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Phone Back Cards Holder Credit Cards Container Invisible Phone Adhesive Bracket

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Description This is a phone card pouch which is made of good silicone materials durable and practical to use. This phone wallet holder is easy to operate which can match various styles to make your phone more distinctive. Share with your friends as it is practical bag. Features -Color:Black -Material:Silicone -Size:9.50X5.60X0.30cm/3.73X2.20X0.12in - The size is moderate to fit cell phones which is convenient and practical. - The cards bag allows you to easily stick the wallet on the back of your mobile phone. - It provides a strong attachment to the surface of your phone back by the strong adhesive. - No worry that the cards or money may lose you can carry them with your cellphone together. - Universal and wide application can be used for many kinds of cellphones or phone cases.

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Credit Repair Secrets (from the Credit Doctor)

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Find Credit Repair Secrets (from the Credit Doctor) by Daniel Rosen in Paperback and other formats in Business & Economics > Finance - General.

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Credit Correlation

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This book provides an advanced guide to correlation modelling for credit portfolios, providing both theoretical underpinnings and practical implementation guidance. The book picks up where pre-crisis credit books left off, offering guidance for quants on the latest tools and techniques for credit portfolio modelling in the presence of CVA (Credit Value Adjustments). Written at an advanced level, it assumes that readers are familiar with the fundamentals of credit modelling covered, for example, in the market leading books by Schonbucher (2003) and O’Kane (2008). Coverage will include the latest default correlation approaches; correlation modelling in the ‘Marshall-Olkin’ contagion framework, in the context of CVA; numerical implementation; and pricing, calibration and risk challenges. The explosive growth of credit derivatives markets in the early-to-mid 000’s was bought to a close by the 2007 financial crisis, where these instruments were held largely to blame for the economic downturn. However, in the wake of increased regulation across all financial instruments and the challenge of buying and selling bonds in large amounts, credit derivatives have once again been found to be the answer and the market has grown significantly.  Written by a practitioner for practitioners, this book will also interest researchers in mathematical finance who want to understand how things happen and work ‘on the floor’. Building the reader’s knowledge from the ground up, and with numerous real life examples used throughout, this book will  prove a popular reference for anyone with a mathematical mind interested credit markets.

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Social Credit Rating

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Social Credit Ratings sind das Ergebnis von Sozialkreditsystemen. Diese umfassen auf verschiedene Datenbanken zugreifende, online betriebene Rating- oder Scoringsysteme, bei denen beispielsweise die Kreditwürdigkeit, das Strafregister und das soziale und gesellschaftliche Verhalten von Personen oder Organisationen wie Unternehmen oder Nichtregierungsorganisationen zur Klassifizierung ihrer Reputation verwendet werden. Das Fahreignungs-Bewertungssystem des Kraftfahrt-Bundesamtes, das für jedermann bestimmte Ordnungswidrigkeiten, Fahrverbote oder Straftaten mit Punkten bewertet und speichert, ist ebenso bekannt und anerkannt wie die SCHUFA-BonitätsAuskunft, der Creditreform-Bonitätsindex oder Noten von Ratingagenturen. Ähnliche Systeme wie Hotelsterne, Verkäuferbewertungen in Online-Shops, Likes, Zertifikate und Zeugniszensuren aller Art sind in Deutschland wie auch in vielen anderen Ländern und weltweit in Social Media verbreitet. Der vom chinesischen Staatsrat beschlossene Aufbau eines staatseigenen Sozialkreditsystems führt solche Ratings und Scorings aufgrund einzigartiger Verknüpfungen in eine neue Dimension, die erst durch die neuen Informations- und Kommunikationstechnologien ermöglicht wurde. Dieses Buch gibt einen tiefen Einblick in die verwendeten Daten, Verfahren, Methoden und Modelle sowie diskutiert Bedeutung, Nutzen, Funktionen, Anwendungsbereiche und auch Risiken und Gefahren von Social Credit Ratings.

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Credit-Risk Modelling

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The risk of counterparty default in banking, insurance, institutional, and pension-fund portfolios is an area of ongoing and increasing importance for finance practitioners. It is, unfortunately, a topic with a high degree of technical complexity. Addressing this challenge, this book provides a comprehensive and attainable mathematical and statistical discussion of a broad range of existing default-risk models. Model description and derivation, however, is only part of the story. Through use of exhaustive practical examples and extensive code illustrations in the Python programming language, this work also explicitly shows the reader how these models are implemented. Bringing these complex approaches to life by combining the technical details with actual real-life Python code reduces the burden of model complexity and enhances accessibility to this decidedly specialized field of study. The entire work is also liberally supplemented with model-diagnostic, calibration, and parameter-estimation techniques to assist the quantitative analyst in day-to-day implementation as well as in mitigating model risk. Written by an active and experienced practitioner, it is an invaluable learning resource and reference text for financial-risk practitioners and an excellent source for advanced undergraduate and graduate students seeking to acquire knowledge of the key elements of this discipline.

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