

El paper "Differential Machine Learning", publicat el 2020 per Antoine Savine i Brian Huge obre una nova porta al càlcul de derivats financers a través d'un disseny d'una nova Xarxa Neuronal, nucli del Deep Learning, creant un vincle entreaquests camps. Aquesta tesi preten donar una comprensió matemàtica de tots els conceptes introduits al paper, complementant-lo i ampliant-lo. L'altra part de la tesis treballa en torn a la possibilitat d'estendre la "twin network" i els seus càlculs per incloure la resta de gregues, implementant les seves prediccions i comparant els resultados amb les fòrmules tancades originals obtingudes pel model de Black-Scholes. En conclusió, la tesis contribueix a la comprensió de la "twin network" i mostra algunes aplicacions de Deep Learning en Finances, concretament en el càlcul d'opcions europeas i les seves respectives gregues. Traducción realizada con la versión gratuita del traductor www.DeepL.com/TranslatorEl paper "Differential Machine Learning", publicado en 2020 por Antoine Savine y Brian Huge abre una nueva puerta al cálculo de derivados financieros a través de un diseño de una nueva Red Neuronal, núcleo del Deep Learning, creando un vínculo entre estos dos campos. Esta tesis pretende dar una comprensión matemática de todos los conceptos introducidos en el paper, complementándolo y ampliándolo. La otra parte de esta tesis trabaja en torno a la posibilidad de extender la "twin network" y sus cálculos para incluir el resto de griegas, implementando sus predicciones y comparando sus resultados con las fórmulas cerradas originales obtenidas por el modelo Black-Scholes. En conclusión, esta tesis contribuye a la comprensión de la "twin network" y muestra algunas aplicaciones de Deep Learning en Finanzas, concretamente en el cálculo de opciones europeas y sus respectivas griegas.The paper ”Differential Machine Learning”, published in 2020 by Antoine Savine and Brian Huge opens a new door to the calculation of financial derivatives through a design of a new Neural Network, the core of Deep Learning, creating a link between these two fields. This thesis aims to give a mathematical understanding of all the concepts introduced in the paper, complementing it and extending it. The other part of this thesis works around the possibility of extending the twin network and its calculations to include the rest of the Greeks, implementing its predictions and comparing its results to the original closed formulas obtained by the Black-Scholes model. In conclusion, this thesis contributes to the understanding of the twin network and showcases some applications of Deep Learning in Finance, specifically in the calculation of European call options and its respective Greeks

LEARNING R PROGRAMMING LANGUAGE FOR ECONOMICS

act Worldwide, scientists conduct research finding answers to questions, and that to do this they have to use, measure, and analyze data. Nowadays, more than never, computer programs are intensive used by researchers from various domains in order to ahieve their goals. There are different software helping scholars to get research results. R is currently one of the top programming languages preferred for accomplishing data science. Descending from the S programming language, R is an independent, open-source, and free software environment, which can be used for statistical analysis, visualization and reporting. In present, the R environment has become one of the most used statistical analysis tools, being used in university and academic research environments, but also in the business environment. More and more companies are using R as a data analysis tool. R is also supported by the academic community. The world's major universities support R. R is being used in the disciplines of finance, banking, insurance, economics, stock market, marketing, computer science, and many other disciplines and fields. Specialists in these fields need R knowledge and skills to analyze data. This article promotes learning of R programming language by students, researchers, and teachers from economic field. Learning R programming language is the acquisition of information, knowledge, and skills of R software. It is an ongoing process that takes place throughout whole professional life without an end date. Learning a programming language like R is the same as learning a spoken language? What is the difference between R and RStudio? How can we gain R skills? These are the questions we propose to answer through this research paper. The goal is helping beginners to enter into the R system. Results consist in providing information and sources to help economist and other people interested in starting learning R programming language

Practical aspects of portfolio selection and optimisation on the capital market

This article highlights some observations concerning the deficiencies in the application of statistics on the capital market, with special reference to Modern Portfolio Theory (MPT). The main point is the sensitivity of statistical parameters (especially the standard deviation of the daily rates of return) to subjective/random factors. For securities with similar patterns and quasi-identical charts, statistical results in contradiction to the evidence of the market can be obtained. This article makes a pledge in favour of the necessity for increased attention in constructing an optimal/efficient portfolio

Sentiment Analysis in Finance

Les dades regeixen el món. Qualsevol persona interessada a dissenyar un producte valuós i tenir impacte ha de tenir això en compte. Els avenços recents en el camp del Processament de Llenguatge Natural han cridat l'atenció d'un gran nombre d'investigadors. Una de les aplicacions més rellevants en aquest camp és l'anàlisi de sentiments a partir d'un text determinat. Aquesta és una tasca desafiant que moltes empreses de diversos sectors inclouen com a part de les seves línees de treball en ciència de dades. Per exemple, en el sector financer, les empreses més innovadores treballen per obtenir indicadors de sentiment mitjançant l'anàlisi de dades textuals, ja sigui per predir els moviments del mercat, guiar estratègies d'inversió o vendre aquesta valuosa informació a tercers. Utilitzant dades de 20 conegudes companyies amb un ampli marge de capitalització del mercat nord-americà, aquest treball pretén ser una prova de concepte de dues metodologies diferents d'anàlisi de sentiments en el sector financer. La primera metodologia calcula les puntuacions de sentiment a partir del text d'un article condicionat als valors de retorn del preu de les accions. La segona metodologia estudia les relacions semàntiques i sintàctiques entre paraules per calcular el sentiment vinculat a un terme d'interès. Tots dos mètodes representen un punt de partida interessant i oposat per al càlcul de sentiment. Finalment, es realitza un estudi numèric dels resultats considerant els valors de correlació i un test de causalitat. Així mateix, aquest treball introdueix un marc per a la simulació d'estratègies d'inversió guiades per les puntuacions de sentiment calculades.Data runs the world. Anyone who is even slightly interested in having an impact and generating a valuable product should take this into account. The recent developments in Natural Language Processing have attracted the attention of a large number of practitioners. One of the most relevant applications in this field is the sentiment analysis of a given text. It is a valuable and challenging task that many companies in various sectors include as part of their data science pipeline. For example, in the field of Finance, the most innovative companies work to obtain sentiment indicators by analyzing textual data, either to predict market movements, guide trading strategies, or sell that valuable information to third parties. Using data from 20 well-known equities with a large capitalization margin from the U.S. market, this work aims to be a proof of concept of two different Sentiment Analysis methodologies in the financial sector. The first methodology computes sentiment scores from article text conditioned to the stocks' price return values. The second methodology studies the semantic and syntactic relationships between words to calculate the sentiment linked to a term of interest. Both methods represent a valuable and opposite starting point for sentiment score computation. Finally, a numerical study of the results is carried out considering correlation values and causality test performance. Also, this work introduces a framework for the simulation of trading strategies guided by the acquired scores

Use of the Monte Carlo Simulation in Valuation of European and American Call Options

This thesis examines the valuation methods used for pricing European and American call options. Options are financial instruments that play an important role in the financial industry and are used in hedging, speculating and arbitraging. Because options are widely used in investing, there is a need for valuation methods that are as precise as possible. Options have been perceived as obscure financial instruments due to the lack of valuation techniques in the past. However, with the discovery of Black-Scholes Model in 1973, the first option valuation method, option trading escalated. In this thesis, the fair market value of S&P 500 index with European exercise style, The Google Option Contract and Apple Option Contract will be obtained by using the Black-Scholes Model, the General Monte Carlo Simulation, The Combined Method and the Least-Squares Monte Carlo. The results from three models will be compared and contrasted in order to determine the best valuation method