1,719 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

    Dataflow Programming and Acceleration of Computationally-Intensive Algorithms

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    The volume of unstructured textual information continues to grow due to recent technological advancements. This resulted in an exponential growth of information generated in various formats, including blogs, posts, social networking, and enterprise documents. Numerous Enterprise Architecture (EA) documents are also created daily, such as reports, contracts, agreements, frameworks, architecture requirements, designs, and operational guides. The processing and computation of this massive amount of unstructured information necessitate substantial computing capabilities and the implementation of new techniques. It is critical to manage this unstructured information through a centralized knowledge management platform. Knowledge management is the process of managing information within an organization. This involves creating, collecting, organizing, and storing information in a way that makes it easily accessible and usable. The research involved the development textual knowledge management system, and two use cases were considered for extracting textual knowledge from documents. The first case study focused on the safety-critical documents of a railway enterprise. Safety is of paramount importance in the railway industry. There are several EA documents including manuals, operational procedures, and technical guidelines that contain critical information. Digitalization of these documents is essential for analysing vast amounts of textual knowledge that exist in these documents to improve the safety and security of railway operations. A case study was conducted between the University of Huddersfield and the Railway Safety Standard Board (RSSB) to analyse EA safety documents using Natural language processing (NLP). A graphical user interface was developed that includes various document processing features such as semantic search, document mapping, text summarization, and visualization of key trends. For the second case study, open-source data was utilized, and textual knowledge was extracted. Several features were also developed, including kernel distribution, analysis offkey trends, and sentiment analysis of words (such as unique, positive, and negative) within the documents. Additionally, a heterogeneous framework was designed using CPU/GPU and FPGAs to analyse the computational performance of document mapping

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    AI: Limits and Prospects of Artificial Intelligence

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    The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence

    Eunomia: Enabling User-specified Fine-Grained Search in Symbolically Executing WebAssembly Binaries

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    Although existing techniques have proposed automated approaches to alleviate the path explosion problem of symbolic execution, users still need to optimize symbolic execution by applying various searching strategies carefully. As existing approaches mainly support only coarse-grained global searching strategies, they cannot efficiently traverse through complex code structures. In this paper, we propose Eunomia, a symbolic execution technique that allows users to specify local domain knowledge to enable fine-grained search. In Eunomia, we design an expressive DSL, Aes, that lets users precisely pinpoint local searching strategies to different parts of the target program. To further optimize local searching strategies, we design an interval-based algorithm that automatically isolates the context of variables for different local searching strategies, avoiding conflicts between local searching strategies for the same variable. We implement Eunomia as a symbolic execution platform targeting WebAssembly, which enables us to analyze applications written in various languages (like C and Go) but can be compiled into WebAssembly. To the best of our knowledge, Eunomia is the first symbolic execution engine that supports the full features of the WebAssembly runtime. We evaluate Eunomia with a dedicated microbenchmark suite for symbolic execution and six real-world applications. Our evaluation shows that Eunomia accelerates bug detection in real-world applications by up to three orders of magnitude. According to the results of a comprehensive user study, users can significantly improve the efficiency and effectiveness of symbolic execution by writing a simple and intuitive Aes script. Besides verifying six known real-world bugs, Eunomia also detected two new zero-day bugs in a popular open-source project, Collections-C.Comment: Accepted by ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 202

    A Review of the Role of Causality in Developing Trustworthy AI Systems

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    State-of-the-art AI models largely lack an understanding of the cause-effect relationship that governs human understanding of the real world. Consequently, these models do not generalize to unseen data, often produce unfair results, and are difficult to interpret. This has led to efforts to improve the trustworthiness aspects of AI models. Recently, causal modeling and inference methods have emerged as powerful tools. This review aims to provide the reader with an overview of causal methods that have been developed to improve the trustworthiness of AI models. We hope that our contribution will motivate future research on causality-based solutions for trustworthy AI.Comment: 55 pages, 8 figures. Under revie

    Structure Diagram Recognition in Financial Announcements

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    Accurately extracting structured data from structure diagrams in financial announcements is of great practical importance for building financial knowledge graphs and further improving the efficiency of various financial applications. First, we proposed a new method for recognizing structure diagrams in financial announcements, which can better detect and extract different types of connecting lines, including straight lines, curves, and polylines of different orientations and angles. Second, we developed a two-stage method to efficiently generate the industry's first benchmark of structure diagrams from Chinese financial announcements, where a large number of diagrams were synthesized and annotated using an automated tool to train a preliminary recognition model with fairly good performance, and then a high-quality benchmark can be obtained by automatically annotating the real-world structure diagrams using the preliminary model and then making few manual corrections. Finally, we experimentally verified the significant performance advantage of our structure diagram recognition method over previous methods
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