Risk quantification of an option portfolio through the introduction of the fuzzy Black-Scholes formula

Treballs Finals del Màster de Ciències Actuarials i Financeres, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2018-2019, Tutor: Ana María Gil LafuenteThe aim of this thesis is to quantify the market risk of an option portfolio under uncertainty. The fuzzy sets theory is introduced to model the parameters of the Black-Scholes option-pricing formula. Since the option price is calculated through the fuzzy Black-Scholes formula, we can compute the Value-at-Risk as a fuzzy number. By doing so, we aim to capture extra information that is lost in traditional models given the uncertainty derived from the fluctuations of financial markets. Finally, we want to conclude whether the introduction of the fuzzy sets theory is useful in order to improve the risk management

Laberints, espais, soledat

Des de l'antiguitat als nostres dies hi ha certes arquitectures que comparteixen una condició d'intemporalitat que desperten una aura d'espiritualitat. És aquesta característica la que em captiva i em porta a reflexionar sobre com alguns edificis posseeixen una essència atemporal que ens guia cap al silenci, la solitud i el deambular per l'espai. Aquests edificis són com a misteriosos laberints que ens conviden a perdre'ns en els seus racons i descobrir els seus secrets. M'interessa aprofundir en com aquesta condició laberíntica es relaciona amb l'experiència de l'usuari i com es manifesta en l'arquitectura contemporània. Aquest treball és una invitació a endinsar-se en el món de l'intemporal i l'espiritual en l'arquitectura, des d'una mirada analítica però també des de la reflexió personal.Desde la antigüedad a nuestros días hay ciertas arquitecturas que comparten una condición de intemporalidad que despiertan una aura de espiritualidad. Es esta característica la que me cautiva y me lleva a reflexionar sobre como algunos edificios poseen una esencia atemporal que nos guía hacia el silencio, la soledad y el deambular por el espacio. Estos edificios son como misteriosos laberintos que nos invitan a perdernos en sus rincones y descubrir sus secretos. Me interesa profundizar en como esta condición laberíntica se relaciona con la experiencia del usuario y como se manifiesta en la arquitectura contemporánea. Este trabajo es una invitación a adentrarse en el mundo de lo intemporal y lo espiritual en la arquitectura, desde una mirada analítica pero también desde la reflexión personal.From ancient times to the present day there are certain architectures that share a condition of timelessness that awaken an aura of spirituality. It is this characteristic that captivates me and leads me to reflect on how some buildings possess an intemporary essence that guides us towards silence, solitude and the wandering through space. These buildings are like mysterious mazes inviting us to lose ourselves in their corners and discover their secrets. I am interested in how this labyrinthine condition relates to the user's experience and how it manifests itself in contemporary architecture. This work is an invitation to enter the world of the timeless and spiritual in architecture, from an analytical look but also from personal reflection

Crisi econòmica, dèficit fiscal i sobiranisme a Catalunya

Treballs Finals del Grau d'Economia, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2015-2016 , Tutora: Maite Montagut Antolí[cat] En aquest treball, s’ha analitzat a través d’un anàlisi macroeconòmic l’economia catalana, espanyola i europea, per tal d’observar l’impacte que la crisi iniciada l’any 2008 ha tingut sobre aquestes, i poder constatar mitjançant una comparació, el grau en que la crisi ha afectat Catalunya. Un cop vistos els efectes de la crisi, s’ha procedit a l’estudi del dèficit fiscal català. S’ha analitzat les diferents formes de càlcul de les Balances Fiscals, a través dels mètodes de flux monetari i flux càrrega benefici, neutralitzant o no el dèficit públic estatal. Posteriorment, s’ha aprofundit en la controvèrsia existent sobre el dèficit fiscal a Catalunya. Finalment, amb l’objectiu d’establir relacions entre l’evolució de la crisi, el dèficit fiscal i l’augment del sobiranisme a Catalunya, s’ha contrastat un conjunt de dades significatives d’enquestes d’opinió realitzades a la població catalana.[eng] In this paper, the Catalan economy has been analyzed with macroeconomic analysis to observe the impact the crisis that began in 2008 has had on it. It has also been studied the results in Catalonia in opposition to those in Spain and Europe (Euro Zone and European Union), to see the extent or degree to which the crisis has affected Catalonia. Once having extracted results of this anlalysis and viewed the crisis effects, it has been proceeded to study the fiscal deficit between Catalonia and Spain. Concerning this fiscal deficit the different ways to calculate the Fiscal Balances, have been analyzed through cash flow and fiscal incidence methods, by neutralizing or not the State deficit. In this part, a huge differentiation has been made between the ‘’Separatists’’ (supporters of the independence for Catalonia and the separation from Spain) and the ‘’Unionists’’ (supporters to maintain the current relations between Catalonia and Spain), to facilitate the subsequent controversy existing in the phenomenon of fiscal deficit. Finally, a discussion on different aspects has been exposed. The most significant of these points are those which have allowed to establish a relationship between the crisis and the rise of sovereignty and, moreover, between the existing structural fiscal deficit and the increasing of independence

Machine learning approach to the study of chromatin

Des de l’aparició de les tecnologies de seqüenciació d’alt rendiment, els conjunts de dades biològiques han esdevingut cada cop més grans i complexes, la qual cosa els fa pràcticament impossibles d’interpretar manualment. El paradigma de l’aprenentatge automàtic permet fer una anàlisi sistemàtica de les relacions i patrons existents en els conjuts de dades, tot aprofitant l’enorme volum de dades disponibles. No obstant això, una aplicació poc curosa dels principis bàsics de l’aprenentatge automàtic pot conduir a estimacions massa optimistes, un problema prevalent conegut com a sobreajust. En el camp del plegament de proteïnes, en vam trobar exemples en models publicats que afirmaven tenir un alt poder predictiu, però que es comportaven de forma mediocre devant de dades noves. En el camp de l’epigenètica, problemes com la falta de reproducibilitat, qualitat heterogènia i conflictes entre replicats esdevenen evidents quan es comparen diferents conjunts de dades de ChIP-seq. Per superar aquestes limitacions vam desenvolupar Zerone, un discretitzador de ChIP-seq basat en aprenentatge automàtic que és capaç de combinar informació de diferents replicats experimentals i d’identificar automàticament dades de baixa qualitat o irreproduïbles.Since the appearance of high throughput sequencing technologies, biological data sets have become increasingly large and complex, which renders them practically impossible to interpret directly by a human. The machine learning paradigm allows a systematic analysis of relationships and patterns within data sets, making possible to extract information by leveraging the sheer amount of data available. However, violations of basic machine learning principles may lead to overly optimistic estimates, a prevalent problem known as overfitting. In the field of protein folding, we found examples of this in published models that claimed high predictive power, but that performed poorly on new data. A different problem arises in epigenetics. Issues such as lack of reproducibility, heterogeneous quality and conflicts between replicates become evident when comparing ChIP-seq data sets. To overcome this limitations we developed Zerone, a machine learning-based ChIP-seq discretizer capable of merging information from several experimental replicates and automatically identifying low quality or irreproducible data

Machine learning approach to the study of chromatin

Des de l’aparició de les tecnologies de seqüenciació d’alt rendiment, els conjunts de dades biològiques han esdevingut cada cop més grans i complexes, la qual cosa els fa pràcticament impossibles d’interpretar manualment. El paradigma de l’aprenentatge automàtic permet fer una anàlisi sistemàtica de les relacions i patrons existents en els conjuts de dades, tot aprofitant l’enorme volum de dades disponibles. No obstant això, una aplicació poc curosa dels principis bàsics de l’aprenentatge automàtic pot conduir a estimacions massa optimistes, un problema prevalent conegut com a sobreajust. En el camp del plegament de proteïnes, en vam trobar exemples en models publicats que afirmaven tenir un alt poder predictiu, però que es comportaven de forma mediocre devant de dades noves. En el camp de l’epigenètica, problemes com la falta de reproducibilitat, qualitat heterogènia i conflictes entre replicats esdevenen evidents quan es comparen diferents conjunts de dades de ChIP-seq. Per superar aquestes limitacions vam desenvolupar Zerone, un discretitzador de ChIP-seq basat en aprenentatge automàtic que és capaç de combinar informació de diferents replicats experimentals i d’identificar automàticament dades de baixa qualitat o irreproduïbles.Since the appearance of high throughput sequencing technologies, biological data sets have become increasingly large and complex, which renders them practically impossible to interpret directly by a human. The machine learning paradigm allows a systematic analysis of relationships and patterns within data sets, making possible to extract information by leveraging the sheer amount of data available. However, violations of basic machine learning principles may lead to overly optimistic estimates, a prevalent problem known as overfitting. In the field of protein folding, we found examples of this in published models that claimed high predictive power, but that performed poorly on new data. A different problem arises in epigenetics. Issues such as lack of reproducibility, heterogeneous quality and conflicts between replicates become evident when comparing ChIP-seq data sets. To overcome this limitations we developed Zerone, a machine learning-based ChIP-seq discretizer capable of merging information from several experimental replicates and automatically identifying low quality or irreproducible data

Zerone: a ChIP-seq discretizer for multiple replicates with built-in quality control

Motivation: Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard method to investigate chromatin protein composition. As the number of community-available ChIP-seq profiles increases, it becomes more common to use data from different sources, which makes joint analysis challenging. Issues such as lack of reproducibility, heterogeneous quality and conflicts between replicates become evident when comparing datasets, especially when they are produced by different laboratories. Results : Here, we present Zerone, a ChIP-seq discretizer with built-in quality control. Zerone is powered by a Hidden Markov Model with zero-inflated negative multinomial emissions, which allows it to merge several replicates into a single discretized profile. To identify low quality or irreproducible data, we trained a Support Vector Machine and integrated it as part of the discretization process. The result is a classifier reaching 95% accuracy in detecting low quality profiles. We also introduce a graphical representation to compare discretization quality and we show that Zerone achieves outstanding accuracy. Finally, on current hardware, Zerone discretizes a ChIP-seq experiment on mammalian genomes in about 5 min using less than 700 MB of memory. Availability and Implementation : Zerone is available as a command line tool and as an R package. The C source code and R scripts can be downloaded from https://github.com/nanakiksc/zerone . The information to reproduce the benchmark and the figures is stored in a public Docker image that can be downloaded from https://hub.docker.com/r/nanakiksc/zerone/ . Contact : [email protected] Supplementary information : Supplementary data are available at Bioinformatics online.This research was supported by the Government of Catalonia and the Spanish Ministery of Economy and Competitiveness (Plan Nacional BFU2012-37168, Centro de Excelencia Severo Ochoa 20132017 SEV-20120208). The fellowship of P.C. was partly supported by the Spanish Ministry of Economy and Competitiveness [State Training Subprogram: predoctoral fellowships for the training of PhD students (FPI) 2013]

Starcode: sequence clustering based on all-pairs search

Motivation: The increasing throughput of sequencing technologies offers new applications and challenges for computational biology. In many of those applications, sequencing errors need to be/ncorrected. This is particularly important when sequencing reads from an unknown reference such as random DNA barcodes. In this case, error correction can be done by performing a pairwise comparison/nof all the barcodes, which is a computationally complex problem. Results: Here, we address this challenge and describe an exact algorithm to determine which pairs of sequences lie within a given Levenshtein distance. For error correction or redundancy reduction purposes, matched pairs are then merged into clusters of similar sequences. The efficiency of starcode is attributable to the poucet search, a novel implementation of the Needleman–Wunsch algorithm performed on the nodes of a trie. On the task of matching random barcodes, starcode outperforms sequence clustering algorithms in both speed and precision. Availability and implementation: The C source code is available at http://github.com/gui11aume/starcode

Starcode: sequence clustering based on all-pairs search

Motivation: The increasing throughput of sequencing technologies offers new applications and challenges for computational biology. In many of those applications, sequencing errors need to be/ncorrected. This is particularly important when sequencing reads from an unknown reference such as random DNA barcodes. In this case, error correction can be done by performing a pairwise comparison/nof all the barcodes, which is a computationally complex problem. Results: Here, we address this challenge and describe an exact algorithm to determine which pairs of sequences lie within a given Levenshtein distance. For error correction or redundancy reduction purposes, matched pairs are then merged into clusters of similar sequences. The efficiency of starcode is attributable to the poucet search, a novel implementation of the Needleman–Wunsch algorithm performed on the nodes of a trie. On the task of matching random barcodes, starcode outperforms sequence clustering algorithms in both speed and precision. Availability and implementation: The C source code is available at http://github.com/gui11aume/starcode

Hierarchical chromatin organization detected by TADpole

The rapid development of Chromosome Conformation Capture (3C-based techniques), as well as imaging together with bioinformatics analyses, has been fundamental for unveiling that chromosomes are organized into the so-called topologically associating domains or TADs. While TADs appear as nested patterns in the 3C-based interaction matrices, the vast majority of available TAD callers are based on the hypothesis that TADs are individual and unrelated chromatin structures. Here we introduce TADpole, a computational tool designed to identify and analyze the entire hierarchy of TADs in intra-chromosomal interaction matrices. TADpole combines principal component analysis and constrained hierarchical clustering to provide a set of significant hierarchical chromatin levels in a genomic region of interest. TADpole is robust to data resolution, normalization strategy and sequencing depth. Domain borders defined by TADpole are enriched in main architectural proteins (CTCF and cohesin complex subunits) and in the histone mark H3K4me3, while their domain bodies, depending on their activation-state, are enriched in either H3K36me3 or H3K27me3, highlighting that TADpole is able to distinguish functional TAD units. Additionally, we demonstrate that TADpole's hierarchical annotation, together with the new DiffT score, allows for detecting significant topological differences on Capture Hi-C maps between wild-type and genetically engineered mouse.European Research Council under the Seventh Framework Program FP7/2007-2013 [609989, in part]; European Union's Horizon 2020 Research and Innovation Programme [676556]; Spanish Ministry of Science and Innovation [BFU2013-47736-P, BFU2017-85926-P to M.A.M-R., IJCI-2015-23352 to I.F., BES-2014-070327 to P.S-V.]; ‘Centro de Excelencia Severo Ochoa 2013–2017’, SEV-2012-0208; CERCA Programme/Generalitat de Catalunya (to C.R.G.). Funding for open access charge: European Research Council under the Seventh Framework Program FP7/2007-2013 [609989]

Machine Learning: How Much Does It Tell about Protein Folding Rates?

The prediction of protein folding rates is a necessary step towards understanding the principles of protein folding. Due to the increasing amount of experimental data, numerous protein folding models and predictors of protein folding rates have been developed in the last decade. The problem has also attracted the attention of scientists from computational fields, which led to the publication of several machine learning-based models to predict the rate of protein folding. Some of them claim to predict the logarithm of protein folding rate with an accuracy greater than 90%. However, there are reasons to believe that such claims are exaggerated due to large fluctuations and overfitting of the estimates. When we confronted three selected published models with new data, we found a much lower predictive power than reported in the original publications. Overly optimistic predictive powers appear from violations of the basic principles of machine-learning. We highlight common misconceptions in the studies claiming excessive predictive power and propose to use learning curves as a safeguard against those mistakes. As an example, we show that the current amount of experimental data is insufficient to build a linear predictor of logarithms of folding rates based on protein amino acid composition