2,249 research outputs found

    Designing capital-ratio triggers for Contingent Convertibles

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    Contingent Convertible (CoCo) bonds represent a novel category of debt financial instruments, recently introduced into the financial landscape. Their primary role is to bolster financial stability by maintaining healthy capital levels for the issuing entity. This is achieved by converting the bond principal into equity or writing it down once the minimum capital ratios are violated. CoCos aim to recapitalize the bank before it is on the brink of collapse, to avoid a state bailout at a huge cost to the taxpayer. Under normal circumstances, CoCo bonds operate as ordinary coupon-paying bonds, which only in case of insufficient capital ratios are converted into equity of the issuer. However, the CoCo market has struggled to expand over the years, and the recent tumult involving Credit Suisse and its enforced CoCo write-off has underscored these challenges. The focus of this research work is on the first hand to understand the reasons for this failure, and, on the other hand, to modify its underlying design in order to restore its intended purpose: to act as a liquidity buffer, strengthening the capital structure of the issuing firm. The cornerstone of the proposed work is the design of a self-adaptive model for leverage. This model features an automatic conversion that does not hinge on the judgment of regulatory authorities. Notably, it allows the issuer's debt-to-assets ratio to remain within predetermined boundaries, where the likelihood of default on outstanding liabilities remains minimal. The pricing of the proposed instruments is difficult as the conversion is dynamic. We view CoCos essentially as a portfolio of different financial instruments. This treatment makes it easier to analyze their response to different market events that may or may not trigger their conversion to equity. We provide evidence of the model's effectiveness and discuss it implications of its implementation, in light of the regulatory environment and best market practices.Skilyrt breytanleg (e. Contingent Convertible, skammstafað CoCo) skuldabréf eru nýstárleg gerð af fjármálagerningum sem nýlega komu fram á sjónarsvið fjármálamarkaða. Helsta hlutverk þeirra er að e a fjármálastöðugleika með því að viðhalda hæfilegum eiginfjárgrunni fyrir útgefendur þeirra. Þetta er gert með því að umbreyta höfuðstól skuldabréfs í hlutafé eða með því færa þau niður þegar krafa um eiginfjárhlutföll eru rofin. CoCo hefur það markmið að endurfjármagna bankann áður en hann fellur og þar með koma í veg fyrir björgunaraðgerðir af hálfu ríkisins, sem hefur í för með sér mikinn kostnað fyrir skattgreiðendur. Undir venjulegum kringumstæðum virka CoCo skuldabréf eins og hefðbundin arðgreiðslu- skuldabréf, sem einungis er breytt í hlutafé þegar eiginfjárhlutföll útgefanda þeirra eru ekki nægjanleg. Eigi að síður hefur markaður fyrir CoCo átt erfitt uppdráttar í gegnum tíðina og hefur nýlegur titringur í kringum Credit Suisse og þvingaðar afskriftir þeirra á CoCo skuldabréfum ýtt enn frekar undir erfiðleikana. Helsti tilgangur þessarar rannsóknar er tvíþættur. Annars vegar er ætlunin að skilja hvers vegna CoCo hefur ekki átt meiri velgengni að fagna en raun ber vitni. Hins vegar er henni ætlað að breyta grundvallarhönnun CoCo í þeim tilgangi að endurheimta upprunalegan tilgang þeirra: sem er að vera stuðpúði lausafés sem styrkir fjármagnsskipan útgáfu fyrirtækisins. Hornsteinn verkefnisins er hönnun á líkani með sjálfaðlögunarhæfni með tilliti til skuldsetningarhlutfalls. Líkanið býr yfir sjálfvirkri umbreytingu sem ræðst því ekki af reglum eftirlitsyfirvalda. Það gerir útgefanda því kleift að viðhalda hlutfalli skulda á móti eignum innan fyrirfram skilgreindra marka, þar sem líkur á vanskilum vegna útistandandi skuldbindinga haldast í lágmarki. Verðlagning gerninganna sem lagðir eru til í rannsókninni er þó vandasöm þar sem umbreytingin er dýnamísk. Í meginatriðum verður litið á CoCos sem safn ólíkra fjármálagerninga. Með þessari aðferð er hægt að greina viðbrögð þeirra við mismunandi markaðsatburðum sem geta mögulega hrint af stað umbreytingu yfir í hlutafé. Sýnt verður fram á skilvirkni líkansins ásamt því að álykta um innleiðingu þess með tilliti til regluverks og bestu markaðsvenja.RU Research Fund Icelandic Research Fun

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A decision-making approach for the health-aware energy management of ship hybrid power plants

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    Although autonomous shipping has attracted increasing interest, its further develop-ment requires innovative solutions to operate autonomous ships without the direct in-tervention of human operators. This study aims to develop a health-aware energy management (HAEM) approach for ship hybrid power plants, integrating the health monitoring information from reliability tools with the energy management tools. This approach employs the equivalent consumption minimisation strategy (ECMS) along with a Dynamic Bayesian network (DBN), as well as the utopia decision-making meth-od and a model for the ship hybrid power plant. The HAEM approach is demonstrated for a parallel hybrid power plant of a pilot boat considering realistic operating profiles. The results demonstrate that by employing HAEM approach for the investigated ship power plant operating for 300 hours reduces its failure rate almost fourfold at the cost of fuel consumption increase of around 1.5 %, compared to the respective operation with the ECMS. This study is expected to contribute towards the development of su-pervisory control of autonomous power plants

    Composite Disturbance Filtering: A Novel State Estimation Scheme for Systems With Multi-Source, Heterogeneous, and Isomeric Disturbances

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    State estimation has long been a fundamental problem in signal processing and control areas. The main challenge is to design filters with ability to reject or attenuate various disturbances. With the arrival of big data era, the disturbances of complicated systems are physically multi-source, mathematically heterogenous, affecting the system dynamics via isomeric (additive, multiplicative and recessive) channels, and deeply coupled with each other. In traditional filtering schemes, the multi-source heterogenous disturbances are usually simplified as a lumped one so that the "single" disturbance can be either rejected or attenuated. Since the pioneering work in 2012, a novel state estimation methodology called {\it composite disturbance filtering} (CDF) has been proposed, which deals with the multi-source, heterogenous, and isomeric disturbances based on their specific characteristics. With the CDF, enhanced anti-disturbance capability can be achieved via refined quantification, effective separation, and simultaneous rejection and attenuation of the disturbances. In this paper, an overview of the CDF scheme is provided, which includes the basic principle, general design procedure, application scenarios (e.g. alignment, localization and navigation), and future research directions. In summary, it is expected that the CDF offers an effective tool for state estimation, especially in the presence of multi-source heterogeneous disturbances

    MINING SARS-COV-2 PHYLOGENETIC TREES TO ESTIMATE CIRCULATING INFECTIONS AND PATTERNS OF MIGRATION

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    The SARS-CoV-2 pandemic led to the formation of very large databases of genomic viral data. These databases contain information on transmission dynamics, emergence and evolution of SARS-CoV-2. However, extracting this information from sequences is difficult, as most methods of analyzing viral genomes were developed for smaller data sets. Therefore, my objective was to develop new fast estimators of the number of infections (I) and the rate of migration based on simple features of SARS-CoV-2 phylogenies. I simulated pathogen evolution using a susceptible-exposed-infectious-recovered (SEIR) model of pathogen spread, reconstructing evolution using CoVizu. For simulations of I, I varied the total number of infections when a final sample was obtained. For simulations of migration rates, I simulated independent groups of infections and varied the rates of movement between these groups. I then extracted summary statistics from the simulation output and developed general linear models (GLMs) and Markov models to predict I and migration rates respectfully. I evaluated the models using validation data and veritable SARS-CoV-2 data. The GLMs formulated to predict I showed significant promise, especially when predicting when there were less than 1 million infections. The Markov models developed to predict migration rates were less successful. However, the simulation pipeline formulated to test the Markov models may be used for further development of efficient methods to estimate migration rates. This research will help inform public health officials on SARS-CoV-2 spread between countries and emerging variants that may become variants of concern. Additionally, the algorithms are flexible and, with new training, may be applied to future outbreaks of novel viral pathogens

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería, Ciencia de Datos y BioinformáticaClave Programa: DBICódigo Línea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e Informátic

    Characterising Memory in Infinite Games

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    Collective variables between large-scale states in turbulent convection

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    The dynamics in a confined turbulent convection flow is dominated by multiple long-lived macroscopic circulation states, which are visited subsequently by the system in a Markov-type hopping process. In the present work, we analyze the short transition paths between these subsequent macroscopic system states by a data-driven learning algorithm that extracts the low-dimensional transition manifold and the related new coordinates, which we term collective variables, in the state space of the complex turbulent flow. We therefore transfer and extend concepts for conformation transitions in stochastic microscopic systems, such as in the dynamics of macromolecules, to a deterministic macroscopic flow. Our analysis is based on long-term direct numerical simulation trajectories of turbulent convection in a closed cubic cell at a Prandtl number Pr=0.7Pr = 0.7 and Rayleigh numbers Ra=106Ra = 10^6 and 10710^7 for a time lag of 10510^5 convective free-fall time units. The simulations resolve vortices and plumes of all physically relevant scales resulting in a state space spanned by more than 3.5 million degrees of freedom. The transition dynamics between the large-scale circulation states can be captured by the transition manifold analysis with only two collective variables which implies a reduction of the data dimension by a factor of more than a million. Our method demonstrates that cessations and subsequent reversals of the large-scale flow are unlikely in the present setup and thus paves the way to the development of efficient reduced-order models of the macroscopic complex nonlinear dynamical system.Comment: 24 pages, 12 Figures, 1 tabl

    Stochastic models of cell population dynamics and tick-borne virus transmission

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    When modelling cellular population dynamics, many mathematical models consider exponential inter-event times. Despite being the most convenient choice from a mathematical and computational perspective, the exponential distribution overestimates the probability of short division times. In Chapter 3, I consider a multi-stage model of the cell cycle to maintain the advantages of a Markovian model, while improving on exponential times to division. With this structure in place, cell generations are introduced in the model to link theoretical predictions with experimental data. The model with cell generations is parameterised making use of CFSE data and Bayesian methods. Then, in order to study fate correlation of cellular siblings, in Chapter 4, I pro- pose a mathematical model that makes use of the theory of branching processes. Cells are categorised based on their fate, either division or death, which is decided at birth. The applicability of this approach is shown by considering a data set of stimulated B cells produced with time-lapse microscopy. The last chapter of this thesis aims to shed light on the role of co-feeding and co-transmission in the spread of a vector-borne virus. Thus, a population of ticks interacts with a population of hosts (small or large vertebrates). First, I consider a single infection whose dynamics is modelled through both deterministic and stochastic models. The basic reproduction number is computed by means of the next generation matrix approach. When modelling co-infection with two different viruses (or two strains of the same virus), a deterministic model is proposed to study only co-feeding transmission, accounting also for co-transmission of the virus. A series of stochastic descriptors of interest are computed when considering all the routes of transmission

    Games with Trading of Control

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